Assignment 2

Managing a Process to Meet Demand

Ideally, the capacity of a process will be such that its output just matches demand. Excess capacity is wasteful and costly; too little capacity means dissatisfied customers and lost page 14revenue. Having the right capacity requires having accurate forecasts of demand, the ability to translate forecasts into capacity requirements, and a process in place capable of meeting expected demand. Even so, process variation and demand variability can make the achievement of a match between process output and demand difficult. Therefore, to be effective, it is also necessary for managers to be able to deal with variation.

Process Variation

Variation occurs in all business processes. It can be due to variety or variability. For example, random variability is inherent in every process; it is always present. In addition, variation can occur as the result of deliberate management choices to offer customers variety.

There are four basic sources of variation:

1. The variety of goods or services being offered. The greater the variety of goods and services, the greater the variation in production or service requirements.

2. Structural variation in demand. These variations, which include trends and seasonal variations, are generally predictable. They are particularly important for capacity planning.

3. Random variation. This natural variability is present to some extent in all processes, as well as in demand for services and products, and it cannot generally be influenced by managers.

4. Assignable variation. These variations are caused by defective inputs, incorrect work methods, out-of-adjustment equipment, and so on. This type of variation can be reduced or eliminated by analysis and corrective action.

Variations can be disruptive to operations and supply chain processes, interfering with optimal functioning. Variations result in additional cost, delays and shortages, poor quality, and inefficient work systems. Poor quality and product shortages or service delays can lead to dissatisfied customers and can damage an organization’s reputation and image. It is not surprising, then, that the ability to deal with variability is absolutely necessary for managers.

Throughout this book, you will learn about some of the tools managers use to deal with variation. An important aspect of being able to deal with variation is to use metrics to describe it. Two widely used metrics are the mean (average) and the standard deviation. The standard deviation quantifies variation around the mean. The mean and standard deviation are used throughout this book in conjunction with variation. So, too, is the normal distribution. Because you will come across many examples of how the normal distribution is used, you may find the overview on working with the normal distribution in the appendix at the end of the book helpful.

Managing the Supply Chain to Achieve Schedule, Cost, and Quality Goals

Consider a bicycle factory. This might be primarily an assembly operation: buying components such as frames, tires, wheels, gears, and other items from suppliers, and then assembling bicycles. The factory also might do some of the fabrication work itself, forming frames and making the gears and chains, and it might buy mainly raw materials and a few parts and materials such as paint, nuts and bolts, and tires. Among the key management tasks in either case are scheduling production, deciding which components to make and which to buy, ordering parts and materials, deciding on the style of bicycle to produce and how many, purchasing new equipment to replace old or worn-out equipment, maintaining equipment, motivating workers, and ensuring that quality standards are met.

Obviously, an airline company and a bicycle factory are completely different types of operations. One is primarily a service operation, the other a producer of goods. Nonetheless, these two operations have much in common. Both involve scheduling activities, motivating employees, ordering and managing supplies, selecting and maintaining equipment, satisfying quality standards, and—above all—satisfying customers. Also, in both businesses, the success of the business depends on short- and long-term planning.

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A primary function of an operations manager is to guide the system by decision making. Certain decisions affect the design of the system, and others affect the operation of the system.

System design involves decisions that relate to system capacity, the geographic location of facilities, the arrangement of departments and the placement of equipment within physical structures, product and service planning, and the acquisition of equipment. These decisions usually, but not always, require long-term commitments. Moreover, they are typically strategic decisions. System operation involves management of personnel, inventory planning and control, scheduling, project management, and quality assurance. These are generally tactical and operational decisions. Feedback on these decisions involves measurement and control. In many instances, the operations manager is more involved in day-to-day operating decisions than with decisions relating to system design. However, the operations manager has a vital stake in system design because system design essentially determines many of the parameters of system operation. For example, costs, space, capacities, and quality are directly affected by design decisions. Even though the operations manager is not responsible for making all design decisions, he or she can provide those decision makers with a wide range of information that will have a bearing on their decisions.

A number of other areas are part of, or support, the operations function. They include purchasing, industrial engineering, distribution, and maintenance.

Purchasing is responsible for the procurement of materials, supplies, and equipment. Close contact with operations is necessary to ensure correct quantities and timing of purchases. The purchasing department is often called on to evaluate vendors for quality, reliability, service, price, and ability to adjust to changing demand. Purchasing is also involved in receiving and inspecting the purchased goods.

Industrial engineering is often concerned with scheduling, performance standards, work methods, quality control, and material handling.

Distribution involves the shipping of goods to warehouses, retail outlets, or final customers.

Maintenance is responsible for general upkeep and the repair of equipment, the buildings and grounds, heating and air-conditioning, parking, removing toxic wastes, and perhaps security.

The operations manager is the key figure in the system. He or she has the ultimate responsibility for the creation of goods or provision of services.

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The kinds of jobs that operations managers oversee vary tremendously from organization to organization, largely because of the different products or services involved. Thus, managing a banking operation obviously requires a different kind of expertise than managing a steelmaking operation. However, in a very important respect, the jobs are the same: They are both essentially managerial. The same thing can be said for the job of any operations manager regardless of the kinds of goods or services being created.

The service sector and the manufacturing sector are both important to the economy. The service sector now accounts for more than 70 percent of jobs in the United States, and it is growing in other countries as well. Moreover, the number of people working in services is increasing, while the number of people working in manufacturing is not. The reason for the decline in manufacturing jobs is twofold: As the operations function in manufacturing companies finds more productive ways of producing goods, the companies are able to maintain or even increase their output using fewer workers. Furthermore, some manufacturing work has been outsourced to more productive companies, many in other countries, that are able to produce goods at lower costs. Outsourcing and productivity will be discussed in more detail in this and other chapters.

Many of the concepts presented in this book apply equally to manufacturing and service. Consequently, whether your interest at this time is on manufacturing or on service, these concepts will be important, regardless of whether a manufacturing example or service example is used to illustrate the concept.

The Why Manufacturing Matters reading gives another reason for the importance of manufacturing jobs.

Models

A model is an abstraction of reality, a simplified representation of something. For example, a toy car is a model of a real automobile. It has many of the same visual features (shape, relative proportions, wheels) that make it suitable for the child’s learning and playing. But the toy does not have a real engine, it cannot transport people, and it does not weigh 3,000 pounds.

Other examples of models include automobile test tracks and crash tests; formulas, graphs, and charts; balance sheets and income statements; and financial ratios. Common statistical models include descriptive statistics such as the mean, median, mode, range, and standard deviation, as well as random sampling, the normal distribution, and regression equations.

Models are sometimes classified as physical, schematic, or mathematical.

Physical models look like their real-life counterparts. Examples include miniature cars, trucks, airplanes, toy animals and trains, and scale-model buildings. The advantage of these models is their visual correspondence with reality. 3-D printers (explained in Chapter 6) are often used to prepare scale models.

Schematic models are more abstract than their physical counterparts; that is, they have less resemblance to the physical reality. Examples include graphs and charts, blueprints, pictures, and drawings. The advantage of schematic models is that they are often relatively simple to construct and change. Moreover, they have some degree of visual correspondence.

Mathematical models are the most abstract: They do not look at all like their real-life counterparts. Examples include numbers, formulas, and symbols. These models are usually the easiest to manipulate, and they are important forms of inputs for computers and calculators.

The variety of models in use is enormous. Nonetheless, all have certain common features: They are all decision-making aids and simplifications of more complex real-life phenomena. Real life involves an overwhelming amount of detail, much of which is irrelevant for any particular problem. Models omit unimportant details so that attention can be concentrated on the most important aspects of a situation.

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Because models play a significant role in operations management decision making, they are heavily integrated into the material of this text. For each model, try to learn (1) its purpose, (2) how it is used to generate results, (3) how these results are interpreted and used, and (4) what assumptions and limitations apply.

The last point is particularly important because virtually every model has an associated set of assumptions or conditions under which the model is valid. Failure to satisfy all of the assumptions will make the results suspect. Attempts to apply the results to a problem under such circumstances can lead to disastrous consequences.

Managers use models in a variety of ways and for a variety of reasons. Models are beneficial because they:

1. Are generally easy to use and less expensive than dealing directly with the actual situation.

2. Require users to organize and sometimes quantify information and, in the process, often indicate areas where additional information is needed.

3. Increase understanding of the problem.

4. Enable managers to analyze what-if questions.

5. Serve as a consistent tool for evaluation and provide a standardized format for analyzing a problem.

6. Enable users to bring the power of mathematics to bear on a problem.

This impressive list of benefits notwithstanding, models have certain limitations of which you should be aware. The following are three of the more important limitations.

1. Quantitative information may be emphasized at the expense of qualitative information.

2. Models may be incorrectly applied and the results misinterpreted. The widespread use of computerized models adds to this risk because highly sophisticated models may be placed in the hands of users who are not sufficiently knowledgeable to appreciate the subtleties of a particular model; thus, they are unable to fully comprehend the circumstances under which the model can be successfully employed.

3. The use of models does not guarantee good decisions.

Quantitative Approaches

Quantitative approaches to problem solving often embody an attempt to obtain mathematically optimal solutions to managerial problems. Q uantitative approaches to decision making in operations management (and in other functional business areas) have been accepted because of calculators and computers capable of handling the required calculations. Computers have had a major impact on operations management. Moreover, the growing availability of software packages for quantitative techniques has greatly increased management’s use of those techniques.

Although quantitative approaches are widely used in operations management decision making, it is important to note that managers typically use a combination of qualitative and quantitative approaches, and many important decisions are based on qualitative approaches.

Performance Metrics

Managers use metrics to manage and control operations. There are many metrics in use, including those related to profits, costs, quality, productivity, flexibility, assets, inventories, schedules, and forecast accuracy. As you read each chapter, note the metrics being used and how they are applied to manage operations.

Analysis of Trade-Offs

Operations personnel frequently encounter decisions that can be described as trade-off decisions. For example, in deciding on the amount of inventory to stock, the decision maker must take into account the trade-off between the increased level of customer service that the additional inventory would yield and the increased costs required to stock that inventory.

Decision makers sometimes deal with these decisions by listing the advantages and disadvantages—the pros and cons—of a course of action to better understand the consequences page 20of the decisions they must make. In some instances, decision makers add weights to the items on their list that reflect the relative importance of various factors. This can help them “net out” the potential impacts of the trade-offs on their decision.

Degree of Customization

A major influence on the entire organization is the degree of customization of products or services being offered to its customers. Providing highly customized products or services such as home remodeling, plastic surgery, and legal counseling tends to be more labor intensive than providing standardized products such as those you would buy “off the shelf” at a mall store or a supermarket or standardized services such as public utilities and internet services. Furthermore, production of customized products or provision of customized services is generally more time consuming, requires more highly skilled people, and involves more flexible equipment than what is needed for standardized products or services. Customized processes tend to have a much lower volume of output than standardized processes, and customized output carries a higher price tag. The degree of customization has important implications for process selection and job requirements. The impact goes beyond operations and supply chains. It affects marketing, sales, accounting, finance, and information systems.

A Systems Perspective

A systems perspective is almost always beneficial in decision making. Think of it as a “big picture” view. A system can be defined as a set of interrelated parts that must work together. In a business organization, the organization can be thought of as a system composed of subsystems (e.g., marketing subsystem, operations subsystem, finance subsystem), which in turn are composed of lower subsystems. The systems approach emphasizes interrelationships among subsystems, but its main theme is that the whole is greater than the sum of its individual parts. Hence, from a systems viewpoint, the output and objectives of the organization as a whole take precedence over those of any one subsystem.

A systems perspective is essential whenever something is being designed, redesigned, implemented, improved, or otherwise changed. It is important to take into account the impact on all parts of the system. For example, if the upcoming model of an automobile will add forward collision braking, a designer must take into account how customers will view the change, the cost of producing the new system, installation procedures, and repair procedures. In addition, workers will need training to make and/or assemble the new system, production scheduling may change, inventory procedures may have to change, quality standards will have to be established, advertising must be informed of the new features, and parts suppliers must be selected.

Establishing Priorities

In virtually every situation, managers discover that certain issues or items are more important than others. Recognizing this enables the managers to direct their efforts to where they will do the most good.

Typically, a relatively few issues or items are very important, so that dealing with those factors will generally have a disproportionately large impact on the results achieved. This well-known effect is referred to as the Pareto phenomenon . This is one of the most important and pervasive concepts in operations management. In fact, this concept can be applied at all levels of management and to every aspect of decision making, both professional and personal.

The Industrial Revolution

The Industrial Revolution began in the 1770s in England and spread to the rest of Europe and to the United States during the 19th century. Prior to that time, goods were produced in small shops by craftsmen and their apprentices. Under that system, it was common for one person to be responsible for making a product, such as a horse-drawn wagon or a piece of furniture, from start to finish. Only simple tools were available; the machines in use today had not been invented.

Then, a number of innovations in the 18th century changed the face of production forever by substituting machine power for human power. Perhaps the most significant of these was the steam engine, because it provided a source of power to operate machines in factories. Ample supplies of coal and iron ore provided materials for generating power and making machinery. The new machines, made of iron, were much stronger and more durable than the simple wooden machines they replaced.

In the earliest days of manufacturing, goods were produced using craft production : Highly skilled workers using simple, flexible tools produced goods according to customer specifications.

Craft production had major shortcomings. Because products were made by skilled craftsmen who custom-fitted parts, production was slow and costly. And when parts failed, the replacements also had to be custom made, which was also slow and costly. Another shortcoming was that production costs did not decrease as volume increased; there were no economies of scale, which would have provided a major incentive for companies to expand. Instead, many small companies emerged, each with its own set of standards.

A major change occurred that gave the Industrial Revolution a boost: the development of standard gauging systems. This greatly reduced the need for custom-made goods. Factories began to spring up and grow rapidly, providing jobs for countless people who were attracted in large numbers from rural areas.

Despite the major changes that were taking place, management theory and practice had not progressed much from early days. What was needed was an enlightened and more systematic approach to management.

Scientific Management

The scientific management era brought widespread changes to the management of factories. The movement was spearheaded by the efficiency engineer and inventor Frederick Winslow Taylor, who is often referred to as the father of scientific management. Taylor believed in a “science of management” based on observation, measurement, analysis and improvement of work methods, and economic incentives. He studied work methods in great detail to identify the best method for doing each job. Taylor also believed that management should be responsible for planning, carefully selecting and training workers, finding the best way to perform each job, achieving cooperation between management and workers, and separating management activities from work activities.

Taylor’s methods emphasized maximizing output. They were not always popular with workers, who sometimes thought the methods were used to unfairly increase output without a corresponding increase in compensation. Certainly, some companies did abuse workers in their quest for efficiency. Eventually, the public outcry reached the halls of Congress, and hearings were held on the matter. Taylor himself was called to testify in 1911, the same year in which his classic book, The Principles of Scientific Management, was published. The publicity from those hearings actually helped scientific management principles to achieve wide acceptance in industry.

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A number of other pioneers also contributed heavily to this movement, including the following:

Frank Gilbreth was an industrial engineer who is often referred to as the father of motion study. He developed principles of motion economy that could be applied to incredibly small portions of a task.

Henry Gantt recognized the value of nonmonetary rewards to motivate workers, and developed a widely used system for scheduling, called Gantt charts.

Harrington Emerson applied Taylor’s ideas to organization structure and encouraged the use of experts to improve organizational efficiency. He testified in a congressional hearing that railroads could save a million dollars a day by applying principles of scientific management.

Henry Ford, the great industrialist, employed scientific management techniques in his factories.

During the early part of the 20th century, automobiles were just coming into vogue in the United States. Ford’s Model T was such a success that the company had trouble keeping up with orders for the cars. In an effort to improve the efficiency of operations, Ford adopted the scientific management principles espoused by Frederick Winslow Taylor. He also introduced the moving assembly line, which had a tremendous impact on production methods in many industries.

Among Ford’s many contributions was the introduction of mass production to the automotive industry, a system of production in which large volumes of standardized goods are produced by low-skilled or semiskilled workers using highly specialized, and often costly, equipment. Ford was able to do this by taking advantage of a number of important concepts. Perhaps the key concept that launched mass production was interchangeable parts , sometimes attributed to Eli Whitney, an American inventor who applied the concept to assembling muskets in the late 1700s. The basis for interchangeable parts was to standardize parts so that any part in a batch of parts would fit any automobile coming down the assembly line. This meant that parts did not have to be custom fitted, as they were in craft production. The standardized parts could also be used for replacement parts. The result was a tremendous decrease in assembly time and cost. Ford accomplished this by standardizing the gauges used to measure parts during production and by using newly developed processes to produce uniform parts.

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A second concept used by Ford was the division of labor , which Adam Smith wrote about in The Wealth of Nations (1776). Division of labor means that an operation, such as assembling an automobile, is divided up into a series of many small tasks, and individual workers are assigned to one of those tasks. Unlike craft production, where each worker was responsible for doing many tasks, and thus required skill, with division of labor the tasks were so narrow that virtually no skill was required.

Together, these concepts enabled Ford to tremendously increase the production rate at his factories using readily available inexpensive labor. Both Taylor and Ford were despised by many workers, because they held workers in such low regard, expecting them to perform like robots. This paved the way for the human relations movement.

The Human Relations Movement

Whereas the scientific management movement heavily emphasized the technical aspects of work design, the human relations movement emphasized the importance of the human element in job design. Lillian Gilbreth, a psychologist and the wife of Frank Gilbreth, worked with her husband, focusing on the human factor in work. (The Gilbreths were the subject of a classic film, Cheaper by the Dozen.) Many of her studies dealt with worker fatigue. In the following decades, there was much emphasis on motivation. Elton Mayo conducted studies at the Hawthorne division of Western Electric. His studies revealed that in addition to the physical and technical aspects of work, worker motivation is critical for improving productivity. Abraham Maslow developed motivational theories, which Frederick Hertzberg refined. Douglas McGregor added Theory X and Theory Y. These theories represented the two ends of the spectrum of how employees view work. Theory X, on the negative end, assumed that workers do not like to work, and have to be controlled—rewarded and punished—to get them to do good work. This attitude was quite common in the automobile industry and in some other industries, until the threat of global competition forced them to rethink that approach. Theory Y, on the other end of the spectrum, assumed that workers enjoy the physical and mental aspects of work and become committed to work. The Theory X approach resulted in an adversarial environment, whereas the Theory Y approach resulted in empowered workers and a more cooperative spirit. William Ouchi added Theory Z, which combined the Japanese approach with such features as lifetime employment, employee problem solving, and consensus building, and the traditional Western approach that features short-term employment, specialists, and individual decision making and responsibility.

Decision Models and Management Science

The factory movement was accompanied by the development of several quantitative techniques. F. W. Harris developed one of the first models in 1915: a mathematical model for inventory order size. In the 1930s, three coworkers at Bell Telephone Labs—H. F. Dodge, H. G. Romig, and W. Shewhart—developed statistical procedures for sampling and quality control. In 1935, L.H.C. Tippett conducted studies that provided the groundwork for statistical sampling theory.

At first, these quantitative models were not widely used in industry. However, the onset of World War II changed that. The war generated tremendous pressures on manufacturing output, and specialists from many disciplines combined efforts to achieve advancements in the military and in manufacturing. After the war, efforts to develop and refine quantitative tools for decision making continued, resulting in decision models for forecasting, inventory management, project management, and other areas of operations management.

During the 1960s and 1970s, management science techniques were highly regarded; in the 1980s, they lost some favor. However, the widespread use of personal computers and user-friendly software in the workplace contributed to a resurgence in the popularity of these techniques.

The Influence of Japanese Manufacturers

A number of Japanese manufacturers developed or refined management practices that increased the productivity of their operations and the quality of their products, due in part to the influence of Americans W. Edwards Deming and Joseph Juran. This made them page 24very competitive, sparking interest in their approaches by companies outside Japan. Their approaches emphasized quality and continual improvement, worker teams and empowerment, and achieving customer satisfaction. The Japanese can be credited with spawning the “quality revolution” that occurred in industrialized countries, and with generating widespread interest in lean production.

The influence of the Japanese on U.S. manufacturing and service companies has been enormous and promises to continue for the foreseeable future. Because of that influence, this book will provide considerable information about Japanese methods and successes.

Table 1.5 provides a chronological summary of some of the key developments in the evolution of operations management.

TABLE 1.5

Historical summary of operations management

Environmental Concerns

Concern about global warming and pollution has had an increasing effect on how businesses operate.

Stricter environmental regulations, particularly in developed nations, are being imposed. Furthermore, business organizations are coming under increasing pressure to reduce their carbon footprint (the amount of carbon dioxide generated by their operations and their supply chains) and to generally operate sustainable processes. Sustainability refers to service page 28and production processes that use resources in ways that do not harm ecological systems that support both current and future human existence. Sustainability measures often go beyond traditional environmental and economic measures to include measures that incorporate social criteria in decision making.

All areas of business will be affected by this. Areas that will be most affected include product and service design, consumer education programs, disaster preparation and response, supply chain page 29waste management, and outsourcing decisions. Note that outsourcing of goods production increases not only transportation costs, but also fuel consumption and carbon released into the atmosphere. Consequently, sustainability thinking may have implications for outsourcing decisions.

Because they all fall within the realm of operations, operations management is central to dealing with these issues. Sometimes referred to as “green initiatives,” the possibilities include reducing packaging, materials, water and energy use, and the environmental impact of the supply chain, including buying locally. Other possibilities include reconditioning used equipment (e.g., printers and copiers) for resale, and recycling.

The reading above suggests that even our choice of diet can affect the environment.

Ethical Conduct

The need for ethical conduct in business is becoming increasingly obvious, given numerous examples of questionable actions in recent history. In making decisions, managers must consider how their decisions will affect shareholders, management, employees, customers, the community at large, and the environment. Finding solutions that will be in the best interests of all of these stakeholders is not always easy, but it is a goal that all managers should strive to achieve. Furthermore, even managers with the best intentions will sometimes make mistakes. If mistakes do occur, managers should act responsibly to correct those mistakes as quickly as possible, and to address any negative consequences.

Many organizations have developed codes of ethics to guide employees’ or members’ conduct. Ethics is a standard of behavior that guides how one should act in various situations. The Markula Center for Applied Ethics at Santa Clara University identifies five principles for thinking ethically:

• The Utilitarian Principle: The good done by an action or inaction should outweigh any harm it causes or might cause. An example is not allowing a person who has had too much to drink to drive.

• The Rights Principle: Actions should respect and protect the moral rights of others. An example is not taking advantage of a vulnerable person.

• The Fairness Principle: Equals should be held to, or evaluated by, the same standards. An example is equal pay for equal work.

• The Common Good Principle: Actions should contribute to the common good of the community. An example is an ordinance on noise abatement.

• The Virtue Principle: Actions should be consistent with certain ideal virtues. Examples include honesty, compassion, generosity, tolerance, fidelity, integrity, and self-control.

The center expands these principles to create a framework for ethical conduct. An ethical framework is a sequence of steps intended to guide thinking and subsequent decisions or actions. page 30Here is the one developed by the Markula Center for Applied Ethics:

1. Recognize an ethical issue by asking if an action could be damaging to a group or an individual. Is there more to it than just what is legal?

2. Make sure the pertinent facts are known, such as who will be impacted, and what options are available.

3. Evaluate the options by referring to the appropriate preceding ethical principle.

4. Identify the “best” option and then further examine it by asking how someone you respect would view it.

5. In retrospect, consider the effect your decision had and what you can learn from it.

More detail is available at the Center’s website: http://www.scu.edu/ethics/practicing/decision/framework.html.

Operations managers, like all managers, have the responsibility to make ethical decisions. Ethical issues arise in many aspects of operations management, including:

• Financial statements: accurately representing the organization’s financial condition.

• Worker safety: providing adequate training, maintaining equipment in good working condition, maintaining a safe working environment.

• Product safety: providing products that minimize the risk of injury to users or damage to property or the environment.

• Quality: honoring warranties, avoiding hidden defects.

• The environment: not doing things that will harm the environment.

• The community: being a good neighbor.

• Hiring and firing workers: avoiding false pretenses (e.g., promising a long-term job when that is not what is intended).

• Closing facilities: taking into account the impact on a community, and honoring commitments that have been made.

• Workers’ rights: respecting workers’ rights, dealing with workers’ problems quickly and fairly.

The Ethisphere Institute recognizes companies worldwide for their ethical leadership. Here are some samples from their list:

Apparel: Gap

Automotive: Ford Motor Company

Business services: Paychex

Café: Starbucks

Computer hardware: Intel

Computer software: Adobe Systems, Microsoft

Consumer electronics: Texas Instruments, Xerox

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E-commerce: eBay

General retail: Costco, Target

Groceries: Safeway, Wegmans, Whole Foods

Health and beauty: L’Oreal

Logistics: UPS

You can see a complete list of recent recipients and the selection criteria at Ethisphere.com.

The Need to Manage the Supply Chain

Supply chain management is being given increasing attention as business organizations face mounting pressure to improve management of their supply chains. In the past, most organizations did little to manage their supply chains. Instead, they tended to concentrate on their own operations and on their immediate suppliers. Moreover, the planning, marketing, production and inventory management functions in organizations in supply chains have often operated independently of each other. As a result, supply chains experienced a range of problems that were seemingly beyond the control of individual organizations. The problems included large oscillations of inventories, inventory stockouts, late deliveries, and quality problems. These and other issues now make it clear that management of supply chains is essential to business success. The other issues include the following:

1. The need to improve operations. Efforts on cost and time reduction, and productivity and quality improvement, have expanded in recent years to include the supply chain. Opportunity now lies largely with procurement, distribution, and logistics—the supply chain.

2. Increasing levels of outsourcing. Organizations are increasing their levels of outsourcing , buying goods or services instead of producing or providing them themselves. As outsourcing increases, some organizations are spending increasing amounts on supply-related activities (wrapping, packaging, moving, loading and unloading, and sorting). A significant amount of the cost and time spent on these and other related activities may be unnecessary. Issues with imported products, including tainted food products, toothpaste, and pet foods, as well as unsafe tires and toys, have led to questions of liability and the need for companies to take responsibility for monitoring the safety of outsourced goods.

1. Increasing transportation costs. Transportation costs are increasing, and they need to be more carefully managed.

2. Competitive pressures. Competitive pressures have led to an increasing number of new products, shorter product development cycles, and increased demand for customization. And in some industries, most notably consumer electronics, product life cycles are relatively short. Added to this are the adoption of quick-response strategies and efforts to reduce lead times.

3. Increasing globalization. Increasing globalization has expanded the physical length of supply chains. A global supply chain increases the challenges of managing a supply chain. Having far-flung customers and/or suppliers means longer lead times and greater opportunities for disruption of deliveries. Often, currency page 32differences and monetary fluctuations are factors, as well as language and cultural differences. Also, tightened border security in some instances has slowed shipments of goods.

4. Increasing importance of e-business. The increasing importance of e-business has added new dimensions to business buying and selling and has presented new challenges.

5. The complexity of supply chains. Supply chains are complex; they are dynamic, and they have many inherent uncertainties that can adversely affect them, such as inaccurate forecasts, late deliveries, substandard quality, equipment breakdowns, and canceled or changed orders.

6. The need to manage inventories. Inventories play a major role in the success or failure of a supply chain, so it is important to coordinate inventory levels throughout a supply chain. Shortages can severely disrupt the timely flow of work and have far-reaching impacts, while excess inventories add unnecessary costs. It would not be unusual to find inventory shortages in some parts of a supply chain and excess inventories in other parts of the same supply chain.

7. The need to deal with trade wars. Trade wars can occur if a country objects to its trade imbalance with another country. This can result in tariffs and retaliatory tariffs, causing changes in cost structures. Uncertainty about how long and to what degree tariffs will be in place can greatly increase pressure on companies that have global supply chains.

Elements of Supply Chain Management

Supply chain management involves coordinating activities across the supply chain. Central to this is taking customer demand and translating it into corresponding activities at each level of the supply chain.

The key elements of supply chain management are listed in Table 1.6. The first element, customers, is the driving element. Typically, marketing is responsible for determining what customers want, as well as forecasting the quantities and timing of customer demand. Product and service design must match customer wants with operations capabilities.

TABLE 1.6

Elements of supply chain management

Processing occurs in each component of the supply chain: It is the core of each organization. The major portion of processing occurs in the organization that produces the product or service for the final customer (the organization that assembles the computer, services the car, etc.). A major aspect of this for both the internal and external portions of a supply chain is scheduling.

Inventory is a staple in most supply chains. Balance is the main objective; too little causes delays and disrupts schedules, but too much adds unnecessary costs and limits flexibility.

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Purchasing is the link between an organization and its suppliers. It is responsible for obtaining goods and/or services that will be used to produce products or provide services for the organization’s customers. Purchasing selects suppliers, negotiates contracts, establishes alliances, and acts as a liaison between suppliers and various internal departments.

The supply portion of a value chain is made up of one or more suppliers, all links in the chain, and each one capable of having an impact on the effectiveness—or the ineffectiveness—of the supply chain. Moreover, it is essential that the planning and execution be carefully coordinated between suppliers and all members of the demand portion of their chains.

Location can be a factor in a number of ways. Where suppliers are located can be important, as can the location of processing facilities. Nearness to market, nearness to sources of supply, or nearness to both may be critical. Also, delivery time and cost are usually affected by location.

Two types of decisions are relevant to supply chain management—strategic and operational. The strategic decisions are the design and policy decisions. The operational decisions relate to day-to-day activities: managing the flow of material and product and other aspects of the supply chain in accordance with strategic decisions.

The major decision areas in supply chain management are location, production, distribution, and inventory. The location decision relates to the choice of locations for both production and distribution facilities. Production and transportation costs and delivery lead times are important. Production and distribution decisions focus on what customers want, when they want it, and how much is needed. Outsourcing can be a consideration. Distribution decisions are strongly influenced by transportation cost and delivery times, because transportation costs often represent a significant portion of total cost. Moreover, shipping alternatives are closely tied to production and inventory decisions. For example, using air transport means higher costs but faster deliveries and less inventory in transit than sea, rail, or trucking options. Distribution decisions must also take into account capacity and quality issues. Operational decisions focus on scheduling, maintaining equipment, and meeting customer demand. Quality control and workload balancing are also important considerations. Inventory decisions relate to determining inventory needs and coordinating production and stocking decisions throughout the supply chain. Logistics management plays the key role in inventory decisions.

Enterprise Resource Planning (ERP) is being increasingly used to provide information sharing in real time among organizations and their major supply chain partners. This important topic is discussed in more detail in Chapter 13.

Operations Tours

Throughout the book you will discover operations tours that describe operations in all sorts of companies. The tour below is of Wegmans Food Markets, a major regional supermarket chain. Wegmans has been consistently ranked high on Fortune magazine’s list of the 100 Best Companies to Work For since the inception of the survey a decade ago.

Why Some Organizations Fail

Organizations fail, or perform poorly, for a variety of reasons. Being aware of those reasons can help managers avoid making similar mistakes. Among the chief reasons are the following:

1. Neglecting operations strategy.

2. Failing to take advantage of strengths and opportunities, and/or failing to recognize competitive threats.

3. Putting too much emphasis on short-term financial performance at the expense of research and development.

4. Placing too much emphasis on product and service design and not enough on process design and improvement.

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5. Neglecting investments in capital and human resources.

6. Failing to establish good internal communications and cooperation among different functional areas.

7. Failing to consider customer wants and needs.

The key to successfully competing is to determine what customers want and then directing efforts toward meeting (or even exceeding) customer expectations. Two basic issues must be addressed. First: What do the customers want? (Which items on the preceding list of the ways business organizations compete are important to customers?) Second: What is the best way to satisfy those wants?

Operations must work with marketing to obtain information on the relative importance of the various items to each major customer or target market.

Understanding competitive issues can help managers develop successful strategies.

Strategies and Tactics

If you think of goals as destinations, then strategies are the roadmaps for reaching those destinations. Strategies provide focus for decision making. Generally speaking, organizations have overall strategies called organizational strategies, which relate to the entire organization. They also have functional strategies, which relate to each of the functional areas of the organization. The functional strategies should support the overall strategies of the organization, just as the organizational strategies should support the goals and mission of the organization.

Tactics are the methods and actions used to accomplish strategies. They are more specific than strategies, and they provide guidance and direction for carrying out actual operations, which need the most specific and detailed plans and decision making in an organization. You might think of tactics as the “how to” part of the process (e.g., how to reach the destination, following the strategy roadmap), and operations as the actual “doing” part of the process. Much of this book deals with tactical operations.

It should be apparent that the overall relationship that exists from the mission down to actual operations is hierarchical. This is illustrated in Figure 2.1.

A simple example may help to put this hierarchy into perspective.

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Here are some examples of different strategies an organization might choose from:

Low cost. Outsource operations to third-world countries that have low labor costs.

Scale-based strategies. Use capital-intensive methods to achieve high output volume and low unit costs.

Specialization. Focus on narrow product lines or limited service to achieve higher quality.

Newness. Focus on innovation to create new products or services.

Flexible operations. Focus on quick response and/or customization.

High quality. Focus on achieving higher quality than competitors.

Service. Focus on various aspects of service (e.g., helpful, courteous, reliable, etc.).

Sustainability. Focus on environmental-friendly and energy-efficient operations.

A wide range of business organizations are beginning to recognize the strategic advantages of sustainability, not only in economic terms, but also through promotional benefits by publicizing their sustainability efforts and achievements.

Sometimes, organizations will combine two or more of these, or other approaches, into their strategy. However, unless they are careful, they risk losing focus and not achieving advantage in any category. Generally speaking, strategy formulation takes into account the way organizations compete and a particular organization’s assessment of its own strengths and weaknesses in order to take advantage of its core competencies —those special attributes or abilities possessed by an organization that give it a competitive edge.

The most effective organizations use an approach that develops core competencies based on customer needs, as well as on what the competition is doing. Marketing and operations work closely to match customer needs with operations capabilities. Competitor competencies are important for several reasons. For example, if a competitor is able to supply high-quality products, it may be necessary to meet that high quality as a baseline. However, merely matching a competitor is usually not sufficient to gain market share. It may be necessary to exceed the quality level of the competitor or gain an edge by excelling in one or more other dimensions, such as rapid delivery or service after the sale. Walmart, for example, has been very successful in managing its supply chain, which has contributed to its competitive advantage.

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To be effective, strategies and core competencies need to be aligned. Table 2.2 lists examples of strategies and companies that have successfully employed those strategies.

TABLE 2.2

Examples of operations strategies

Strategy Formulation

Strategy formulation is almost always critical to the success of a strategy. Walmart discovered this when it opened stores in Japan. Although Walmart thrived in many countries on its reputation for low-cost items, Japanese consumers associated low cost with low quality, causing Walmart to rethink its strategy in the Japanese market. And many felt that Hewlett-Packard (HP) committed a strategic error when it acquired Compaq Computers at a cost of $19 billion. HP’s share of the computer market was less after the merger than the sum of the shares of the separate companies before the merger. In another example, U.S. automakers adopted a strategy in the early 2000s of offering discounts and rebates on a range of cars and SUVs, many of which were on low-margin vehicles. The strategy put a strain on profits, but customers began to expect those incentives, and the companies maintained them to keep from losing additional market share.

On the other hand, Coach, the maker of leather handbags and purses, successfully changed its longtime strategy to grow its market by creating new products. Long known for its highly durable leather goods in a market where women typically owned few handbags, Coach created a new market for itself by changing women’s view of handbags by promoting “different handbags for different occasions” such as party bags, totes, clutches, wristlets, overnight bags, purses, and day bags. And Coach introduced many fashion styles and colors.

To formulate an effective strategy, senior managers must take into account the core competencies of the organizations, and they must scan the environment. They must determine page 48what competitors are doing, or planning to do, and take that into account. They must critically examine other factors that could have either positive or negative effects. This is sometimes referred to as the SWOT analysis (strengths, weaknesses, opportunities, and threats). Strengths and weaknesses have an internal focus and are typically evaluated by operations people. Threats and opportunities have an external focus and are typically evaluated by marketing people. SWOT is often regarded as the link between organizational strategy and operations strategy.

An alternative to SWOT analysis is Michael Porter’s five forces model, ¹ which takes into account the threat of new competition, the threat of substitute products or services, the bargaining power of customers, the bargaining power of suppliers, and the intensity of competition.

In formulating a successful strategy, organizations must take into account both order qualifiers and order winners. Order qualifiers are those characteristics that potential customers perceive as minimum standards of acceptability for a product to be considered for purchase. However, that may not be sufficient to get a potential customer to purchase from the organization. Order winners are those characteristics of an organization’s goods or services that cause them to be perceived as better than the competition.

Characteristics such as price, delivery reliability, delivery speed, and quality can be order qualifiers or order winners. Thus, quality may be an order winner in some situations, but in others only an order qualifier. Over time, a characteristic that was once an order winner may become an order qualifier.

Obviously, it is important to determine the set of order qualifier characteristics and the set of order winner characteristics. It is also necessary to decide on the relative importance of each characteristic so that appropriate attention can be given to the various characteristics. Marketing must make that determination and communicate it to operations.

Environmental scanning is the monitoring of events and trends that present either threats or opportunities for the organization. Generally, these include competitors’ activities; changing consumer needs; legal, economic, political, and environmental issues; the potential for new markets; and the like.

Another key factor to consider when developing strategies is technological change, which can present real opportunities and threats to an organization. Technological changes occur in products (high-definition TV, improved computer chips, improved cellular telephone systems, and improved designs for earthquake-proof structures); in services (faster order processing, faster delivery); and in processes (robotics, automation, computer-assisted processing, point-of-sale scanners, and flexible manufacturing systems). The obvious benefit is a competitive edge; the risk is that incorrect choices, poor execution, and higher-than-expected operating costs will create competitive disadvantages.

Important factors may be internal or external. The following are key external factors:

1. Economic conditions. These include the general health and direction of the economy, inflation and deflation, interest rates, tax laws, and tariffs.

2. Political conditions. These include favorable or unfavorable attitudes toward business, political stability or instability, and wars.

3. Legal environment. This includes antitrust laws, government regulations, trade restrictions, minimum wage laws, product liability laws and recent court experience, labor laws, and patents.

4. Technology. This can include the rate at which product innovations are occurring, current and future process technology (equipment, materials handling), and design technology.

5. Competition. This includes the number and strength of competitors, the basis of competition (price, quality, special features), and the ease of market entry.

6. Customers. Loyalty, existing relationships, and understanding of wants and needs are important.

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7. Suppliers. Supplier relationships, dependability of suppliers, quality, flexibility, and service are typical considerations.

8. Markets. This includes size, location, brand loyalties, ease of entry, potential for growth, long-term stability, and demographics.

The organization also must take into account various internal factors that relate to possible strengths or weaknesses. Among the key internal factors are the following:

1. Human resources. These include the skills and abilities of managers and workers, special talents (creativity, designing, problem solving), loyalty to the organization, expertise, dedication, and experience.

2. Facilities and equipment. Capacities, location, age, and cost to maintain or replace can have a significant impact on operations.

3. Financial resources. Cash flow, access to additional funding, existing debt burden, and cost of capital are important considerations.

4. Products and services. These include existing products and services, and the potential for new products and services.

5. Technology. This includes existing technology, the ability to integrate new technology, and the probable impact of technology on current and future operations.

6. Other. Other factors include patents, labor relations, company or product image, distribution channels, relationships with distributors, maintenance of facilities and equipment, access to resources, and access to markets.

After assessing internal and external factors and an organization’s distinctive competence, a strategy or strategies must be formulated that will give the organization the best chance of success. Among the types of questions that may need to be addressed are the following:

What role, if any, will the internet play?

Will the organization have a global presence?

To what extent will outsourcing be used?

What will the supply chain management strategy be?

To what extent will new products or services be introduced?

What rate of growth is desirable and sustainable?

What emphasis, if any, should be placed on lean production?

How will the organization differentiate its products and/or services from competitors’?

The organization may decide to have a single, dominant strategy (e.g., be the price leader) or have multiple strategies. A single strategy would allow the organization to concentrate on one particular strength or market condition. On the other hand, multiple strategies may be needed to address a particular set of conditions.

Many companies are increasing their use of outsourcing to reduce overhead, gain flexibility, and take advantage of suppliers’ expertise. Amazon provides a great example of some of the potential benefits of outsourcing as part of a business strategy.

Growth is often a component of strategy, especially for new companies. A key aspect of this strategy is the need to seek a growth rate that is sustainable. In the 1990s, fast-food company Boston Market dazzled investors and fast-food consumers alike. Fueled by its success, it undertook rapid expansion. By the end of the decade, the company was nearly bankrupt; it had overexpanded. In 2000, it was absorbed by fast-food giant McDonald’s.

Companies increase their risk of failure not only by missing or incomplete strategies; they also fail due to poor execution of strategies. And sometimes they fail due to factors beyond their control, such as natural or man-made disasters, major political or economic changes, or competitors that have an overwhelming advantage (e.g., deep pockets, very low labor costs, less rigorous environmental requirements).

A useful resource on successful business strategies is the Profit Impact of Market Strategy (PIMS) database ( www.pimsonline.com). The database contains profiles of over 3,000 businesses located primarily in the United States, Canada, and western Europe. It is used by companies and academic institutions to guide strategic thinking. It allows subscribers to answer strategy questions about their business. Moreover, they can use it to generate benchmarks and develop successful strategies.

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According to the PIMS website,

The database is a collection of statistically documented experiences drawn from thousands of businesses, designed to help understand what kinds of strategies (e.g., quality, pricing, vertical integration, innovation, advertising) work best in what kinds of business environments. The data constitute a key resource for such critical management tasks as evaluating business performance, analyzing new business opportunities, evaluating and reality testing new strategies, and screening business portfolios. The primary role of the PIMS Program of the Strategic Planning Institute is to help managers understand and react to their business environment. PIMS does this by assisting managers as they develop and test strategies that will achieve an acceptable level of winning as defined by various strategies and financial measures.

Source: https://www.inc.com/encyclopedia/profit-impact-of-market-strategies-pims.html

Supply Chain Strategy

A supply chain strategy specifies how the supply chain should function to achieve supply chain goals. The supply chain strategy should be aligned with the business strategy. If it is well executed, it can create value for the organization. It establishes how the organization should work with suppliers and policies relating to customer relationships and sustainability. Supply chain strategy is covered in more detail in a later chapter.

Sustainability Strategy

Society is placing increasing emphasis on corporate sustainability practices in the form of governmental regulations and interest groups. For these and other reasons, business organizations are or should be devoting attention to sustainability goals. To be successful, they will need a sustainability strategy. That requires elevating sustainability to the level of organizational page 51governance; formulating goals for products and services, for processes, and for the entire supply chain; measuring achievements and striving for improvements; and possibly linking executive compensation to the achievement of sustainability goals.

Global Strategy

Global strategies have two different aspects. One relates to where parts or products are made, or where services such as customer support are performed. The other relates to where products or service are sold. With wages and standards of living increases in countries such as China and India, new market opportunities present themselves, requiring well-thought out strategies to take advantage of those potential opportunities while minimizing any associated risks.

As globalization increased, many companies realized that strategic decisions with respect to globalization had to be made. One issue companies face today is that what works in one country or region does not necessarily work in another, and strategies must be carefully crafted to take these variabilities into account. Another issue is the threat of political or social upheaval. Still another issue is the difficulty of coordinating and managing far-flung operations. Indeed, “In today’s global markets, you don’t have to go abroad to experience international competition. Sooner or later the world comes to you.” ²

Strategic Operations Management Decision Areas

Operations management people play a strategic role in many strategic decisions in a business organization. Table 2.4 highlights some key decision areas. Notice that most of the decision areas have cost implications.

TABLE 2.4

Strategic operations management decisions

Two factors that tend to have universal strategic operations importance relate to quality and time. The following section discusses quality and time strategies.

Quality and Time Strategies

Traditional strategies of business organizations have tended to emphasize cost minimization or product differentiation. While not abandoning those strategies, many organizations have embraced strategies based on quality and/or time.

Quality-based strategies focus on maintaining or improving the quality of an organization’s products or services. Quality is generally a factor in both attracting and retaining customers. page 53Quality-based strategies may be motivated by a variety of factors. They may reflect an effort to overcome an image of poor quality, a desire to catch up with the competition, a desire to maintain an existing image of high quality, or some combination of these and other factors. Interestingly enough, quality-based strategies can be part of another strategy such as cost reduction, increased productivity, or time, all of which benefit from higher quality.

Time-based strategies focus on reducing the time required to accomplish various activities (e.g., develop new products or services and market them, respond to a change in customer demand, or deliver a product or perform a service). By doing so, organizations seek to improve service to the customer and to gain a competitive advantage over rivals who take more time to accomplish the same tasks.

Time-based strategies focus on reducing the time needed to conduct the various activities in a process. The rationale is that by reducing time, costs are generally less, productivity is higher, quality tends to be higher, product innovations appear on the market sooner, and customer service is improved.

Organizations have achieved time reduction in some of the following:

Planning time: The time needed to react to a competitive threat, to develop strategies and select tactics, to approve proposed changes to facilities, to adopt new technologies, and so on.

Product/service design time: The time needed to develop and market new or redesigned products or services.

Processing time: The time needed to produce goods or provide services. This can involve scheduling, repairing equipment, methods used, inventories, quality, training, and the like.

Changeover time: The time needed to change from producing one type of product or service to another. This may involve new equipment settings and attachments, different methods, equipment, schedules, or materials.

Delivery time: The time needed to fill orders.

Response time for complaints: These might be customer complaints about quality, timing of deliveries, and incorrect shipments. These might also be complaints from employees about working conditions (e.g., safety, lighting, heat or cold), equipment problems, or quality problems.

It is essential for marketing and operations personnel to collaborate on strategy formulation in order to ensure that the buying criteria of the most important customers in each market segment are addressed.

Agile operations is a strategic approach for competitive advantage that emphasizes the use of flexibility to adapt and prosper in an environment of change. Agility involves a blending of several distinct competencies such as cost, quality, and reliability along with flexibility. Processing aspects of flexibility include quick equipment changeovers, scheduling, and innovation. Product or service aspects include varying output volumes and product mix.

Successful agile operations requires careful planning to achieve a system that includes people, flexible equipment, and information technology. Reducing the time needed to perform work is one of the ways an organization can improve a key metric: productivity.

Computing Productivity

Productivity measures can be based on a single input (partial productivity), on more than one input (multifactor productivity), or on all inputs (total productivity). Table 2.7 lists some examples of productivity measures. The choice of productivity measure depends primarily on the purpose of the measurement. If the purpose is to track improvements in labor productivity, then labor becomes the obvious input measure.

TABLE 2.7

Some examples of different types of productivity measures

Partial measures are often of greatest use in operations management. Table 2.8 provides some examples of partial productivity measures.

TABLE 2.8

Some examples of partial productivity measures

The units of output used in productivity measures depend on the type of job performed. The following are examples of labor productivity:

Similar examples can be listed for machine productivity (e.g., the number of pieces per hour turned out by a machine).

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Calculations of multifactor productivity measure inputs and outputs using a common unit of measurement, such as cost. For instance, the measure might use cost of inputs and units of the output:

Note: The unit of measure must be the same for all factors in the denominator

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Productivity measures are useful on a number of levels. For an individual department or organization, productivity measures can be used to track performance over time. This allows managers to judge performance and to decide where improvements are needed. For example, if productivity has slipped in a certain area, operations staff can examine the factors used to compute productivity to determine what has changed and then devise a means of improving productivity in subsequent periods.

Productivity measures also can be used to judge the performance of an entire industry or the productivity of a country as a whole. These productivity measures are aggregate measures.

In essence, productivity measurements serve as scorecards of the effective use of resources. Business leaders are concerned with productivity as it relates to competitiveness: If two firms both have the same level of output but one requires less input because of higher productivity, that one will be able to charge a lower price and consequently increase its share of the market. Or that firm might elect to charge the same price, thereby reaping a greater profit. Government leaders are concerned with national productivity because of the close relationship between productivity and a nation’s standard of living. High levels of productivity are largely responsible for the relatively high standards of living enjoyed by people in industrial nations. Furthermore, wage and price increases not accompanied by productivity increases tend to create inflationary pressures on a nation’s economy.

Advantages of domestic-based operations for domestic markets often include higher worker productivity, better control of quality, avoidance of intellectual property losses, lower shipping costs, political stability, low inflation, and faster delivery.

Productivity in the Service Sector

Service productivity is more problematic than manufacturing productivity. In many situations, it is more difficult to measure, and thus to manage, because it involves intellectual activities and a high degree of variability. Think about medical diagnoses, surgery, consulting, legal services, customer service, and computer repair work. This makes productivity improvements more difficult to achieve. Nonetheless, because service is becoming an increasingly large portion of our economy, the issues related to service productivity will have to be dealt with. It is interesting to note that government statistics normally do not include service firms.

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A useful measure closely related to productivity is process yield. Where products are involved, process yield is defined as the ratio of output of good product (i.e., defective product is not included) to the quantity of raw material input. Where services are involved, process yield measurement is often dependent on the particular process. For example, in a car rental agency, a measure of yield is the ratio of cars rented to cars available for a given day. In education, a measure for college and university admission yield is the ratio of student acceptances to the total number of students approved for admission. For subscription services, yield is the ratio of new subscriptions to the number of calls made or the number of letters mailed. However, not all services lend themselves to a simple yield measurement. For example, services such as automotive, appliance, and computer repair don’t readily lend themselves to such measures.

Factors that Affect Productivity

Numerous factors affect productivity. Generally, they are methods, capital, quality, technology, and management.

A commonly held misconception is that workers are the main determinant of productivity. According to that theory, the route to productivity gains involves getting employees to work harder. However, the fact is that many productivity gains in the past have come from technological improvements. Familiar examples include:

However, technology alone won’t guarantee productivity gains; it must be used wisely and thoughtfully. Without careful planning, technology can actually reduce productivity, especially if it leads to inflexibility, high costs, or mismatched operations. Another current productivity pitfall results from employees’ use of computers or smartphones for nonwork-related activities (playing games or checking stock prices or sports scores on the internet or smartphones, and texting friends and relatives). Beyond all of these is the dip in productivity that results while employees learn to use new equipment or procedures that will eventually lead to productivity gains after the learning phase ends.

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Other factors that affect productivity include the following:

Standardizing processes and procedures wherever possible to reduce variability can have a significant benefit for both productivity and quality.

Quality differences may distort productivity measurements. One way this can happen is when comparisons are made over time, such as comparing the productivity of a factory now with one 30 years ago. Quality is now much higher than it was then, but there is no simple way to incorporate quality improvements into productivity measurements.

Use of the internet can lower costs of a wide range of transactions, thereby increasing productivity. It is likely that this effect will continue to increase productivity in the foreseeable future.

Computer viruses can have an immense negative impact on productivity.

Searching for lost or misplaced items wastes time, hence negatively affecting productivity.

Scrap rates have an adverse effect on productivity, signaling inefficient use of resources.

New workers tend to have lower productivity than seasoned workers. Thus, growing companies may experience a productivity lag.

Safety should be addressed. Accidents can take a toll on productivity.

A shortage of technology-savvy workers hampers the ability of companies to update computing resources, generate and sustain growth, and take advantage of new opportunities.

Layoffs often affect productivity. The effect can be positive and negative. Initially, productivity may increase after a layoff, because the workload remains the same but fewer workers do the work—although they have to work harder and longer to do it. However, as time goes by, the remaining workers may experience an increased risk of burnout, and they may fear additional job cuts. The most capable workers may decide to leave.

Labor turnover has a negative effect on productivity; replacements need time to get up to speed.

Design of the workspace can impact productivity. For example, having tools and other work items within easy reach can positively impact productivity.

Incentive plans that reward productivity increases can boost productivity.

And there are still other factors that affect productivity, such as equipment breakdowns and shortages of parts or materials. The education level and training of workers and their health can greatly affect productivity. The opportunity to obtain lower costs due to higher productivity elsewhere is a key reason many organizations turn to outsourcing. Hence, an alternative to outsourcing can be improved productivity. Moreover, as a part of their strategy for quality, the best organizations strive for continuous improvement. Productivity improvements can be an important aspect of that approach.

Improving Productivity

A company or a department can take a number of key steps toward improving productivity:

1. Develop productivity measures for all operations. Measurement is the first step in managing and controlling an operation.

2. Look at the system as a whole in deciding which operations are most critical. It is overall productivity that is important. Managers need to reflect on the value of potential productivity improvements before okaying improvement efforts. The issue is effectiveness. There are several aspects of this. One is to make sure the result will be something customers want. For example, if a company is able to increase its output through productivity improvements, but then is unable to sell the increased output, the increase in productivity isn’t effective. Second, it is important to adopt a systems viewpoint: A productivity increase in one part of an operation that doesn’t increase the productivity of the system would not be effective. For example, suppose a system consists of a sequence of two operations, where the output of the first operation is the input to the second operation, and each operation can complete its part of the process at a rate of 20 units per hour. If the productivity of the first operation is increased, but the productivity of the second operation is not, the output of the system will still be 20 units per hour.

3. Develop methods for achieving productivity improvements, such as soliciting ideas from workers (perhaps organizing teams of workers, engineers, and managers), studying how other firms have increased productivity, and reexamining the way work is done.

4. Establish reasonable goals for improvement.

5. Make it clear that management supports and encourages productivity improvement. Consider incentives to reward workers for contributions.

6. Measure improvements and publicize them.

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Don’t confuse productivity with efficiency. Efficiency is a narrower concept that pertains to getting the most out of a fixed set of resources; productivity is a broader concept that pertains to effective use of overall resources. For example, an efficiency perspective on mowing a lawn with a hand mower would focus on the best way to use the hand mower; a productivity perspective would include the possibility of using a power mower.

Fracking productivity improvement is another example. Drilling methods have become more effective. Drillers are now adopting a hydraulic fracturing method pioneered by companies such as Liberty Resources and EOG Resources that uses larger amounts of water and minerals. Although it is a more costly process, it has increased production rates in the first year of a well’s life, after which output tends to drop off dramatically. Processes such as these have reduced the break-even cost of producing a barrel of oil and kept profitable some acreage that drillers might otherwise have left idle.

Executive Opinions

A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast.

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Salesforce Opinions

Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future. There are, however, several drawbacks to using salesforce opinions. One is that staff members may be unable to distinguish between what customers would like to do and what they actually will do. Another is that these people are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic. In addition, if forecasts are used to establish sales quotas, there will be a conflict of interest because it is to the salesperson’s advantage to provide low sales estimates.

Consumer Surveys

Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers. Therefore, organizations seeking consumer input usually resort to consumer surveys, which enable them to sample consumer opinions. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. On the other hand, a considerable amount of knowledge and skill is required to construct a survey, administer it, and correctly interpret the results for valid information. Surveys can be expensive and time-consuming. In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns. For example, much of the consumer’s thoughtful information gathering before purchasing a new car is often undermined by the glitter of a new car showroom or a high-pressure sales pitch. Along the same lines, low response rates to a mail survey should—but often don’t—make the results suspect.

If these and similar pitfalls can be avoided, surveys can produce useful information.

Other Approaches

A manager may solicit opinions from a number of other managers and staff people. Occasionally, outside experts are needed to help with a forecast. Advice may be needed on political or economic conditions in the United States or a foreign country, or some other aspect of importance with which an organization lacks familiarity.

Another approach is the Delphi method , an iterative process intended to achieve a consensus forecast. This method involves circulating a series of questionnaires among individuals who possess the knowledge and ability to contribute meaningfully. Responses are kept anonymous, which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail. Each new questionnaire is developed using the information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgments.

The Delphi method has been applied to a variety of situations, not all of which involve forecasting. The discussion here is limited to its use as a forecasting tool.

As a forecasting tool, the Delphi method is useful for technological forecasting; that is, for assessing changes in technology and their impact on an organization. Often, the goal is to predict when a certain event will occur. For instance, the goal of a Delphi forecast might be to predict when video telephones might be installed in at least 50 percent of residential homes or when a vaccine for a disease might be developed and ready for mass distribution. For the most part, these are long-term, single-time forecasts, which usually have very little hard information to go by or data that are costly to obtain, so the problem does not lend itself to analytical techniques. Rather, judgments of experts or others who possess sufficient knowledge to make predictions are used.

Naive Methods

A simple but widely used approach to forecasting is the naive approach. A naive forecast uses a single previous value of a time series as the basis of a forecast. The naive approach can be used with a stable series (variations around an average), with seasonal variations, or with trend. With a stable series, the last data point becomes the forecast for the next period. Thus, if demand for a product last week was 20 cases, the forecast for this week is 20 cases. With seasonal variations, the forecast for this “season” is equal to the value of the series last “season.” For example, the forecast for demand for turkeys this Thanksgiving season is equal to demand for turkeys last Thanksgiving; the forecast of the number of checks cashed at a bank on the first day of the month next month is equal to the number of checks cashed on the first day of this month; and the forecast for highway traffic volume this Friday is equal to the highway traffic volume last Friday. For data with trend, the forecast is equal to the last value of the series plus or minus the difference between the last two page 83values of the series. For example, suppose the last two values were 50 and 53. The next forecast would be 56:

Although at first glance the naive approach may appear too simplistic, it is nonetheless a legitimate forecasting tool. Consider the advantages: It has virtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and it is easily understandable. The main objection to this method is its inability to provide highly accurate forecasts. However, if resulting accuracy is acceptable, this approach deserves serious consideration. Moreover, even if other forecasting techniques offer better accuracy, they will almost always involve a greater cost. The accuracy of a naive forecast can serve as a standard of comparison against which to judge the cost and accuracy of other techniques. Thus, managers must answer the question: Is the increased accuracy of another method worth the additional resources required to achieve that accuracy?

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Techniques for Averaging

Historical data typically contain a certain amount of random variation, or white noise, that tends to obscure systematic movements in the data. This randomness arises from the combined influence of many—perhaps a great many—relatively unimportant factors, and it cannot be reliably predicted. Averaging techniques smooth variations in the data. Ideally, it would be desirable to completely remove any randomness from the data and leave only “real” variations, such as changes in the demand. As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are “real.”

Averaging techniques smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. A forecast based on an average thus tends to exhibit less variability than the original data (see Figure 3.2). This can be advantageous because many of these movements merely reflect random variability rather than a true change in the series. Moreover, because responding to changes in expected demand often entails considerable cost (e.g., changes in production rate, changes in the size of a workforce, inventory changes), it is desirable to avoid reacting to minor variations. Thus, minor variations are treated as random variations, whereas larger variations are viewed as more likely to reflect “real” changes, although these, too, are smoothed to a certain degree.

Averaging techniques generate forecasts that reflect recent values of a time series (e.g., the average value over the last several periods). These techniques work best when a series tends to vary around an average, although they also can handle step changes or gradual changes in the level of the series. Three techniques for averaging are described in this section:

1. Moving average

2. Weighted moving average

3. Exponential smoothing

Moving Average One weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all. But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome. A moving average forecast uses a number of the most recent actual data values in generating a forecast. The moving average forecast can be computed using the following equation:

where

For example, MA ₃ would refer to a three-period moving average forecast, and MA ₅ would refer to a five-period moving average forecast.

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Note that in a moving average, as each new actual value becomes available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the average. Consequently, the forecast “moves” by reflecting only the most recent values.

In computing a moving average, including a moving total column—which gives the sum of the n most current values from which the average will be computed—aids computations. To update the moving total: Subtract the oldest value from the newest value and add that amount to the moving total for each update.

Figure 3.3 illustrates a three-period moving average forecast plotted against actual demand over 31 periods. Note how the moving average forecast lags the actual values and how smooth the forecasted values are compared with the actual values.

The moving average can incorporate as many data points as desired. In selecting the number of periods to include, the decision maker must take into account that the number of data points in the average determines its sensitivity to each new data point: The fewer the data points in an average, the more sensitive (responsive) the average tends to be. (See Figure 3.4A.)

If responsiveness is important, a moving average with relatively few data points should be used. This will permit quick adjustment to, say, a step change in the data, but it also will page 86cause the forecast to be somewhat responsive even to random variations. Conversely, moving averages based on more data points will smooth more but be less responsive to “real” changes. Hence, the decision maker must weigh the cost of responding more slowly to changes in the data against the cost of responding to what might simply be random variations. A review of forecast errors can help in this decision.

The advantages of a moving average forecast are that it is easy to compute and easy to understand. A possible disadvantage is that all values in the average are weighted equally. For instance, in a 10-period moving average, each value has a weight of 1/10. Hence, the oldest value has the same weight as the most recent value. If a change occurs in the series, a moving average forecast can be slow to react, especially if there are a large number of values in the average. Decreasing the number of values in the average increases the weight of more recent values, but it does so at the expense of losing potential information from less recent values.

Weighted Moving Average A weighted average is similar to a moving average, except that it typically assigns more weight to the most recent values in a time series. For instance, the most recent value might be assigned a weight of .40, the next most recent value a weight of .30, the next after that a weight of .20, and the next after that a weight of .10. Note that the weights must sum to 1.00, and that the heaviest weights are assigned to the most recent values.

where

Note that if four weights are used, only the four most recent demands are used to prepare the forecast.

The advantage of a weighted average over a simple moving average is that the weighted average is more reflective of the most recent occurrences. However, the choice of weights is somewhat arbitrary and generally involves the use of trial and error to find a suitable weighting scheme.

Exponential Smoothing Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point. That is:

where (Actual − Previous forecast) represents the forecast error and α is a percentage of the error. More concisely,

where

The smoothing constant α represents a percentage of the forecast error. Each new forecast is equal to the previous forecast plus a percentage of the previous error. For example, suppose the previous forecast was 42 units, actual demand was 40 units, and α = .10. The new forecast would be computed as follows:

Then, if the actual demand turns out to be 43, the next forecast would be

An alternate form of Formula 3–3a reveals the weighting of the previous forecast and the latest actual demand:

For example, if α = .10, this would be

The quickness of forecast adjustment to error is determined by the smoothing constant, α. The closer its value is to zero, the slower the forecast will be to adjust to forecast errors (i.e., the greater the smoothing). Conversely, the closer the value of α is to 1.00, the greater the responsiveness and the less the smoothing. This is illustrated in Figure 3.4B.

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Selecting a smoothing constant is basically a matter of judgment or trial and error, using forecast errors to guide the decision. The goal is to select a smoothing constant that balances the benefits of smoothing random variations with the benefits of responding to real changes if and when they occur. Commonly used values of α range from .05 to .50. Low values of α are used when the underlying average tends to be stable; higher values are used when the underlying average is susceptible to change.

Some computer packages include a feature that permits automatic modification of the smoothing constant if the forecast errors become unacceptably large.

Exponential smoothing is one of the most widely used techniques in forecasting, partly because of its ease of calculation and partly because of the ease with which the weighting scheme can be altered—simply by changing the value of α.

Note: Exponential smoothing should begin several periods back to enable forecasts to adjust to the data, instead of starting one period back. A number of different approaches can be used to obtain a starting forecast, such as the average of the first several periods, a subjective estimate, or the first actual value as the forecast for period 2 (i.e., the naive approach). For simplicity, the naive approach is used in this book. In practice, using an average of, say, the first three values as a forecast for period 4 would provide a better starting forecast because that would tend to be more representative.

Other Forecasting Methods

You may find two other approaches to forecasting interesting. They are briefly described in this section.

Focus Forecasting Some companies use forecasts based on a “best recent performance” basis. This approach, called focus forecasting , was developed by Bernard T. Smith, page 89and is described in several of his books. ¹ It involves the use of several forecasting methods (e.g., moving average, weighted average, and exponential smoothing) all being applied to the last few months of historical data after any irregular variations have been removed. The method that has the highest accuracy is then used to make the forecast for the next month. This process is used for each product or service, and is repeated monthly.

Diffusion Models When new products or services are introduced, historical data are not generally available on which to base forecasts. Instead, predictions are based on rates of product adoption and usage spread from other established products, using mathematical diffusion models. These models take into account such factors as market potential, attention from mass media, and word of mouth. Although the details are beyond the scope of this text, it is important to point out that diffusion models are widely used in marketing and to assess the merits of investing in new technologies.

Techniques for Trend

Analysis of trend involves developing an equation that will suitably describe trend (assuming that trend is present in the data). The trend component may be linear, or it may not. Some commonly encountered nonlinear trend types are illustrated in Figure 3.5. A simple plot of the data often can reveal the existence and nature of a trend. The discussion here focuses exclusively on linear trends because these are fairly common.

There are two important techniques that can be used to develop forecasts when trend is present. One involves use of a trend equation; the other is an extension of exponential smoothing.

Trend Equation A linear trend equation has the form

where

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For example, consider the trend equation F _t = 45 + 5 t. The value of F _t when t = 0 is 45, and the slope of the line is 5, which means that, on average, the value of F _t will increase by five units for each time period. If t = 10, the forecast, F _t , is 45 + 5(10) = 95 units. The equation can be plotted by finding two points on the line. One can be found by substituting some value of t into the equation (e.g., t = 10) and then solving for F _t . The other point is a (i.e., F _t at t = 0). Plotting those two points and drawing a line through them yields a graph of the linear trend line.

The coefficients of the line, a and b, are based on the following two equations:

where

Note that these two equations are identical to those used for computing a linear regression line, except that t replaces x in the equations. Values for the trend equation can be obtained easily by using the Excel template.

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Trend-Adjusted Exponential Smoothing

A variation of simple exponential smoothing can be used when a time series exhibits a linear trend. It is called trend-adjusted exponential smoothing , or sometimes double smoothing, to differentiate it from simple exponential smoothing, which is appropriate only when data vary around an average or have step or gradual changes. If a series exhibits a trend, and simple smoothing is used on it, the forecasts will all lag the trend: If the data are increasing, each forecast will be too low; if decreasing, each forecast will be too high.

The trend-adjusted forecast (TAF) is composed of two elements—a smoothed error and a trend factor.

where

and

where

In order to use this method, one must select values of α and β (usually through trial and error) and make a starting forecast and an estimate of trend.

Using the cell phone data from the previous example (where it was concluded that the data exhibited a linear trend), use trend-adjusted exponential smoothing to obtain forecasts for periods 6 through 11, with α = .40 and β = .30.

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The initial estimate of trend is based on the net change of 28 for the three changes from period 1 to period 4, for an average of 9.33. The Excel spreadsheet is shown in Table 3.2. Notice that an initial estimate of trend is estimated from the first four values and that the starting forecast (period 5) is developed using the previous (period 4) value of 728 plus the initial trend estimate:

TABLE 3.2

Using the Excel template for trend-adjusted smoothing

Source: Microsoft

Unlike a linear trend line, trend-adjusted smoothing has the ability to adjust to changes in trend. Of course, trend projections are much simpler with a trend line than with trend-adjusted forecasts, so a manager must decide which benefits are most important when choosing between these two techniques for trend.

Techniques for Seasonality

Seasonal variations in time-series data are regularly repeating upward or downward movements in series values that can be tied to recurring events. Seasonality may refer to regular annual variations. Familiar examples of seasonality are weather variations (e.g., sales of winter and summer sports equipment) and vacations or holidays (e.g., airline travel, greeting card sales, visitors at tourist and resort centers). The term seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data. For example, rush hour traffic occurs twice a day—incoming in the morning and outgoing in the late afternoon. Theaters and restaurants often experience weekly demand patterns, with demand higher later in the week. Banks may experience daily seasonal variations (heavier traffic during the noon hour and just before closing), weekly variations (heavier toward the end of the week), and monthly variations (heaviest around the beginning of the month because of Social Security, payroll, and welfare checks being cashed or deposited). Mail volume; sales of toys, beer, automobiles, and turkeys; highway usage; hotel registrations; and gardening also exhibit seasonal variations.

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Seasonality in a time series is expressed in terms of the amount that actual values deviate from the average value of a series. If the series tends to vary around an average value, then seasonality is expressed in terms of that average (or a moving average); if trend is present, seasonality is expressed in terms of the trend value.

There are two different models of seasonality: additive and multiplicative. In the additive model, seasonality is expressed as a quantity (e.g., 20 units), which is added to or subtracted from the series average in order to incorporate seasonality. In the multiplicative model, seasonality is expressed as a percentage of the average (or trend) amount (e.g., 1.10), which is then used to multiply the value of a series to incorporate seasonality. Figure 3.6 illustrates the two models for a linear trend line. In practice, businesses use the multiplicative model much more widely than the additive model, because it tends to be more representative of actual experience, so we will focus exclusively on the multiplicative model.

The seasonal percentages in the multiplicative model are referred to as seasonal relatives or seasonal indexes. Suppose that the seasonal relative for the quantity of toys sold in May at a store is 1.20. This indicates that toy sales for that month are 20 percent above the monthly average. A seasonal relative of .90 for July indicates that July sales are 90 percent of the monthly average.

Knowledge of seasonal variations is an important factor in retail planning and scheduling. Moreover, seasonality can be an important factor in capacity planning for systems that must be designed to handle peak loads (e.g., public transportation, electric power plants, highways, and bridges). Knowledge of the extent of seasonality in a time series can enable one to remove seasonality from the data (i.e., to seasonally adjust data) in order to discern other patterns page 95or the lack of patterns in the series. Thus, one frequently reads or hears about “seasonally adjusted unemployment” and “seasonally adjusted personal income.”

The next section briefly describes how seasonal relatives are used.

Using Seasonal Relatives Seasonal relatives are used in two different ways in forecasting. One way is to deseasonalize data; the other way is to incorporate seasonality in a forecast.

To deseasonalize data is to remove the seasonal component from the data in order to get a clearer picture of the nonseasonal (e.g., trend) components. Deseasonalizing data is accomplished by dividing each data point by its corresponding seasonal relative (e.g., divide November demand by the November relative, divide December demand by the December relative, and so on).

Incorporating seasonality in a forecast is useful when demand has both trend (or average) and seasonal components. Incorporating seasonality can be accomplished in this way:

1. Obtain trend estimates for desired periods using a trend equation.

2. Add seasonality to the trend estimates by multiplying (assuming a multiplicative model is appropriate) these trend estimates by the corresponding seasonal relative (e.g., multiply the November trend estimate by the November seasonal relative, multiply the December trend estimate by the December seasonal relative, and so on).

Example 4 illustrates these two techniques.

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Computing Seasonal Relatives A widely used method for computing seasonal relatives involves the use of a centered moving average . This approach effectively accounts for any trend (linear or curvilinear) that might be present in the data. For example, Figure 3.7 illustrates how a three-period centered moving average closely tracks the data originally shown in Figure 3.3.

Manual computation of seasonal relatives using the centered moving average method is a bit cumbersome, so the use of software is recommended. Manual computation is illustrated in Solved Problem 4 at the end of the chapter. The Excel template (on the website) is a simple and convenient way to obtain values of seasonal relatives (indexes). Example 5 illustrates this approach.

For practical purposes, you can round the relatives to two decimal places. Thus, the seasonal (standard) index values are:

Computing Seasonal Relatives Using the Simple Average Method The simple average (SA) method is an alternative way to compute seasonal relatives. Each seasonal relative is the average for that season divided by the average of all seasons. This method is illustrated in Example 5, where the seasons are days. Note that there is no need to standardize the relatives when using the SA method.

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The obvious advantage of the SA method compared to the centered MA method is the simplicity of computations. When the data have a stationary mean (i.e., variation around an average), the SA method works quite well, providing values of relatives that are quite close to those obtained using the centered MA method, which is generally accepted as accurate. Conventional wisdom is that the SA method should not be used when linear trend is present in the data. However, it can be used to obtain fairly good values of seasonal relatives as long as the ratio of the intercept to the slope is large, or when variations are large relative to the slope, shown as follows. Also, the larger the ratio, the smaller the error. The general relationship is illustrated in the following figure.

Techniques for Cycles

Cycles are up-and-down movements similar to seasonal variations but of longer duration—say, two to six years between peaks. When cycles occur in time-series data, their frequent irregularity makes it difficult or impossible to project them from past data because turning points are difficult to identify. A short moving average or a naive approach may be of some value, although both will produce forecasts that lag cyclical movements by one or several periods.

The most commonly used approach is explanatory: Search for another variable that relates to, and leads, the variable of interest. For example, the number of housing starts (i.e., permits to build houses) in a given month often is an indicator of demand a few months later for products and services directly tied to construction of new homes (landscaping; sales of washers and dryers, carpeting, and furniture; new demands for shopping, transportation, schools). Thus, if an organization is able to establish a high correlation with such a leading variable (i.e., changes in the variable precede changes in the variable of interest), it can develop an equation that describes the relationship, enabling forecasts to be made. It is important that a persistent relationship exists between the two variables. Moreover, the higher the correlation, the better the chances that the forecast will be on target.

Simple Linear Regression

The simplest and most widely used form of regression involves a linear relationship between two variables. A plot of the values might appear like that in Figure 3.8. The object in linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical page 99deviations of data points from the line (i.e., the least squares criterion). This least squares line has the equation

where

( Note: It is conventional to represent values of the predicted variable on the y axis and values of the predictor variable on the x axis.) Figure 3.9 is a general graph of a linear regression line.

The coefficients a and b of the line are based on the following two equations:

where

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TABLE 3.3

Using the Excel template for linear regression

Source: Microsoft

One indication of how accurate a prediction might be for a linear regression line is the amount of scatter of the data points around the line. If the data points tend to be relatively close to the line, predictions using the linear equation will tend to be more accurate than if the data points are widely scattered. The scatter can be summarized using the standard error of estimate . It can be computed by finding the vertical difference between each data point and the page 101computed value of the regression equation for that value of x, squaring each difference, adding the squared differences, dividing by n − 2, and then finding the square root of that value.

where

For the data given in Table 3.3, the error column shows the y − y _c differences. Squaring each error and summing the squares yields .01659. Hence, the standard error of estimate is

One application of regression in forecasting relates to the use of indicators. These are uncontrollable variables that tend to lead or precede changes in a variable of interest. For example, changes in the Federal Reserve Board’s discount rate may influence certain business activities. Similarly, an increase in energy costs can lead to price increases for a wide range of products and services. Careful identification and analysis of indicators may yield insight into possible future demand in some situations. There are numerous published indexes and websites from which to choose. ² These include:

Net change in inventories on hand and on order

Interest rates for commercial loans

Industrial output

Consumer price index (CPI)

The wholesale price index

Stock market prices

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Other potential indicators are population shifts, local political climates, and activities of other firms (e.g., the opening of a shopping center may result in increased sales for nearby businesses). Three conditions are required for an indicator to be valid:

1. The relationship between movements of an indicator and movements of the variable should have a logical explanation.

2. Movements of the indicator must precede movements of the dependent variable by enough time so that the forecast isn’t outdated before it can be acted upon.

3. A fairly high correlation should exist between the two variables.

Correlation measures the strength and direction of relationship between two variables. Correlation can range from −1.00 to +1.00. A correlation of +1.00 indicates that changes in one variable are always matched by changes in the other; a correlation of −1.00 indicates that increases in one variable are matched by decreases in the other; and a correlation close to zero indicates little linear relationship between two variables. The correlation between two variables can be computed using the equation

The square of the correlation coefficient, r ², provides a measure of the percentage of variability in the values of y that is “explained” by the independent variable. The possible values of r ² range from 0 to 1.00. The closer r ² is to 1.00, the greater the percentage of explained variation. A high value of r ², say .80 or more, would indicate that the independent variable is a good predictor of values of the dependent variable. A low value, say .25 or less, would indicate a poor predictor, and a value between .25 and .80 would indicate a moderate predictor.

Comments on the Use of Linear Regression Analysis

Use of simple regression analysis implies that certain assumptions have been satisfied. Basically, these are as follows:

• Variations around the line are random. If they are random, no patterns such as cycles or trends should be apparent when the line and data are plotted.

• Deviations around the average value (i.e., the line) should be normally distributed. A concentration of values close to the line with a small proportion of larger deviations supports the assumption of normality.

• Predictions are being made only within the range of observed values.

If the assumptions are satisfied, regression analysis can be a powerful tool. To obtain the best results, observe the following:

• Always plot the data to verify that a linear relationship is appropriate.

• The data may be time-dependent. Check this by plotting the dependent variable versus time; if patterns appear, use analysis of time series instead of regression, or use time as an independent variable as part of a multiple regression analysis.

• A small correlation may imply that other variables are important.

In addition, note these weaknesses of regression:

• Simple linear regression applies only to linear relationships with one independent variable.

• One needs a considerable amount of data to establish the relationship—in practice, 20 or more observations.

• All observations are weighted equally.

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Nonlinear and Multiple Regression Analysis

Simple linear regression may prove inadequate to handle certain problems because a linear model is inappropriate or because more than one predictor variable is involved. When nonlinear relationships are present, you should employ nonlinear regression; models that involve more than one predictor require the use of multiple regression analysis. While these analyses are beyond the scope of this text, you should be aware that they are often used. Multiple regression forecasting substantially increases data requirements.

Summarizing Forecast Accuracy

Forecast accuracy is a significant factor when deciding among forecasting alternatives. Accuracy is based on the historical error performance of a forecast.

Three commonly used measures for summarizing historical errors are the mean absolute deviation (MAD) , the mean squared error (MSE) , and the mean absolute percent error (MAPE) . MAD is the average absolute error, MSE is the average of squared errors, and MAPE is the average absolute percent error. The formulas used to compute MAD, ³ MSE, and MAPE are as follows:

Example 9 illustrates the computation of MAD, MSE, and MAPE.

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From a computational standpoint, the difference between these measures is that MAD weights all errors evenly, MSE weights errors according to their squared values, and MAPE weights according to relative error.

One use for these measures is to compare the accuracy of alternative forecasting methods. For instance, a manager could compare the results to determine which one yields the lowest MAD, MSE, or MAPE for a given set of data. Another use is to track error performance over time to decide if attention is needed. Is error performance getting better or worse, or is it staying about the same?

In some instances, historical error performance is secondary to the ability of a forecast to respond to changes in data patterns. Choice among alternative methods would then focus on the cost of not responding quickly to a change relative to the cost of responding to changes that are not really there (i.e., random fluctuations).

Overall, the operations manager must settle on the relative importance of historical performance versus responsiveness and whether to use MAD, MSE, or MAPE to measure historical performance. MAD is the easiest to compute, but weights errors linearly. MSE squares errors, thereby giving more weight to larger errors, which typically cause more problems. MAPE should be used when there is a need to put errors in perspective. For example, an error of 10 in a forecast of 15 is huge. Conversely, an error of 10 in a forecast of 10,000 is insignificant. Hence, to put large errors in perspective, MAPE would be used. Another use of MAPE is when there is a need to compare forecast errors for different products or services. One example would be forecasts for store brands versus national brands.

What Does Product and Service Design Do?

The primary focus of product or service design should be on customer satisfaction. The various activities and responsibilities of product and service design include the following (functional interactions are shown in parentheses):

1. Translate customer wants and needs into product and service requirements (marketing, operations)

2. Refine existing products and services (marketing)

3. Develop new products and/or services (marketing, operations)

4. Formulate quality goals (marketing, operations)

5. Formulate cost targets (accounting, finance, operations)

6. Construct and test prototypes (operations, marketing, engineering)

7. Document specifications

8. Translate product and service specifications into process specifications (engineering, operations)

Product and service design involves or affects nearly every functional area of an organization. However, marketing and operations have major involvement.

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Objectives of Product and Service Design

Primary consideration: Customer satisfaction.

Secondary considerations: Cost or profit, quality, ability to produce a product or provide a service, ethics/safety, and sustainability.

Key Questions

From a buyer’s standpoint, most purchasing decisions entail two fundamental considerations; one is cost and the other is quality or performance. From the organization’s standpoint, the key questions are:

1. Is there demand for it? What is the potential size of the market, and what is the expected demand profile (will demand be long term or short term, will it grow slowly or quickly)?

2. Can we do it? Do we have the necessary knowledge, skills, equipment, capacity, and supply chain capability? For products, this is known as manufacturability ; for services, this is known as serviceability . Also, is outsourcing some or all of the work an option?

1. What level of quality is appropriate? What do customers expect? What level of quality do competitors provide for similar items? How would it fit with our current offerings?

2. Does it make sense from an economic standpoint? What are the potential liability issues, ethical considerations, sustainability issues, costs, and profits? For nonprofits, is the cost within budget?

Reasons for Product and Service Design or Redesign

Product and service design typically has strategic implications for the success and prosperity of an organization. Consequently, decisions in this area are some of the most fundamental that managers must make. Product and service design or redesign should be closely tied to an organization’s strategy.

Organizations become involved in product and service design or redesign for a variety of reasons. The main forces that initiate design or redesign are market opportunities and threats. The factors that give rise to market opportunities and threats can be one or more changes:

• Economic (e.g., low demand, excessive warranty claims, the need to reduce costs)

• Social and demographic (e.g., aging baby boomers, population shifts)

• Political, liability, or legal (e.g., government changes, safety issues, new regulations)

• Competitive (e.g., new or changed products or services, new advertising/promotions)

• Cost or availability (e.g., of raw materials, components, labor, water, energy)

• Technological (e.g., in product components, processes)

While each of these factors may seem obvious, let’s reflect a bit on technological changes, which can create a need for product or service design changes in several different ways. An obvious way is new technology that can be used directly in a product or service (e.g., a faster, smaller microprocessor that spawns a new generation of smartphones). Technology also can indirectly affect product and service design: Advances in processing technology may require altering an existing design to make it compatible with the new processing technology. Still another way that technology can impact product design is illustrated by digital recording technology that allows television viewers to skip commercials when they view a recorded program. This means that advertisers (who support a television program) can’t get their message to viewers. To overcome this, some advertisers have adopted a strategy of making their products an integral part of a television program, say by having their products prominently displayed and/or mentioned by the actors as a way to call viewers’ attention to their products without the need for commercials.

The following reading suggests another potential benefit of product redesign.

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Cradle-to-Grave Assessment

Cradle-to-grave assessment , also known as life cycle analysis, is the assessment of the environmental impact of a product or service throughout its useful life, focusing on such factors as global warming (the amount of carbon dioxide released into the atmosphere), smog formation, oxygen depletion, and solid waste generation. For products, cradle-to-grave analysis takes into account impacts in every phase of a product’s life cycle, from raw material extraction from the earth, or the growing and harvesting of plant materials, through fabrication of parts and assembly operations, page 147or other processes used to create products, as well as the use or consumption of the product, and final disposal at the end of a product’s useful life. It also considers energy consumption, pollution and waste, and transportation in all phases. Although services generally involve less use of materials, cradle-to-grave assessment of services is nonetheless important, because services consume energy and involve many of the same or similar processes that products involve.

The goal of cradle-to-grave assessment is to choose products and services that have the least environmental impact, while still taking into account economic considerations. The procedures of cradle-to-grave assessment are part of the ISO 14000 environmental management standards, which are discussed in Chapter 9.

End-of-Life Programs

End-of-life (EOL) programs deal with products that have reached the end of their useful lives. The products include both consumer products and business equipment. The purpose of these programs is to reduce the dumping of products, particularly electronic equipment, in landfills or third-world countries, as has been the common practice, or incineration, which converts materials into hazardous air and water emissions and generates toxic ash. Although the programs are not limited to electronic equipment, that equipment poses problems because it typically contains toxic materials such as lead, cadmium, chromium, and other heavy metals. IBM provides a good example of the potential of EOL programs. Over the last 15 years, it has collected about 2 billion pounds of product and product waste.

The Three Rs: Reduce, Reuse, and Recycle

Designers often reflect on three particular aspects of potential cost savings and reducing environmental impact: reducing the use of materials through value analysis; refurbishing and then reselling returned goods that are deemed to have additional useful life, which is referred to as remanufacturing; and reclaiming parts of unusable products for recycling.

Reduce: Value Analysis

Value analysis refers to an examination of the function of parts and materials in an effort to reduce the cost and/or improve the performance of a product. Typical questions that would be asked as part of the analysis include: Could a cheaper part or material be used? Is the function necessary? Can the function of two or more parts or components be performed by a single part for a lower cost? Can a part be simplified? Could product specifications be relaxed, and would this result in a lower price? Could standard parts be substituted for nonstandard parts? Table 4.1 provides a checklist of questions that can guide a value analysis.

TABLE 4.1

Overview of value analysis

The following reading describes how Kraft Foods is working to reduce water and energy use, CO ₂ and plant waste, and packaging.

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Reuse: Remanufacturing

An emerging concept in manufacturing is the remanufacturing of products. Remanufacturing refers to refurbishing used products by replacing worn-out or defective components, and reselling the products. This can be done by the original manufacturer, or another company. Among the products that have remanufactured components are automobiles, printers, copiers, cameras, computers, and telephones.

There are a number of important reasons for doing this. One is that a remanufactured product can be sold for about 50 percent of the cost of a new product. Another is that the process requires mostly unskilled and semiskilled workers. Also, in the global market, European lawmakers are increasingly requiring manufacturers to take back used products, because this means fewer products end up in landfills and there is less depletion of natural resources, such as raw materials and fuel.

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Designing products so they can be more easily taken apart has given rise to yet another design consideration: Design for disassembly (DFD) .

Recycle

Recycling is sometimes an important consideration for designers. Recycling means recovering materials for future use. This applies not only to manufactured parts but also to materials used during production, such as lubricants and solvents. Reclaimed metal or plastic parts may be melted down and used to make different products. (See readings above and on next page.)

Companies recycle for a variety of reasons, including

1. Cost savings

2. Environment concerns

3. Environmental regulations

An interesting note: Companies that want to do business in the European Union must show that a specified proportion of their products are recyclable.

The pressure to recycle has given rise to the term design for recycling (DFR) , referring to product design that takes into account the ability to disassemble a used product to recover the recyclable parts.

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Strategies for Product or Service Life Stages

Most, but not all, products and services go through a series of stages over their useful life, sometimes referred to as their life cycle, as shown in Figure 4.1. Demand typically varies by phase. Different phases call for different strategies. In every phase, forecasts of demand and cash flow are key inputs for strategy.

When a product or service is introduced, it may be treated as a curiosity item. Many potential buyers may suspect that all the bugs haven’t been worked out and that the price may drop after the introductory period. Strategically, companies must carefully weigh the trade-offs in getting all the bugs out versus getting a leap on the competition, as well as getting to market at an advantageous time. For example, introducing new high-tech products or features during peak back-to-school buying periods or holiday buying periods can be highly desirable.

It is important to have a reasonable forecast of initial demand so an adequate supply of product or an adequate service capacity is in place.

Over time, design improvements and increasing demand yield higher reliability and lower costs, leading the growth in demand. In the growth phase, it is important to obtain accurate projections of the demand growth rate and how long that will persist, and then to ensure that capacity increases coincide with increasing demand.

In the next phase, the product or service reaches maturity, and demand levels off. Few, if any, design changes are needed. Generally, costs are low and productivity is high. New uses for products or services can extend their life and increase the market size. Examples include baking soda, duct tape, and vinegar. The maker of LEGOs has found a way to grow its market, as described in the following reading.

In the decline phase, decisions must be made about whether to discontinue a product or service and replace it with new ones or abandon the market, or to attempt to find new uses or new users for the existing product or service. For example, duct tape and baking page 152soda are two products that have been employed well beyond their original uses of taping heating and cooling ducts and cooking. The advantages of keeping existing products or services can be tremendous. The same workers can produce the product or provide the service using much of the same equipment, the same supply chain, and perhaps the same distribution channels. Consequently, costs tend to be very low, and additional resource needs and training needs are low.

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Some products do not exhibit life cycles: wooden pencils; paper clips; nails; knives, forks, and spoons; drinking glasses; and similar items. However, most new products do.

Some service life cycles are related to the life cycles of products. For example, as older products are phased out, services such as installation and repair of the older products also phase out.

Wide variations exist in the amount of time a particular product or service takes to pass through a given phase of its life cycle: Some pass through various stages in a relatively short period; others take considerably longer. Often, it is a matter of the basic need for the item and the rate of technological change. Some toys, novelty items, and style items have a life cycle of less than one year, whereas other, more useful items, such as clothes washers and dryers, may last for many years before yielding to technological change.

Product Life Cycle Management

Product life cycle management (PLM) is a systematic approach to managing the series of changes a product goes through, from its conception, design, and development, through production and any redesign, to its end of life. PLM incorporates everything related to a particular product. That includes data pertaining to production processes, business processes, people, and anything else related to the product.

PLM software can be used to automate the management of product-related data and integrate the data with other business processes, such as enterprise resource planning (discussed in Chapter 12). A goal of PLM is to eliminate waste and improve efficiency. For example, PLM is considered to be an integral part of lean production (discussed in Chapter 14).

There are three phases of PLM application:

• Beginning of life, which involves design and development;

• Middle of life, which involves working with suppliers, managing product information and warranties; and

• End of life, which involves strategies for product discontinuance, disposal, or recycling.

Although PLM is generally associated with manufacturing, the same management structure can be applied to software development and services.

Degree of Standardization

An important issue that often arises in both product/service design and process design is the degree of standardization. Standardization refers to the extent to which there is absence of variety in a product, service, or process. Standardized products are made in large quantities of identical items; calculators, computers, and 2 percent milk are examples. Standardized service implies that every customer or item processed receives essentially the same service. An automatic car wash is a good example: Each car, regardless of how clean or dirty it is, receives the same service. Standardized processes deliver standardized service or produce standardized goods.

Standardization carries a number of important benefits, as well as certain disadvantages. Standardized products are immediately available to customers. Standardized products mean interchangeable parts, which greatly lower the cost of production while increasing productivity and making replacement or repair relatively easy compared with that of customized parts. Design costs are generally lower. For example, automobile producers standardize key components of automobiles across product lines; components such as brakes, electrical systems, and other “under-the-skin” parts would be the same for all car models. By reducing variety, companies save time and money while increasing the quality and reliability of their products.

Another benefit of standardization is reduced time and cost to train employees and reduced time to design jobs. Similarly, the scheduling of work, inventory handling, and purchasing and accounting activities become much more routine, and quality is more consistent.

Lack of standardization can at times lead to serious difficulties and competitive struggles. For example, the use of the English system of measurement by U.S. manufacturers, while most of the rest of the world’s manufacturers use the metric system, has led to problems page 154in selling U.S. goods in foreign countries and in buying foreign machines for use in the United States. This may make it more difficult for U.S. firms to compete in the European Union.

Standardization also has disadvantages. A major one relates to the reduction in variety. This can limit the range of customers to whom a product or service appeals. And that creates a risk that a competitor will introduce a better product or greater variety and realize a competitive advantage. Another disadvantage is that a manufacturer may freeze (standardize) a design prematurely and, once the design is frozen, find compelling reasons to resist modification.

Obviously, designers must consider important issues related to standardization when making choices. The major advantages and disadvantages of standardization are summarized in Table 4.2.

TABLE 4.2

Advantages and disadvantages of standardization

Designing for Mass Customization

Companies like standardization because it enables them to produce high volumes of relatively low-cost products, albeit products with little variety. Customers, on the other hand, typically prefer more variety, although they like the low cost. The question for producers is how to resolve these issues without (1) losing the benefits of standardization, and (2) incurring a host of problems that are often linked to variety. These include increasing the resources needed to achieve design variety; increasing variety in the production process, which would add to the skills necessary to produce products, causing a decrease in productivity; creating an additional inventory burden during and after production, by having to carry replacement parts for the increased variety of parts; and adding to the difficulty of diagnosing and repairing product failures. The answer, at least for some companies, is mass customization , a strategy of producing standardized goods or services, but incorporating some degree of customization in the final product or service. Several tactics make this possible. One is delayed differentiation, and another is modular design. (See reading on following page.)

Delayed differentiation is a postponement tactic: the process of producing, but not quite completing, a product or service, postponing completion until customer preferences or specifications are known. There are a number of variations of this. In the case of goods, almost-finished units might be held in inventory until customer orders are received, at which time customized features are incorporated, according to customer requests. For example, furniture makers can produce dining room sets, but not apply stain, allowing customers a choice of stains. Once the choice is made, the stain can be applied in a relatively short time, thus eliminating a long wait for customers, giving the seller a competitive advantage. Similarly, various e-mail or internet services can be delivered to customers as standardized packages, which can then be modified according to the customer’s preferences. HP printers that are made in the United States but intended for foreign markets are mostly completed in domestic assembly plants and then finalized closer to the country of use. The result of delayed differentiation is a product or service with customized features that can be quickly produced, appealing to the customers’ desire for variety and speed of delivery, and yet one that for the most part is standardized, enabling the producer to realize the benefits of standardized production. This technique is not new. Manufacturers of men’s clothing, for example, produce suits with pants that have legs that are unfinished, allowing customers to tailor choices as to the exact length and whether to have cuffs or no cuffs. What is new is the extent to which business organizations are finding ways to incorporate this concept into a broad range of products and services.

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Modular design is a form of standardization. Modules represent groupings of component parts into subassemblies, usually to the point where the individual parts lose their separate identity. One familiar example of modular design is computers, which have modular parts that can be replaced if they become defective. By arranging modules in different configurations, different computer capabilities can be obtained. For mass customization, modular design enables producers to quickly assemble products with modules to achieve a customized configuration for an individual customer, avoiding the long customer wait that would occur if individual parts had to be assembled. Dell Computers has successfully used this concept to become a dominant force in the PC industry by offering consumers the opportunity to configure modules according to their own specifications. Many other computer manufacturers now use a similar approach. Modular design also is found in the construction industry. One firm in Rochester, New York, makes prefabricated motel rooms complete with wiring, plumbing, and even room decorations in its factory and then moves the complete rooms by rail to the construction site, where they are integrated into the structure.

One advantage of modular design of equipment compared with nonmodular design is that failures are often easier to diagnose and remedy because there are fewer pieces to investigate. Similar advantages are found in the ease of repair and replacement; the faulty module is page 156conveniently removed and replaced with a good one. The manufacture and assembly of modules generally involve simplifications: Fewer parts are involved, so purchasing and inventory control become more routine, fabrication and assembly operations become more standardized, and training costs often are relatively low.

The main disadvantages of modular design stem from the decrease in variety: The number of possible configurations of modules is much less than the number of possible configurations based on individual components. Another disadvantage that is sometimes encountered is the inability to disassemble a module in order to replace a faulty part; the entire module must be scrapped—usually at a higher cost.

Reliability

Reliability is a measure of the ability of a product, a part, a service, or an entire system to perform its intended function under a prescribed set of conditions. The importance of reliability is underscored by its use by prospective buyers in comparing alternatives, and by sellers as one determinant of price. Reliability also can have an impact on repeat sales, reflect on the product’s image, and, if it is too low, create legal implications. Reliability is also a consideration for sustainability: The higher the reliability of a product, the fewer the resources that will be needed to maintain it, and the less frequently it will involve the three Rs.

The term failure is used to describe a situation in which an item does not perform as intended. This includes not only instances in which the item does not function at all, but also instances in which the item’s performance is substandard or it functions in a way not intended. For example, a smoke alarm might fail to respond to the presence of smoke (not operate at all), it might sound an alarm that is too faint to provide an adequate warning (substandard performance), or it might sound an alarm even though no smoke is present (unintended response).

Reliabilities are always specified with respect to certain conditions, called normal operating conditions . These can include load, temperature, and humidity ranges, as well as operating procedures and maintenance schedules. Failure of users to heed these conditions often results in premature failure of parts or complete systems. For example, using a passenger car to tow heavy loads will cause excess wear and tear on the drive train; driving over potholes or curbs often results in untimely tire failure; and using a calculator to drive nails might have a marked impact on its usefulness for performing mathematical operations.

Improving Reliability Reliability can be improved in a number of ways, some of which are listed in Table 4.3.

TABLE 4.3

Potential ways to improve reliability

Because overall system reliability is a function of the reliability of individual components, improvements in their reliability can increase system reliability. Unfortunately, inadequate production or assembly procedures can negate even the best of designs, and this is often a source of failures. System reliability can be increased by the use of backup components. Failures in actual use often can be reduced by upgrading user education and refining maintenance recommendations or procedures. Finally, it may be possible to increase the overall reliability of the system by simplifying the system (thereby reducing the number of components that could cause the system to fail) or altering component relationships (e.g., increasing the reliability of interfaces).

A fundamental question concerning improving reliability is: How much reliability is needed? Obviously, the reliability needed for a household light bulb isn’t in the same category page 157as the reliability needed for an airplane. So the answer to the question depends on the potential benefits of improvements and on the cost of those improvements. Generally speaking, reliability improvements become increasingly costly. Thus, although benefits initially may increase at a much faster rate than costs, the opposite eventually becomes true. The optimal level of reliability is the point where the incremental benefit received equals the incremental cost of obtaining it. In the short term, this trade-off is made in the context of relatively fixed parameters (e.g., costs). However, in the longer term, efforts to improve reliability and reduce costs can lead to higher optimal levels of reliability.

Robust Design

Some products or services will function as designed only within a narrow range of conditions, while others will perform as designed over a much broader range of conditions. The latter have robust design . Consider a pair of fine leather boots—obviously not made for trekking through mud or snow. Now consider a pair of heavy rubber boots—just the thing for mud or snow. The rubber boots have a design that is more robust than that of the fine leather boots.

The more robust a product or service, the less likely it will fail due to a change in the environment in which it is used or in which it is performed. Hence, the more designers can build robustness into the product or service, the better it should hold up, resulting in a higher level of customer satisfaction.

A similar argument can be made for robust design as it pertains to the production process. Environmental factors can have a negative effect on the quality of a product or service. The more resistant a design is to those influences, the less likely is a negative effect. For example, many products go through a heating process: food products, ceramics, steel, petroleum products, and pharmaceutical products. Furnaces often do not heat uniformly; heat may vary either by position in an oven or over an extended period of production. One approach to this problem might be to develop a superior oven; another might be to design a system that moves the product during heating to achieve uniformity. A robust-design approach would develop a product that is unaffected by minor variations in temperature during processing.

Taguchi’s Approach Japanese engineer Genichi Taguchi’s approach is based on the concept of robust design. His premise is that it is often easier to design a product that is insensitive to environmental factors, either in manufacturing or in use, than to control the environmental factors.

The central feature of Taguchi’s approach—and the feature used most often by U.S. companies—is parameter design. This involves determining the specification settings for both the product and the process that will result in robust design in terms of manufacturing variations, product deterioration, and conditions during use.

The Taguchi approach modifies the conventional statistical methods of experimental design. Consider this example. Suppose a company will use 12 chemicals in a new product it intends to produce. There are two suppliers for these chemicals, but the chemical concentrations vary slightly between the two suppliers. Classical design of experiments would require 2 ¹² = 4,096 test runs to determine which combination of chemicals would be optimum. Taguchi’s approach would involve only testing a portion of the possible combinations. Relying on experts to identify the variables that would be most likely to affect important performance, the number of combinations would be dramatically reduced, perhaps to, say, 32. Identifying the best combination in the smaller sample might be a near-optimal combination instead of the optimal combination. The value of this approach is its ability to achieve major advances in product or process design fairly quickly, using a relatively small number of experiments.

Critics charge that Taguchi’s methods are inefficient and incorrect, and often lead to non-optimal solutions. Nonetheless, his methods are widely used and have been credited with helping to achieve major improvements in U.S. products and manufacturing processes.

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Degree of Newness

Product or service design change can range from the modification of an existing product or service to an entirely new product or service:

1. Modification of an existing product or service

2. Expansion of an existing product line or service offering

3. Clone of a competitor’s product or service

4. New product or service

The degree of change affects the newness to the organization and the newness to the market. For the organization, a low level of newness can mean a fairly quick and easy transition to producing the new product, while a high level of newness would likely mean a slower and more difficult, and therefore more costly, transition. For the market, a low level of newness would mean little difficulty with market acceptance, but possibly low profit potential. Even in instances of low profit potential, organizations might use this strategy to maintain market share. A high level of newness, on the other hand, might mean more difficulty with acceptance, or it might mean a rapid gain in market share with a high potential for profits. Unfortunately, there is no way around these issues. It is important to carefully assess the risks and potential benefits of any design change, taking into account clearly identified customer wants.

Quality Function Deployment

Obtaining input from customers is essential to assure that they will want what is offered for sale. Although obtaining input can be informal through discussions with customers, there is a formal way to document customer wants. Quality function deployment (QFD) is a structured approach for integrating the “voice of the customer” into both the product and service development process. The purpose is to ensure that customer requirements are factored into every aspect of the process. Listening to and understanding the customer is the central feature of QFD. Requirements often take the form of a general statement such as, “It should be easy to adjust the cutting height of the lawn mower.” Once the requirements are known, they must be translated into technical terms related to the product or service. For example, a statement about changing the height of the lawn mower may relate to the mechanism used to accomplish that, its position, instructions for use, tightness of the spring that controls the mechanism, or materials needed. For manufacturing purposes, these must be related to the materials, dimensions, and equipment used for processing.

The structure of QFD is based on a set of matrices. The main matrix relates customer requirements (what) and their corresponding technical requirements (how). This matrix is illustrated in Figure 4.2. The matrix provides a structure for data collection.

Source: Ernst and Young Consulting Group, Total Quality (Homewood, IL: Dow-Jones Irwin, 1991), p. 121.

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Additional features are usually added to the basic matrix to broaden the scope of analysis. Typical additional features include importance weightings and competitive evaluations. A correlational matrix is usually constructed for technical requirements; this can reveal conflicting technical requirements. With these additional features, the set of matrices has the form illustrated in Figure 4.3. It is often referred to as the house of quality because of its house-like appearance.

An analysis using this format is shown in Figure 4.4. The data relate to a commercial printer (customer) and the company that supplies the paper. At first glance, the display appears complex. It contains a considerable amount of information for product and process planning. Therefore, let’s break it up into separate parts and consider them one at a time. To start, a key part is the list of customer requirements on the left side of the figure. Next, note the technical requirements, listed vertically near the top. The key relationships and their degree of importance are shown in the center of the figure. The circle with a dot inside indicates the strongest positive relationship; that is, it denotes the most important technical requirements for satisfying customer requirements. Now look at the “importance to customer” numbers that are shown next to each customer requirement (3 is the most important). Designers will take into account the importance values and the strength of correlation in determining where to focus the greatest effort.

Next, consider the correlation matrix at the top of the “house.” Of special interest is the strong negative correlation between “paper thickness” and “roll roundness.” Designers will have to find some way to overcome that or make a trade-off decision.

On the right side of the figure is a competitive evaluation comparing the supplier’s performance on the customer requirements with each of the two key competitors (A and B). For example, the supplier (X) is worst on the first customer requirement and best on the third customer requirement. The line connects the X performances. Ideally, design will cause all of the Xs to be in the highest positions.

Across the bottom of Figure 4.4 are importance weightings, target values, and technical evaluations. The technical evaluations can be interpreted in a manner similar to that of the competitive evaluations (note the line connecting the Xs). The target values typically contain technical specifications, which we will not discuss. The importance weightings are the sums of values assigned to the relationships (see the lower right-hand key for relationship weights). The 3 in the first column is the product of the importance to the customer, 3, and the small (Δ) weight, 1. The importance weightings and target evaluations help designers focus on desired results. In this example, the first technical requirement has the lowest importance weighting, while the next four technical requirements all have relatively high importance weightings.

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The house of quality approach involves a sequence of “houses,” beginning with design characteristics, which leads to specific components, then production processes, and finally, a quality plan. The sequence is illustrated in Figure 4.5. Although the details of each house are beyond the scope of this text, Figure 4.5 provides a conceptual understanding of the progression involved.

The Kano Model

The Kano model is a theory of product and service design developed by Dr. Noriaki Kano, a Japanese professor, who offered a perspective on customer perceptions of quality different from the traditional view that “more is better.” Instead, he proposed different categories of quality and posited that understanding them would better position designers to assess and address quality needs. His model provides insights into the attributes that are perceived to be page 161important to customers. The model employs three definitions of quality: basic, performance, and excitement.

Basic quality refers to customer requirements that have only a limited effect on customer satisfaction if present, but lead to dissatisfaction if not present. For example, putting a very short cord on an electrical appliance will likely result in customer dissatisfaction, but beyond a certain length (e.g., 4 feet), adding more cord will not lead to increased levels of customer satisfaction. Performance quality refers to customer requirements that generate satisfaction or dissatisfaction in proportion to their level of functionality and appeal. For example, increasing the tread life of a tire or the amount of time house paint will last will add to customer satisfaction. Excitement quality refers to a feature or attribute that was unexpected by the customer and causes excitement (the “wow” factor), such as a voucher for dinner for two at the hotel restaurant when checking in. Figure 4.6A portrays how the three definitions of quality influence customer satisfaction or dissatisfaction relative to the degree of implementation. Note that features that are perceived by customers as basic quality result in dissatisfaction if they are missing or at low levels, but do not result in customer satisfaction if they are present, even at high levels. Performance factors can result in satisfaction or dissatisfaction, depending on the degree to which they are present. Excitement factors, because they are unexpected, do not result in dissatisfaction when they are absent or at low levels, but have the potential for disproportionate levels of satisfaction if they are present.

Over time, features that excited become performance features, and performance features soon become basic quality features, as illustrated in Figure 4.6B. The rates at which various design elements are migrating is an important input from marketing that will enable designers to continue to satisfy and delight customers and not waste efforts on improving what have become basic quality features.

The lesson of the Kano model is that design elements that fall into each aspect of quality must first be determined. Once basic needs have been met, additional efforts in those areas should not be pursued. For performance features, cost–benefit analysis comes into play, and these features should be included as long as the benefit exceeds the cost. Excitement features pose somewhat of a challenge. Customers are not likely to indicate excitement factors in surveys because they don’t know that they want them. However, small increases in such factors produce disproportional increases in customer satisfaction and generally increase brand loyalty, so it is important for companies to strive to identify and include these features when economically feasible.

The Kano model can be used in conjunction with QFD, as well as in Six Sigma projects (see Chapter 9 for a discussion of Six Sigma).

Concurrent Engineering

To achieve a smoother transition from product design to production, and to decrease product development time, many companies are using simultaneous development, or concurrent engineering. In its narrowest sense, concurrent engineering means bringing design and manufacturing engineering people together early in the design phase to simultaneously develop the product and the processes for creating the product. More recently, this concept has been enlarged to include manufacturing personnel (e.g., materials specialists) and marketing and purchasing personnel in loosely integrated, cross-functional teams. In addition, the views of suppliers and customers are frequently sought. The purpose, of course, is to achieve product designs that reflect customer wants, as well as manufacturing capabilities.

Traditionally, designers developed a new product without any input from manufacturing, and then turned over the design to manufacturing, which would then have to develop a process for making the new product. This “over-the-wall” approach created tremendous challenges for manufacturing, generating numerous conflicts and greatly increasing the time needed to successfully produce a new product. It also contributed to an “us versus them” mentality.

For these and similar reasons, the simultaneous development approach has great appeal. Among the key advantages of this approach are the following:

1. Manufacturing personnel are able to identify production capabilities and capacities. Very often, they have some latitude in design in terms of selecting suitable materials and processes. Knowledge of production capabilities can help in the selection process. In addition, cost and quality considerations can be greatly influenced by design, and conflicts during production can be greatly reduced.

2. Design or procurement of critical tooling, some of which might have long lead times, can occur early in the process. This can result in a major shortening of the product development process, which could be a key competitive advantage.

3. The technical feasibility of a particular design or a portion of a design can be assessed early on. Again, this can avoid serious problems during production.

4. The emphasis can be on problem resolution instead of conflict resolution.

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However, despite the advantages of concurrent engineering, a number of potential difficulties exist in this co-development approach. Two key ones are the following:

• Long-standing boundaries between design and manufacturing can be difficult to overcome. Simply bringing a group of people together and thinking they will be able to work together effectively is probably naive.

• There must be extra communication and flexibility if the process is to work, and these can be difficult to achieve.

Hence, managers should plan to devote special attention if this approach is to work.

Computer-Aided Design (CAD)

Computers are increasingly used for product design. Computer-aided design (CAD) uses computer graphics for product design. The designer can modify an existing design or create a new one on a monitor by means of a light pen, a keyboard, a joystick, or a similar device. Once the design is entered into the computer, the designer can maneuver it on the screen: It can be rotated to provide the designer with different perspectives, it can be split apart to give the designer a view of the inside, and a portion of it can be enlarged for closer examination. The designer can obtain a printed version of the completed design and file it electronically, making it accessible to people in the firm who need this information (e.g., marketing, operations).

A growing number of products are being designed in this way, including transformers, automobile parts, aircraft parts, integrated circuits, and electric motors.

A major benefit of CAD is the increased productivity of designers. No longer is it necessary to laboriously prepare mechanical drawings of products or parts and revise them repeatedly to correct errors or incorporate revisions. A rough estimate is that CAD increases the productivity of designers from 3 to 10 times. A second major benefit of CAD is the creation of a database for manufacturing that can supply needed information on product geometry and dimensions, tolerances, material specifications, and so on. It should be noted, however, that CAD needs this database to function and that this entails a considerable amount of effort.

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Some CAD systems allow the designer to perform engineering and cost analyses on proposed designs. For instance, the computer can determine the weight and volume of a part and do stress analysis as well. When there are a number of alternative designs, the computer can quickly go through the possibilities and identify the best one, given the designer’s criteria. CAD that includes finite element analysis (FEA) capability can greatly shorten the time to market of new products. It enables developers to perform simulations that aid in the design, analysis, and commercialization of new products. Designers in industries such as aeronautics, biomechanics, and automotives use FEA.

Production Requirements

As noted earlier in the chapter, designers must take into account production capabilities. Design needs to clearly understand the capabilities of production (e.g., equipment, skills, types of materials, schedules, technologies, special abilities). This helps in choosing designs that match capabilities. When opportunities and capabilities do not match, management must consider the potential for expanding or changing capabilities to take advantage of those opportunities.

Forecasts of future demand can be very useful, supplying information on the timing and volume of demand, and information on demands for new products and services.

Manufacturability is a key concern for manufactured goods: Ease of fabrication and/or assembly is important for cost, productivity, and quality. With services, ease of providing the service, cost, productivity, and quality are of great concern.

The term design for manufacturing (DFM) is used to indicate the designing of products that are compatible with an organization’s capabilities. A related concept in manufacturing is design for assembly (DFA) . A good design must take into account not only how a product will be fabricated, but also how it will be assembled. Design for assembly focuses on reducing the number of parts in an assembly, as well as on the assembly methods and sequence that will be employed. Another, more general term, manufacturability , is sometimes used when referring to the ease with which products can be fabricated and/or assembled.

Component Commonality

Companies often have multiple products or services to offer customers. Often, these products or services have a high degree of similarity of features and components. This is particularly true of product families, but it is also true of many services. Companies can realize significant benefits when a part can be used in multiple products. For example, car manufacturers employ this tactic by using internal components such as water pumps, engines, and transmissions on several automobile nameplates. In addition to the savings in design time, companies reap benefits through standard training for assembly and installation, increased opportunities for savings by buying in bulk from suppliers, and commonality of parts for repair, which reduces the inventory that dealers and auto parts stores must carry. Similar benefits accrue in services. For example, in automobile repair, component commonality means less training is needed because the variety of jobs is reduced. The same applies to appliance repair, where commonality and substitutability of parts are typical. Multiple-use forms in financial and medical services are other examples. Computer software often comprises a number of modules that are commonly used for similar applications, thereby saving the time and cost to write the code for major portions of the software. Tool manufacturers use a design that allows tool users to attach different power tools to a common power source. Similarly, HP has a universal power source that can be used with a variety of computer hardware.

Overview of Service Design

Service design begins with the choice of a service strategy, which determines the nature and focus of the service, and the target market. This requires an assessment by top management of the potential market and profitability (or need, in the case of a nonprofit organization) of a particular service, and an assessment of the organization’s ability to provide the service. Once decisions on the focus of the service and the target market have been made, the customer requirements and expectations of the target market must be determined.

Two key issues in service design are the degree of variation in service requirements and the degree of customer contact and customer involvement in the delivery system. These have an impact on the degree to which service can be standardized or must be customized. The lower the degree of customer contact and service requirement variability, the more standardized the service can be. Service design with no contact and little or no processing variability is very much like product design. Conversely, high variability and high customer contact generally mean the service must be highly customized. A related consideration in service design is the opportunity for selling: The greater the degree of customer contact, the greater the opportunities for selling.

Differences between Service Design and Product Design

Service operations managers must contend with issues that may be insignificant or nonexistent for managers in a production setting. These include the following:

1. Products are generally tangible; services are generally intangible. Consequently, service design often focuses more on intangible factors (e.g., peace of mind, ambiance) than does product design.

2. In many instances, services are created and delivered at the same time (e.g., a haircut, a car wash). In such instances, there is less latitude in finding and correcting errors before the customer has a chance to discover them. Consequently, training, process design, and customer relations are particularly important.

3. Services cannot be inventoried. This poses restrictions on flexibility and makes capacity issues very important.

4. Services that are highly visible to consumers and must be designed with that in mind; this adds an extra dimension to process design, one that usually is not present in product design.

5. Some services have low barriers to entry and exit. This places additional pressures on service design to be innovative and cost-effective.

6. Location is often important to service design, with convenience as a major factor. Hence, design of services and choice of location are often closely linked.

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7. Service systems range from those with little or no customer contact to those that have a very high degree of customer contact. Here are some examples of those different types:

   Insulated technical core; little or no customer contact (e.g., software development)

   Production line; little or no customer contact (e.g., automatic car wash)

   Personalized service (e.g., haircut, medical service)

   Consumer participation (e.g., diet program, dance lessons)

   Self-service (e.g., supermarket)

   If there is little or no customer contact, service system design is like product system design.

8. Demand variability alternately creates customer waiting times, which sometimes leads to lost sales, or idle service resources.

When demand variability is a factor, designers may approach service design from one of two perspectives. One is a cost and efficiency perspective, and the other is a customer perspective. Waiting line analysis (see Chapter 18) can be especially useful in this regard.

Basing design objectives on cost and efficiency is essentially a “product design approach” to service design. Because customer participation makes both quality and demand variability more difficult to manage, designers may opt to limit customer participation in the process where possible. Alternatively, designers may use staff flexibility as a means of dealing with demand variability.

In services, a significant aspect of perceived quality relates to the intangibles that are part of the service package. Designers must proceed with caution because attempts to achieve a high level of efficiency tend to depersonalize service and to create the risk of negatively altering the customer’s perception of quality. Such attempts may involve the following:

• Reducing consumer choices makes service more efficient, but it can be both frustrating and irritating for the customer. An example would be a cable company that bundles channels, rather than allowing customers to pick only the channels they want.

• Standardizing or simplifying certain elements of service can reduce the cost of providing a service, but it risks eliminating features that some customers value, such as personal attention.

• Incorporating flexibility in capacity management by employing part-time or temporary staff may involve the use of less-skilled or less-interested people, and service quality may suffer.

Design objectives based on customer perspective require understanding the customer experience, and focusing on how to maintain control over service delivery to achieve customer satisfaction. The customer-oriented approach involves determining consumer wants and needs in order to understand relationships between service delivery and perceived quality. This enables designers to make enlightened choices in designing the delivery system.

Of course, designers must keep in mind that while depersonalizing service delivery for the sake of efficiency can negatively impact perceived quality, customers may not want or be willing to pay for highly personalized service either, so trade-offs may have to be made.

Phases in the Service Design Process

Table 4.5 lists the phases in the service design process. As you can see, they are quite similar to the phases of product design, except that the delivery system also must be designed.

TABLE 4.5

Phases in service design process

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Service Blueprinting

A useful tool for conceptualizing a service delivery system is the service blueprint , which is a method for describing and analyzing a service process. A service blueprint is much like an architectural drawing, but instead of showing building dimensions and other construction features, a service blueprint shows the basic customer and service actions involved in a service operation. Figure 4.7 illustrates a simple service blueprint for a restaurant. At the top of the figure are the customer actions, and just below are the related actions of the direct contact service people. Next are what are sometimes referred to as “backstage contacts”—in this example, the kitchen staff—and below those are the support, or “backroom,” operations. In this example, support operations include the reservation system, ordering of food and supplies, cashier, and the outsourcing of laundry service. Figure 4.7 is a simplified illustration—typically, time estimates for actions and operations would be included.

The major steps in service blueprinting are as follows:

1. Establish boundaries for the service and decide on the level of detail needed.

2. Identify and determine the sequence of customer and service actions and interactions. A flowchart can be a useful tool for this.

3. Develop time estimates for each phase of the process, as well as time variability.

4. Identify potential failure points and develop a plan to prevent or minimize them, as well as a plan to respond to service errors.

Characteristics of Well-Designed Service Systems

There are a number of characteristics of well-designed service systems. They can serve as guidelines in developing a service system. They include the following:

• Being consistent with the organization’s mission.

• Being user-friendly.

• Being robust if variability is a factor.

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• Being easy to sustain.

• Being cost-effective.

• Having value that is obvious to customers.

• Having effective linkages between back-of-the-house operations (i.e., no contact with the customer) and front-of-the-house operations (i.e., direct contact with customers). Front operations should focus on customer service, while back operations should focus on speed and efficiency.

• Having a single, unifying theme, such as convenience or speed.

• Having design features and checks that will ensure service that is reliable and of high quality.

Challenges of Service Design

Variability is a major concern in most aspects of business operations, and it is particularly so in the design of service systems. Requirements tend to be variable, both in terms of differences in what customers want or need, and in terms of the timing of customer requests. Because services generally cannot be stored, there is the additional challenge of balancing supply and demand. This is less of a problem for systems in which the timing of services can be scheduled (e.g., doctor’s appointment), but not so in others (e.g., emergency room visit).

Another challenge is that services can be difficult to describe precisely and are dynamic in nature, especially when there is a direct encounter with the customer (e.g., personal services), due to the large number of variables.

Guidelines for Successful Service Design

1. Define the service package in detail. A service blueprint may be helpful for this.

2. Focus on the operation from the customer’s perspective. Consider how customer expectations and perceptions are managed during and after the service.

3. Consider the image that the service package will present both to customers and to prospective customers.

4. Recognize that designers’ familiarity with the system may give them quite a different perspective than that of the customer, and take steps to overcome this.

5. Make sure that managers are involved and will support the design once it is implemented.

6. Define quality for both tangibles and intangibles. Intangible standards are more difficult to define, but they must be addressed.

7. Make sure that recruitment, training, and reward policies are consistent with service expectations.

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8. Establish procedures to handle both predictable and unpredictable events.

9. Establish systems to monitor, maintain, and improve service.

Finding the Probability of Functioning When Activated

The probability that a system or a product will operate as planned is an important concept in system and product design. Determining that probability when the product or system consists of a number of independent components requires the use of the rules of probability for independent events. Independent events have no relation to the occurrence or nonoccurrence of each other. What follows are three examples illustrating the use of probability rules to determine whether a given system will operate successfully.

Rule 1. If two or more events are independent and success is defined as the probability that all of the events occur, then the probability of success is equal to the product of the probabilities of the events.

Example Suppose a room has two lamps, but to have adequate light both lamps must work (success) when turned on. One lamp has a probability of working of .90, and the other has a probability of working of .80. The probability that both will work is .90 × .80 = .72. Note that the order of multiplication is unimportant: .80 × .90 = .72. Also note that if the room had three lamps, three probabilities would have been multiplied.

This system can be represented by the following diagram:

Even though the individual components of a system might have high reliabilities, the system as a whole can have considerably less reliability because all components that are in series (as are the ones in the preceding example) must function. As the number of components in a series increases, the system reliability decreases. For example, a system that has eight components in a series, each with a reliability of .99, has a reliability of only .99 ⁸ = .923.

Obviously, many products and systems have a large number of component parts that must all operate, and some way to increase overall reliability is needed. One approach is to use redundancy in the design. This involves providing backup parts for some items.

Rule 2. If two events are independent and success is defined as the probability that at least one of the events will occur, the probability of success is equal to the probability of either one plus 1.00 minus that probability multiplied by the other probability.

Example There are two lamps in a room. When turned on, one has a probability of working of .90 and the other has a probability of working of .80. Only a single lamp is needed to light for success. If one fails to light when turned on, the other lamp is turned on. Hence, one of the lamps is a backup in case the other one fails. Either lamp can be treated as the backup; the probability of success will be the same. The probability of success is .90 + (1 − .90) × .80 = .98. If the .80 light is first, the computation would be .80 + (1 − .80) × .90 = .98.

This system can be represented by the following diagram:

Rule 3. If two or more events are involved and success is defined as the probability that at least one of them occurs, the probability of success is 1 − p (all fail).

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Example Three lamps have probabilities of .90, .80, and .70 of lighting when turned on. Only one lighted lamp is needed for success; hence, two of the lamps are considered to be backups. The probability of success is

1 − [(1 − .90) × (1 − .80) × (1 − .70)] = .994

Note: It is assumed that the switch that activates each lamp has a reliability of 100%. To see how to incorporate a switch with less than 100% reliability, consider that the second “lamp” is actually a switch with a probability of operating equal to .80, and the third lamp is the only backup (i.e., the second lamp). Thus, the problem would be solved in exactly the same way.

This system can be represented by the following diagram:

Finding the Probability of Functioning for a Specified Length of Time

The second way of looking at reliability considers the incorporation of a time dimension: Probabilities are determined relative to a specified length of time. This approach is commonly used in product warranties, which pertain to a given period of time after purchase of a product.

A typical profile of product failure rate over time is illustrated in Figure 4S.1. Because of its shape, it is sometimes referred to as a bathtub curve. Frequently, a number of products fail shortly after they are put into service, not because they wear out, but because they are defective to begin with. The rate of failures decreases rapidly once the truly defective items are weeded out. During the second phase, there are fewer failures because most of the defective items have been eliminated, and it is too soon to encounter items that fail because they have worn out. In some cases, this phase covers a relatively long time. In the third phase, failures occur because the products are worn out, and the failure rate increases.

Information on the distribution and length of each phase requires the collection of historical data and analysis of those data. It often turns out that the mean time between failures (MTBF) page 179in the infant mortality phase can be modeled by a negative exponential distribution, such as that depicted in Figure 4S.2. Equipment failures, as well as product failures, may occur in this pattern. In such cases, the exponential distribution can be used to determine various probabilities of interest. The probability that equipment or a product put into service at time 0 will fail before some specified time, T, is equal to the area under the curve between 0 and T. Reliability is specified as the probability that a product will last at least until time  T; reliability is equal to the area under the curve beyond T. (Note that the total area under the curve in each phase is treated as 100 percent for computational purposes.) Observe that, as the specified length of service increases, the area under the curve to the right of that point (i.e., the reliability) decreases.

Determining values for the area under a curve to the right of a given point, T, becomes a relatively simple matter using a table of exponential values. An exponential distribution is completely described using a single parameter, the distribution mean, which reliability engineers often refer to as the mean time between failures. Using the symbol T to represent length of service, the probability that failure will not occur before time T (i.e., the area in the right tail) is easily determined:

P(no failure before T) = e ^{− T/MTBF}

where

The probability that failure will occur before time T is:

P(failure before T) = 1 − e ^{− T/MTBF}

Selected values of e ^{− T/MTBF} are listed in Table 4S.1.

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TABLE 4S.1

Values of e ^{− T/MTBF}

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Product failure due to wear-out can sometimes be modeled by a normal distribution. Obtaining probabilities involves the use of a table (refer to Appendix Table B.2). The table provides areas under a normal curve from (essentially) the left end of the curve to a specified point z , where z is a standardized value computed using the formula

Thus, to work with the normal distribution, it is necessary to know the mean of the distribution and its standard deviation. A normal distribution is illustrated in Figure 4S.3. Appendix Table B.2 contains normal probabilities (i.e., the area that lies to the left of z). To obtain a probability that service life will not exceed some value T, compute z and refer to the table. To find the reliability for time T, subtract this probability from 100 percent. To obtain the value of T that will provide a given probability, locate the nearest probability under the curve to the left in Appendix Table B.2. Then, use the corresponding z in the preceding formula and solve for T.

Steps in the Capacity Planning Process

1. Estimate future capacity requirements.

2. Evaluate existing capacity and facilities and identify gaps.

3. Identify alternatives for meeting requirements.

4. Conduct financial analyses of each alternative.

5. Assess key qualitative issues for each alternative.

6. Select the alternative to pursue that will be best in the long term.

7. Implement the selected alternative.

8. Monitor results.

Capacity planning can be difficult at times due to the complex influence of market forces and technology.

Calculating Processing Requirements

A necessary piece of information is the capacity requirements of products that will be processed. To get this information, one must have reasonably accurate demand forecasts for each product and know the standard processing time per unit for each product, the number of workdays per year, and the number of shifts that will be used.

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The task of determining capacity requirements should not be taken lightly. Substantial losses can occur when there are misjudgments on capacity needs. One key reason for those misjudgments can be overly optimistic projections of demand and growth. Marketing personnel are generally optimistic in their outlook, which isn’t necessarily a bad thing. But care must be taken so that that optimism doesn’t lead to overcapacity, because the resulting underutilized capacity will create an additional cost burden. Another key reason for misjudgments may be focusing exclusively on sales and revenue potential, and not taking into account the product mix that will be needed to generate those sales and revenues. To avoid that, marketing and operations personnel must work closely to determine the optimal product mix needed and the resulting cost and profit.

A reasonable approach to determining capacity requirements is to obtain a forecast of future demand, translate demand into both the quantity and the timing of capacity requirements, and then decide what capacity changes (increased, decreased, or no changes) are needed.

Long-term capacity alternatives include the expansion or contraction of an existing facility, opening or closing branch facilities, and the relocation of existing operations. At this point, a decision must be made about whether to make or buy a good, or provide or buy a service.

Cost–Volume Analysis

Cost–volume analysis focuses on relationships between cost, revenue, and volume of output. The purpose of cost–volume analysis is to estimate the income of an organization under different operating conditions. It is particularly useful as a tool for comparing capacity alternatives.

Use of the technique requires identification of all costs related to the production of a given product. These costs are then designated as fixed costs or variable costs. Fixed costs tend to remain constant regardless of volume of output. Examples include rental costs, property taxes, equipment costs, heating and cooling expenses, and certain administrative costs. Variable costs vary directly with volume of output. The major components of variable costs are generally materials and labor costs. We will assume that variable cost per unit remains the same regardless of volume of output, and that all output can be sold.

Table 5.4 summarizes the symbols used in the cost–volume formulas.

TABLE 5.4

Cost–volume symbols

The total cost associated with a given volume of output is equal to the sum of the fixed cost and the variable cost per unit times volume:

where v = variable cost per unit. Figure 5.6A shows the relationship between volume of output and fixed costs, total variable costs, and total (fixed plus variable) costs.

Revenue per unit, like variable cost per unit, is assumed to be the same regardless of quantity of output. Total revenue will have a linear relationship to output, as illustrated in Figure 5.6B. The total revenue associated with a given quantity of output, Q, is

Figure 5.6C describes the relationship between profit—which is the difference between total revenue and total (i.e., fixed plus variable) cost—and volume of output. The volume at which total cost and total revenue are equal is referred to as the break-even point (BEP) . When volume is less than the break-even point, there is a loss; when volume is greater than the break-even point, there is a profit. The greater the deviation from this point, the greater the profit or loss. Figure 5.6D shows total profit or loss relative to the break-even point. Figure 5.6D can be obtained from Figure 5.6C by drawing a horizontal line through the point where the total cost and total revenue lines intersect. Total profit can be computed using the formula

Rearranging terms, we have

The difference between revenue per unit and variable cost per unit, R − v, is known as the contribution margin.

The required volume, Q, needed to generate a specified profit is

A special case of this is the volume of output needed for total revenue to equal total cost. This is the break-even point, computed using the formula

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Different alternatives can be compared by plotting the profit lines for the alternatives, as shown in Figure 5.6E.

Figure 5.6E illustrates the concept of an indifference point : the quantity at which a decision maker would be indifferent between two competing alternatives. In this illustration, a quantity less than the point of indifference would favor choosing alternative B because its profit is higher in that range, while a quantity greater than the point of indifference would favor choosing alternative A.

Capacity alternatives may involve step costs, which are costs that increase stepwise as potential volume increases. For example, a firm may have the option of purchasing one, two, or three machines, with each additional machine increasing the fixed cost, although perhaps not linearly. (See Figure 5.7A.) Then, fixed costs and potential volume would depend on the number of machines purchased. The implication is that multiple break-even quantities may occur, possibly one for each range. Note, however, that the total revenue line might not intersect the fixed-cost line in a particular range, meaning that there would be no break-even point in that range. This possibility is illustrated in Figure 5.7B, where there is no break-even point in the first range. In order to decide how many machines to purchase, a manager must consider projected annual demand (volume) relative to the multiple break-even points and choose the most appropriate number of machines, as Example 4 shows.

Cost–volume analysis can be a valuable tool for comparing capacity alternatives if certain assumptions are satisfied:

• One product is involved.

• Everything produced can be sold.

• The variable cost per unit is the same regardless of the volume.

• Fixed costs do not change with volume changes, or they are step changes.

• The revenue per unit is the same regardless of volume.

• Revenue per unit exceeds variable cost per unit.

As with any quantitative tool, it is important to verify that the assumptions on which the technique is based are reasonably satisfied for a particular situation. For example, revenue per unit or variable cost per unit is not always constant. In addition, fixed costs may not be constant over the range of possible output. If demand is subject to random variations, one must take that into account in the analysis. Also, cost–volume analysis requires that fixed and variable costs can be separated, and this is sometimes exceedingly difficult to accomplish. Cost–volume analysis works best with one product or a few products that have the same cost characteristics.

A notable benefit of cost–volume considerations is the conceptual framework it provides for integrating cost, revenue, and profit estimates into capacity decisions. If a proposal looks attractive using cost–volume analysis, the next step would be to develop cash flow models to see how it fares with the addition of time and more flexible cost functions.

Financial Analysis

Operations personnel need to have the ability to do financial analysis. A problem that is universally encountered by managers is how to allocate scarce funds. A common approach is to use financial analysis to rank investment proposals, taking into account the time value of money.

Two important terms in financial analysis are cash flow and present value:

Cash flow refers to the difference between the cash received from sales (of goods or services) and other sources (e.g., sale of old equipment) and the cash outflow for labor, materials, overhead, and taxes.

Present value expresses in current value the sum of all future cash flows of an investment proposal.

The three most commonly used methods of financial analysis are payback, present value, and internal rate of return.

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Payback is a crude but widely used method that focuses on the length of time it will take for an investment to return its original cost. For example, an investment with an original cost of $6,000 and a monthly net cash flow of $1,000 has a payback period of six months.

Payback doesn’t take into account the time value of money. Its use is easier to rationalize for short-term paybacks than for long-term paybacks. The present value (PV) method does take the time value of money into account. It summarizes the initial cost of an investment, its estimated annual cash flows, and any expected salvage value in a single value called the equivalent current value, taking into account the time value of money (i.e., interest rates).

The internal rate of return (IRR) summarizes the initial cost, expected annual cash flows, and estimated future salvage value of an investment proposal in an equivalent interest rate. In other words, this method identifies the rate of return that equates the estimated future returns and the initial cost.

These techniques are appropriate when there is a high degree of certainty associated with estimates of future cash flows. In many instances, however, operations managers and other managers must deal with situations better described as risky or uncertain. When conditions of risk or uncertainty are present, decision theory is often applied.

Decision Theory

Decision theory is a helpful tool for financial comparison of alternatives under conditions of risk or uncertainty. It is suited to capacity decisions and to a wide range of other decisions managers must make. It involves identifying a set of possible future conditions that could influence results, listing alternative courses of action, and developing a financial outcome for each alternative–future condition combination. Decision theory is described in the supplement to this chapter.

Waiting-Line Analysis

Analysis of lines is often useful for designing or modifying service systems. Waiting lines have a tendency to form in a wide variety of service systems (e.g., airport ticket counters, telephone calls to a cable television company, hospital emergency rooms). The lines are symptoms of bottleneck operations. Analysis is useful in helping managers choose a capacity level that will be cost-effective through balancing the cost of having customers wait with the cost of providing additional capacity. It can aid in the determination of expected costs for various levels of service capacity.

This topic is described in Chapter 18.

Simulation

Simulation can be a useful tool in evaluating what-if scenarios, and is described on this book’s website.

Process Types

There are five basic process types: job shop, batch, repetitive, continuous, and project.

Job Shop. A job shop usually operates on a relatively small scale. It is used when a low volume of high-variety goods or services will be needed. Processing is intermittent; work includes small jobs, each with somewhat different processing requirements. High flexibility using general-purpose equipment and skilled workers are important characteristics of a job shop. A manufacturing example of a job shop is a tool and die shop that is able to produce one-of-a-kind tools. A service example is a veterinarian’s office, which is able to process many types of animals and a variety of injuries and diseases.

Batch. Batch processing is used when a moderate volume of goods or services is desired, and it can handle a moderate variety in products or services. The equipment need not be as flexible as in a job shop, but processing is still intermittent. The skill level of workers doesn’t need to be as high as in a job shop because there is less variety in the jobs being processed. Examples of batch systems include bakeries, which make bread, cakes, or cookies in batches; movie theaters, which show movies to groups (batches) of people; and airlines, which carry planeloads (batches) of people from airport to airport. Other examples of products that lend themselves to batch production are paint, ice cream, soft drinks, beer, magazines, and books. Other examples of services include plays, concerts, music videos, radio and television programs, and public address announcements.

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Repetitive. When higher volumes of more standardized goods or services are needed, repetitive processing is used. The standardized output means only slight flexibility of equipment is needed. Skill of workers is generally low. Examples of this type of system include production lines and assembly lines. Sometimes these terms are used interchangeably, although assembly lines generally involve the last stages of an assembled product. Familiar products made by these systems include automobiles, television sets, smartphones, and computers. An example of a service system is an automatic carwash. Other examples of service include cafeteria lines and ticket collectors at sports events and concerts. Also, mass customization is an option.

Continuous. When a very high volume of nondiscrete, highly standardized output is desired, a continuous system is used. These systems have almost no variety in output and, hence, no need for equipment flexibility. Workers’ skill requirements can range from low to high, depending on the complexity of the system and the expertise that workers need. Generally, if equipment is highly specialized, worker skills can be lower. Examples of nondiscrete products made in continuous systems include petroleum products, steel, sugar, flour, and salt. Continuous services include air monitoring, supplying electricity to homes and businesses, and the internet.

These process types are found in a wide range of manufacturing and service settings. The ideal is to have process capabilities match product or service requirements. Failure to do so can result in inefficiencies and higher costs than are necessary, perhaps creating a competitive disadvantage. Table 6.1 provides a brief description of each process type, along with the advantages and disadvantages of each.

TABLE 6.1

Types of processing

Figure 6.2 provides an overview of these four process types in the form of a matrix, with an example for each process type. Note that job variety, process flexibility, and unit cost are highest for a job shop and get progressively lower moving from job shop to continuous processing. Conversely, volume of output is lowest for a job shop and gets progressively higher moving from job shop to continuous processing. Note, too, that the examples fall along the diagonal. The implication is that the diagonal represents the ideal choice of processing system for a given set of circumstances. For example, if the goal is to be able to process a small volume of jobs that will involve high variety, job shop processing is most appropriate. For less variety and a higher volume, a batch system would be most appropriate, and so on. Note that combinations far from the diagonal would not even be considered, such as using a job shop for high-volume, low-variety jobs, or continuous processing for low-volume, high-variety jobs, because that would result in either higher than necessary costs or lost opportunities.

Another consideration is that products and services often go through life cycles that begin with low volume, which increases as products or services become better known. When that happens, a manager must know when to shift from one type of process (e.g., job shop) to the next (e.g., batch). Of course, some operations remain at a certain level (e.g., magazine publishing), while others increase (or decrease as markets become saturated) over time. Again, it is important for a manager to assess his or her products and services and make a judgment on whether to plan for changes in processing over time.

All of these process types (job shop, batch, repetitive, and continuous) are typically ongoing operations. However, some situations are not ongoing but instead are of limited duration. In such instances, the work is often organized as a project.

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Project. A project is used for work that is nonroutine, with a unique set of objectives to be accomplished in a limited time frame. Examples range from simple to complicated, including such things as putting on a play, consulting, making a motion picture, launching a new product or service, publishing a book, building a dam, and building a bridge. Equipment flexibility and worker skills can range from low to high.

The type of process or processes used by an organization influences a great many activities of the organization. Table 6.2 briefly describes some of those influences.

TABLE 6.2

Process choice affects numerous activities/functions

Process type also impacts supply chain requirements. Repetitive and continuous processes require steady inputs of high-volume goods and services. Delivery reliability in terms of quality and timing is essential. Job shop and batch processing may mean that suppliers have to be able to deal with varying order quantities and timing of orders. In some instances, seasonality is a factor, so suppliers must be able to handle periodic large demand.

The processes discussed do not always exist in their “pure” forms. It is not unusual to find hybrid processes—processes that have elements of other process types embedded in them. For instance, companies that operate primarily in a repetitive mode, or a continuous mode, will often have repair shops (i.e., job shops) to fix or make new parts for equipment that fails. Also, if volume increases for some items, an operation that began, say, in a job shop or as a batch mode may evolve into a batch or repetitive operation. This may result in having some operations in a job shop or batch mode, and others in a repetitive mode.

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Product and Service Profiling

Process selection can involve substantial investment in equipment and have a very specific influence on the layout of facilities, which also require heavy investment. Moreover, mismatches between operations capabilities and market demand and pricing or cost strategies can have a significant negative impact on the ability of the organization to compete or, in government agencies, to effectively service clients. Therefore, it is highly desirable to assess the page 252degree of correlation between various process choices and market conditions before making process choices in order to achieve an appropriate matching.

Product or service profiling can be used to avoid any inconsistencies by identifying key product or service dimensions and then selecting appropriate processes. Key dimensions often relate to the range of products or services that will be processed, expected order sizes, pricing strategies, expected frequency of schedule changes, and order-winning requirements.

Sustainable Production of Goods and Services

Business organizations are facing increasing pressure from a variety of sources to operate sustainable production processes. According to the Lowell Center for Sustainable Production ( http://sustainableproduction.org), “Sustainable Production is the creation of goods and services using processes and systems that are: non-polluting; conserving of energy and natural resources; economically efficient; safe and healthful for workers, communities, and consumers; and socially and creatively rewarding for all working people.” To achieve this, the Lowell Center advocates designing and operating processes in ways that:

• “wastes and ecologically incompatible byproducts are reduced, eliminated or recycled on-site;

• chemical substances or physical agents and conditions that present hazards to human health or the environment are eliminated;

• energy and materials are conserved, and the forms of energy and materials used are most appropriate for the desired ends; and

• work spaces are designed to minimize or eliminate chemical, ergonomic and physical hazard.”

To achieve these goals, business organizations must focus on a number of factors that include energy use and efficiency, CO ₂ (carbon footprint) and toxic emissions, waste generation, lighting, heating, cooling, ventilation, noise and vibration, and worker health and safety.

Lean Process Design

Lean process design is guided by general principles that are discussed more fully in a later chapter. One principle of particular interest here is waste reduction, which relates to sustainability objectives. Lean design also focuses on variance reduction in workload over the entire process to achieve level production and thereby improve process flow. Successful lean design results in reduced inventory and floor space; quicker response times and shorter lead times; reduced defects, rework, and scrap; and increased productivity. Lean design is often translated into practice using cellular layouts, which are discussed later in this chapter.

Lean process design has broad applications in seemingly diverse areas such as health care delivery systems, manufacturing, construction projects, and process reengineering.

Automation

An increasingly asked question in process design is whether to automate. Automation is machinery that has sensing and control devices that enable it to operate automatically. If a company decides to automate, the next question is how much. Automation can range from factories that are completely automated to a single automated operation.

Automated services are becoming increasingly important. Examples range from automated teller machines (ATMs) to automated heating and air conditioning and include automated inspection, automated storage and retrieval systems, package sorting, mail processing, e-mail, online banking, and E-Z pass.

Automation offers a number of advantages over human labor. It has low variability, whereas it is difficult for a human to perform a task in exactly the same way, in the same amount of time, and on a repetitive basis. In a production setting, variability is detrimental to quality and to meeting schedules. Moreover, machines do not get bored or distracted, nor do they go on strike, ask for higher wages, or file labor grievances. Still another advantage of automation is the reduction of variable costs. In order for automated processing to be an option, job-processing requirements must be standardized (i.e., have very little or no variety).

Both manufacturing and service organizations are increasing their use of automation as a way to reduce costs, increase productivity, and improve quality and consistency.

Automation is frequently touted as a strategy necessary for competitiveness. However, automation also has certain disadvantages and limitations compared to human labor. To begin with, it can be costly. Technology is expensive; usually it requires high volumes of output to offset high costs. In addition, automation is much less flexible than human labor. Once a process has been automated, there are substantial reasons for not changing it. Moreover, workers sometimes fear automation because it might cause them to lose their jobs. This can have an adverse effect on morale and productivity.

Decision makers must carefully examine the issue of whether to automate, or the degree to which to automate, so they clearly understand all the ramifications. Also, much thought and page 254careful planning are necessary to successfully integrate automation into a production system. Otherwise, it can lead to major problems. Automation has important implications not only for cost and flexibility, but also for the fit with overall strategic priorities. If the decision is made to automate, care must be taken to remove waste from the system prior to automating, to avoid building the waste into the automated system. Table 6.3 has a list of questions for organizations that are considering automation.

TABLE 6.3

Automation questions

Generally speaking, there are three kinds of automation: fixed, programmable, and flexible.

Fixed automation is the least flexible. It uses high-cost, specialized equipment for a fixed sequence of operations. Low cost and high volume are its primary advantages; minimal variety and the high cost of making major changes in either product or process are its primary limitations.

Programmable automation involves the use of high-cost, general-purpose equipment controlled by a computer program that provides both the sequence of operations and specific details about each operation. This type of automation has the capability of economically producing a fairly wide variety of low-volume products in small batches. Numerically controlled (N/C) machines and some robots are applications of programmable automation.

Computer-aided manufacturing (CAM) refers to the use of computers in process control, ranging from robots to automated quality control. Numerically controlled (N/C) machines are programmed to follow a set of processing instructions based on mathematical relationships that tell the machine the details of the operations to be performed. The instructions are stored on a device such as a microprocessor. Although N/C machines have been used for many years, they are an important part of new approaches to manufacturing. Individual machines often have their own computer; this is referred to as computerized numerical control (CNC). Or one computer may control a number of N/C machines, which is referred to as direct numerical control (DNC).

N/C machines are best used in cases where parts are processed frequently and in small batches, where part geometry is complex, close tolerances are required, mistakes are costly, and there is the possibility of frequent changes in design. The main limitations of N/C page 255machines are the higher skill levels needed to program the machines and their inability to detect tool wear and material variation.

The use of robots in manufacturing is sometimes an option. Robots can handle a wide variety of tasks, including welding, assembly, loading and unloading of machines, painting, and testing. They relieve humans from heavy or dirty work and often eliminate drudgery tasks.

Some uses of robots are fairly simple, others are much more complex. At the lowest level are robots that follow a fixed set of instructions. Next are programmable robots, which can repeat a set of movements after being led through the sequence. These robots “play back” a mechanical sequence much as a video recorder plays back a visual sequence. At the next level up are robots that follow instructions from a computer. Below are robots that can recognize objects and make certain simple decisions.

Still another form of robots are collaborative robots (also known as cobots) that are designed to work collaboratively with humans. The collaborative application of robotics enables humans and robots to work together safely and effectively, augmenting the capabilities of their human counterparts, achieving results neither could do alone. Cobots are designed with multiple advanced sensors, software, and end of arm tooling that help them quickly and easily sense and adapt to anything that comes into their work space. They also have the ability to detect any abnormal force applied to their joints while in motion. These robots can be programmed to respond immediately by stopping or reversing positions when they come into contact with a human.

Flexible automation evolved from programmable automation. It uses equipment that is more customized than that of programmable automation. A key difference between the two is that flexible automation requires significantly less changeover time. This permits almost continuous operation of equipment and product variety without the need to produce in batches.

In practice, flexible automation is used in several different formats.

A flexible manufacturing system (FMS) is a group of machines that include supervisory computer control, automatic material handling, and robots or other automated processing equipment. Reprogrammable controllers enable these systems to produce a variety of similar products. Systems may range from three or four machines to more than a dozen. They are designed to handle intermittent processing requirements with some of the benefits of automation and some of the flexibility of individual, or stand-alone, machines (e.g., N/C machines). Flexible manufacturing systems offer reduced labor costs and more consistent quality when compared with more traditional manufacturing methods, lower capital investment and higher page 256flexibility than “hard” automation, and relatively quick changeover time. Flexible manufacturing systems often appeal to managers who hope to achieve both the flexibility of job shop processing and the productivity of repetitive processing systems.

Although these are important benefits, an FMS also has certain limitations. One is that this type of system can handle a relatively narrow range of part variety, so it must be used for a family of similar parts, which all require similar machining. Also, an FMS requires longer planning and development times than more conventional processing equipment because of its increased complexity and cost. Furthermore, companies sometimes prefer a gradual approach to automation, and FMS represents a sizable chunk of technology.

Computer-integrated manufacturing (CIM) is a system that uses an integrating computer system to link a broad range of manufacturing activities, including engineering design, flexible manufacturing systems, purchasing, order processing, and production planning and control. Not all elements are absolutely necessary. For instance, CIM might be as simple as linking two or more FMSs by a host computer. More encompassing systems can link scheduling, purchasing, inventory control, shop control, and distribution. In effect, a CIM system integrates information from other areas of an organization with manufacturing.

The overall goal of using CIM is to link various parts of an organization to achieve rapid response to customer orders and/or product changes, to allow rapid production, and to reduce indirect labor costs.

A shining example of how process choices can lead to competitive advantages can be found at Allen-Bradley’s computer-integrated manufacturing process in Milwaukee, Wisconsin. The company converted a portion of its factory to a fully automated “factory within a factory” to assemble contacts and relays for electrical motors. A handful of humans operate the factory, although once an order has been entered into the system, the machines do virtually all the work, including packaging and shipping, and quality control. Any defective items are removed from the line, and replacement parts are automatically ordered and scheduled to compensate for the defective items. The humans program the machines, monitor operations, and attend to any problems signaled by a system of warning lights.

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As orders come into the plant, computers determine production requirements and schedules and order the necessary parts. Bar-coded labels that contain processing instructions are automatically placed on individual parts. As the parts approach a machine, a sensing device reads the bar code and communicates the processing instructions to the machine. The factory can produce 600 units an hour.

The company has realized substantial competitive advantages from the system. Orders can be completed and shipped within 24 hours of entry into the system, indirect labor costs and inventory costs have been greatly reduced, and quality is very high.

The Internet of Things (IoT). The internet of things is the extension of internet connectivity into devices such as cell phones, vehicles, audio and video device, and much more, some of which you are probably familiar with. These devices can send and receive information with others over the internet. Industrial use of the IoT will have a major impact on manufacturing and the global economy with intelligence that augments human capabilities. Applications involve AI (artificial intelligence) machine learning, quality and productivity improvement, and predictive maintenance.

3D Printing

A 3D printer is a type of industrial robot that is controlled using computer-assisted design (CAD). 3D printing , also known as additive manufacturing, involves processes that create three-dimensional objects by applying successive layers of materials to create the objects. The objects can be of almost any size or shape. These processes are different than many familiar processes that use subtractive manufacturing to create objects: Material is removed by methods such as cutting, grinding, sanding, drilling, and milling. Also, producing an object using 3D printing is generally much slower than the time needed using more conventional techniques in a factory setting.

In early applications, material was deposited onto a powder bed using inkjet printer heads—hence, the name 3D printing. Today, the term 3D printing refers to a wide range of techniques such as extrusion (the deformation of either metal or plastic forced under pressure through a die to create a shape) and sintering (using heat or pressure or both to form a solid material from powder without causing it to liquefy).

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3D printers come in a wide variety of sizes and shapes. Some printers look very much like a microwave oven, while others look completely different.

The use of 3D scanning technologies allows the replication of objects without the use of molds. That can be beneficial in cases where molding techniques are difficult or costly, or where contact with substances used in molding processes could harm the original item. 3D objects can also be created from photographs of an existing object. That involves taking a series of photographs of the object (usually about 20) from various angles in order to capture adequate detail of the object for reproduction.

It is possible that in the long term, 3D printing technologies could have a significant impact on where and how production occurs and on supply chains.

Applications. Commercial applications of 3D printing are occurring in a wide array of businesses, and also have a few consumer applications, some of which are shown in Table 6.4.

TABLE 6.4

Some examples of applications of 3D technology

Benefits. Although 3D printing is unlikely to replace more widespread forms of high-volume production in the foreseeable future, it does offer an alternate form of production that provides value in a wide range of applications, even in high-volume systems. In some of those applications, manufacturers have been able to substantially reduce the cost and/or time needed to develop or produce items. Among the examples is production of replacement parts in the case of equipment failure when no spare parts are available. Replacement occurs much faster than the time it would take to receive the part from a supplier, thereby avoiding costly production delays. Other examples include economical production of small quantities of items, and the avoidance of shipping costs and time when the application is not near a supplier.

Advances in 3D printing and reduced costs have fueled a growth in on-demand and micro-manufacturing. On-demand production is not only attractive to customers who want page 259customization, it also reduces inventory needs, and hence, storage space and costs. Additional benefits are increased agility and a reduction in the need for end-item forecasts.

3D printing will become even more useful through development in three areas: printers and printing methods, software to design and print, and materials used in printing.

Drones

Drones are unmanned aircraft, usually small, and remotely controlled or programmed to fly to a specific location. An important benefit is providing an “eye-in-the sky” to obtain visual detail in places that are hazardous to humans or that are not readily accessible. For example, drones are proving to be very helpful in assessing storm and earthquake damage, especially in situations where access by vehicles or on foot is difficult or impossible due to the terrain, debris, or where roads or bridges are impassible. They are also useful for assessing crop damage, monitoring forest fires, and inspecting pipelines, cell towers, railroad tracks, and power lines. In addition, when medicines and medical supplies are urgently needed in remote areas, drones can be used to deliver them. Despite these many benefits, the use of drones poses a number of issues. There is the possibility of collisions with other drones, power lines, birds, or other objects, as well as mechanical failure or operator error, any of which can result in failure to accomplish the intended task. In addition, crashes have the potential to injure nearby humans or cause damage to property.

Repetitive and Continuous Processing: Product Layouts

Product layouts are used to achieve a smooth and rapid flow of large volumes of goods or customers through a system. This is made possible by highly standardized goods or services that allow highly standardized, repetitive processing. The work is divided into a series of standardized tasks, permitting specialization of equipment and division of labor. The large volumes handled by these systems usually make it economical to invest substantial sums of money in equipment and job design. Because only one or a few very similar items are involved, it is feasible to arrange an entire layout to correspond to the technological processing requirements of the product or service. For instance, if a portion of a manufacturing operation required the sequence of cutting, sanding, and painting, the appropriate pieces of equipment would be arranged in that same sequence. And because each item follows the same sequence of operations, it is often possible to utilize fixed-path material-handling equipment, such as conveyors to transport items between operations. The resulting arrangement forms a line like the one depicted in Figure 6.3. In manufacturing environments, the lines are referred to as production lines or assembly lines , depending on the type of activity involved. In service processes, the term line may or may not be used. It is common to refer to a cafeteria line as such but not a car wash, although from a conceptual standpoint the two are nearly identical. Figure 6.4 illustrates the layout of a typical cafeteria serving line. Examples of this type of layout are less plentiful in service environments because processing requirements usually exhibit too much variability to make standardization feasible. Without high standardization, many of the benefits of repetitive processing are lost. When lines are used, certain compromises may be made. For instance, an automatic car wash provides equal treatment to all cars—the same amount of soap, water, and scrubbing for a given type of wash (e.g., basic wash) —even though cars may differ considerably in cleaning needs.

Product layouts achieve a high degree of labor and equipment utilization, which tends to offset their high equipment costs. Because items move quickly from operation to operation, the amount of work-in-process is often minimal. Consequently, operations are so closely tied to each other that the entire system is highly vulnerable to being shut down because of mechanical failure or high absenteeism. Maintenance procedures are geared to this. Preventive maintenance—periodic inspection and replacement of worn parts or those with high failure rates—reduces the probability of breakdowns during the operations. Of course, no amount of preventive activity can completely eliminate failures, so management must take measures to page 262provide quick repair. These include maintaining an inventory of spare parts and having repair personnel available to quickly restore equipment to normal operation. These procedures are fairly expensive. Because of the specialized nature of equipment, problems become more difficult to diagnose and resolve, and spare-part inventories can be extensive.

Repetitive processing can be machine-paced (e.g., automatic car wash, automobile assembly), worker-paced (e.g., fast-food restaurants such as McDonald’s, Burger King), or even customer-paced (e.g., cafeteria line).

The main advantages of product layouts are:

• A high rate of output.

• Low unit cost due to high volume. The high cost of specialized equipment is spread over many units.

• Labor specialization, which reduces training costs and time, and results in a wide span of supervision.

• Low material-handling cost per unit. Material handling is simplified because units follow the same sequence of operations. Material handling is often automated.

• A high utilization of labor and equipment.

• The establishment of routing and scheduling in the initial design of the system. These activities do not require much attention once the system is operating.

• Fairly routine accounting, purchasing, and inventory control.

The primary disadvantages of product layouts include the following:

• The intensive division of labor usually creates dull, repetitive jobs that provide little opportunity for advancement and may lead to morale problems and to repetitive stress injuries.

• Poorly skilled workers may exhibit little interest in maintaining equipment or in the quality of output.

• The system is fairly inflexible in response to changes in the volume of output or changes in product or process design.

• The system is highly susceptible to shutdowns caused by equipment breakdowns or excessive absenteeism because workstations are highly interdependent.

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• Preventive maintenance, the capacity for quick repairs, and spare-parts inventories are necessary expenses.

• Incentive plans tied to individual output are impractical because they would cause variations among outputs of individual workers, which would adversely affect the smooth flow of work through the system.

U-Shaped Layouts. Although a straight production line may have intuitive appeal, a U-shaped line (see Figure 6.5) has a number of advantages that make it worthy of consideration. One disadvantage of a long, straight line is that it interferes with cross-travel of workers and vehicles. A U-shaped line is more compact; it often requires approximately half the length of a straight production line. In addition, a U-shaped line permits increased communication among workers on the line because workers are clustered, thus facilitating teamwork. Flexibility in work assignments is increased because workers can handle not only adjacent stations but also stations on opposite sides of the line. Moreover, if materials enter the plant at the same point that finished products leave it, a U-shaped line minimizes material handling.

Of course, not all situations lend themselves to U-shaped layouts: On highly automated lines, there is less need for teamwork and communication, and entry and exit points may be on opposite sides of the building. Also, operations may need to be separated because of noise or contamination factors.

Intermittent Processing: Process Layouts

Process layouts (functional layouts) are designed to process items or provide services that involve a variety of processing requirements. The variety of jobs that are processed requires frequent adjustments to equipment. This causes a discontinuous work flow, which is referred to as intermittent processing . The layouts feature departments or other functional groupings in which similar kinds of activities are performed. A manufacturing example of a process layout is the machine shop, which has separate departments for milling, grinding, drilling, and so on. Items that require those operations are frequently moved in lots or batches to the departments in a sequence that varies from job to job. Consequently, variable-path material-handling equipment (forklift trucks, jeeps, tote boxes) is needed to handle the variety of routes and items. The use of general-purpose equipment provides the flexibility necessary to handle a wide range of processing requirements. Workers who operate the equipment are usually skilled or semiskilled. Figure 6.6 illustrates the departmental arrangement typical of a process layout.

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Process layouts are quite common in service environments. Examples include hospitals, colleges and universities, banks, auto repair shops, airlines, and public libraries. For instance, hospitals have departments or other units that specifically handle surgery, maternity, pediatrics, psychiatric, emergency, and geriatric care. And universities have separate schools or departments that concentrate on one area of study such as business, engineering, science, or math.

Because equipment in a process layout is arranged by type rather than by processing sequence, the system is much less vulnerable to shutdown caused by mechanical failure or absenteeism. In manufacturing systems especially, idle equipment is usually available to replace machines that are temporarily out of service. Moreover, because items are often processed in lots (batches), there is considerably less interdependence between successive operations than with a product layout. Maintenance costs tend to be lower because the equipment is less specialized than that of product layouts, and the grouping of machinery permits repair personnel to become skilled in handling that type of equipment. Machine similarity reduces the necessary investment in spare parts. On the negative side, routing and scheduling must be done on a continual basis to accommodate the variety of processing demands typically imposed on these systems. Material handling is inefficient, and unit handling costs are generally much higher than in product layouts. In-process inventories can be substantial due to batch processing and capacity mismatches. Furthermore, it is not uncommon for such systems to have equipment utilization rates under 50 percent because of routing and scheduling complexities related to the variety of processing demands being handled.

In sum, process layouts have both advantages and disadvantages. The advantages of process layouts include the following:

• The systems can handle a variety of processing requirements.

• The systems are not particularly vulnerable to equipment failures.

• General-purpose equipment is often less costly than the specialized equipment used in product layouts and is easier and less costly to maintain.

• It is possible to use individual incentive systems.

The disadvantages of process layouts include the following:

• In-process inventory costs can be high if batch processing is used in manufacturing systems.

• Routing and scheduling pose continual challenges.

• Equipment utilization rates are low.

• Material handling is slow and inefficient, and more costly per unit than in product layouts.

• Job complexities often reduce the span of supervision and result in higher supervisory costs than with product layouts.

• Special attention necessary for each product or customer (e.g., routing, scheduling, machine setups) and low volumes result in higher unit costs than with product layouts.

• Accounting, inventory control, and purchasing are much more involved than with product layouts.

Fixed-Position Layouts

In fixed-position layouts , the item being worked on remains stationary, and workers, materials, and equipment are moved about as needed. This is in marked contrast to product and process layouts. Almost always, the nature of the product dictates this kind of arrangement: Weight, size, bulk, or some other factor makes it undesirable or extremely difficult to move the product. Fixed-position layouts are used in large construction projects (buildings, power plants, dams), shipbuilding, and production of large aircraft and space mission rockets. In those instances, attention is focused on timing of material and equipment deliveries so as not to clog up the work site and to avoid having to relocate materials and equipment around the work site. Lack of storage space can present significant problems, for example, at construction sites in crowded urban locations. Because of the many diverse activities carried out on large projects and because of the wide range of skills required, special efforts are needed to page 265coordinate the activities, and the span of control can be quite narrow. For these reasons, the administrative burden is often much higher than it would be under either of the other layout types. Material handling may or may not be a factor; in many cases, there is no tangible product involved (e.g., designing a computerized inventory system). When goods and materials are involved, material handling often resembles process-type, variable-path, general-purpose equipment. Projects might require use of earth-moving equipment and trucks to haul materials to, from, and around the work site, for example.

Fixed-position layouts are widely used in farming, firefighting, road building, home building, remodeling and repair, and drilling for oil. In each case, compelling reasons bring workers, materials, and equipment to the product’s location instead of the other way around.

Combination Layouts

The three basic layout types are ideal models, which may be altered to satisfy the needs of a particular situation. It is not hard to find layouts that represent some combination of these pure types. For instance, supermarket layouts are essentially process layouts, yet we find that most use fixed-path material-handling devices such as roller-type conveyors in the stockroom and belt-type conveyors at the cash registers. Hospitals also use the basic process arrangement, although frequently patient care involves more of a fixed-position approach, in which nurses, doctors, medicines, and special equipment are brought to the patient. By the same token, faulty parts made in a product layout may require off-line reworking, which involves customized processing. Moreover, conveyors are frequently observed in both farming and construction activities.

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Process layouts and product layouts represent two ends of a continuum from small jobs to continuous production. Process layouts are conducive to the production of a wider range of products or services than product layouts, which is desirable from a customer standpoint where customized products are often in demand. However, process layouts tend to be less efficient and have higher unit production costs than product layouts. Some manufacturers are moving away from process layouts in an effort to capture some of the benefits of product layouts. Ideally, a system is flexible and yet efficient, with low unit production costs. Cellular manufacturing, group technology, and flexible manufacturing systems represent efforts to move toward this ideal.

Cellular Layouts

Cellular Production. Cellular production is a type of layout in which workstations are grouped into what is referred to as a cell. Groupings are determined by the operations needed to perform work for a set of similar items, or part families, that require similar processing. The cells become, in effect, miniature versions of product layouts. The cells may have no conveyorized movement of parts between machines, or they may have a flow line connected by a conveyor (automatic transfer). All parts follow the same route, although minor variations (e.g., skipping an operation) are possible. In contrast, the functional layout involves multiple paths for parts. Moreover, there is little effort or need to identify part families.

Cellular manufacturing enables companies to produce a variety of products with as little waste as possible. A cell layout provides a smooth flow of work through the process with minimal transport or delay. Benefits frequently associated with cellular manufacturing include minimal work in process, reduced space requirements and lead times, productivity and quality improvement, and increased flexibility.

Figure 6.7 provides a comparison between a traditional process layout (6.7A) and a cellular layout (6.7B). To get a sense of the advantage of the cellular layout, trace the movement of an order in the traditional layout (6.7A) that is depicted by the path of the arrow. Begin on the bottom left at Shipping/Receiving, and then follow the arrow to Warehouse, where a batch of raw material is released for production. Follow the path (shown by the arrows) that the batch takes as it moves through the system to Shipping/Receiving and then to the Customer. Now turn to Figure 6.7B. Note the simple path the order takes as it moves through the system.

Several techniques facilitate effective cellular layout design. Among them are the following two:

Single-minute exchange of die (SMED) enables an organization to quickly convert a machine or process to produce a different (but similar) product type. Thus, a single cell can produce a variety of products without the time-consuming equipment changeover associated with large batch processes, enabling the organization to quickly respond to changes in customer demand.

Right-sized equipment is often smaller than equipment used in traditional process layouts, and is mobile, so it can quickly be reconfigured into a different cellular layout in a different location.

Table 6.5 lists the benefits of cellular layouts compared to functional layouts.

TABLE 6.5

A comparison of functional (process) layouts and cellular layouts

The biggest challenges of implementing cellular manufacturing involve issues of equipment and layout and issues of workers and management. Equipment and layout issues relate to design and cost. The costs of work stoppages during implementation can be considerable, as can the costs of new or modified equipment and the rearrangement of the layout. The costs to implement cellular manufacturing must be weighed against the cost savings that can be expected from using cells. Also, the implementation of cell manufacturing often requires employee training and the redefinition of jobs. Each of the workers in each cell should ideally be able to complete the entire range of tasks required in that cell, and often this means being more multiskilled than they were previously. In addition, cells are often expected to be self-managing, and therefore workers will have to be able to work effectively in teams. Managers have to learn to be less involved than with more traditional work methods.

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Group Technology. Effective cellular manufacturing must have groups of identified items with similar processing characteristics. This strategy for product and process design is known as group technology and involves identifying items with similarities in either design characteristics or manufacturing characteristics, and grouping them into part families. Design characteristics include size, shape, and function; manufacturing or processing characteristics involve the type and sequence of operations required. In many cases, design and processing characteristics are correlated, although this is not always the case. Thus, design families may be different from processing families. Figure 6.8 illustrates a group of parts with similar processing characteristics but different design characteristics.

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Once similar items have been identified, items can be classified according to their families. Then, a system can be developed that facilitates retrieval from a database for purposes of design and manufacturing. For instance, a designer can use the system to determine if there is an existing part similar or identical to one that needs to be designed. It may happen that an existing part, with some modification, is satisfactory. This greatly enhances the productivity of design. Similarly, planning the manufacturing of a new part can include matching it with one of the part families in existence, thereby alleviating much of the burden of specific processing details.

The conversion to group technology and cellular production requires a systematic analysis of parts to identify the part families. This is often a major undertaking; it is a time-consuming job that involves the analysis of a considerable amount of data. Three primary methods for accomplishing this are visual inspection, examination of design and production data, and production flow analysis.

Visual inspection is the least accurate of the three but also the least costly and the simplest to perform. Examination of design and production data is more accurate but much more time-consuming. It is perhaps the most commonly used method of analysis. Production flow analysis has a manufacturing perspective and not a design perspective, because it examines operations sequences and machine routings to uncover similarities. Moreover, the operation sequences and routings are taken as givens. In reality, the existing procedures may be far from optimal.

Conversion to cellular production can involve costly realignment of equipment. Consequently, a manager must weigh the benefits of a switch from a process layout to a cellular one against the cost of moving equipment, as well as the cost and time needed for grouping parts.

Flexible manufacturing systems, discussed earlier, are more fully automated versions of cellular manufacturing.

Service Layouts

As is the case with manufacturing, service layouts can often be categorized as product, process, or fixed-position layouts. In a fixed-position service layout (e.g., appliance repair, roofing, landscaping, home remodeling, copier service), materials, labor, and equipment are brought to the customer’s residence or office. Process layouts are common in services due mainly to the high degree of variety in customer processing requirements. Examples include hospitals, supermarkets and department stores, vehicle repair centers, and banks. If the service is organized sequentially, with all customers or work following the same or similar sequence, as it is in a car wash or a cafeteria line, a product layout is used.

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However, service layout requirements are somewhat different from manufacturing layout requirements. The degree of customer contact and the degree of customization are two key factors in service layout design. If contact and customization are both high, as in health care and personal care, the service environment is a job shop, usually with high labor content and flexible equipment, and a layout that supports this. If customization is high but contact low page 270(e.g., picture framing, tailoring), the layout can be arranged to facilitate workers and equipment. If contact is high but customization is low (e.g., supermarkets, gas stations), self-service is a possibility, in which case layout must take into account the ease of obtaining the service, as well as customer safety. If the degree of contact and the need for customization are low, the core service and the customer can be separated, making it easier to achieve a high degree of efficiency in operations. Highly standardized services may lend themselves to automation (e.g., web services, online banking, ATM machines).

Let’s consider some of these layouts.

Warehouse and Storage Layouts. The design of storage facilities presents a different set of factors than the design of factory layouts. Frequency of order is an important consideration. Items that are ordered frequently should be placed near the entrance to the facility, and those ordered infrequently should be placed toward the rear of the facility. Any correlations between items are also significant (i.e., item A is usually ordered with item B), suggesting that placing those two items close together would reduce the cost and time of picking (retrieving) those items. Other considerations include the number and widths of aisles, the height of storage racks, rail and/or truck loading and unloading, and the need to periodically make a physical count of stored items.

Retail Layouts. The objectives that guide design of manufacturing layouts often pertain to cost minimization and product flow. However, with retail layouts such as department stores, supermarkets, and specialty stores, designers must take into account the presence of customers and the opportunity to influence sales volume and customer attitudes through carefully designed layouts. Traffic patterns and traffic flow are important factors to consider. Some large retail chains use standard layouts for all or most of their stores. This has several advantages. Most obvious is the ability to save time and money by using one layout instead of page 271custom designing one for each store. Another advantage is to avoid confusing consumers who visit more than one store. In the case of service retail outlets, especially small ones such as dry cleaners, shoe repair, and auto service centers, layout design is much simpler.

Office Layouts. Office layouts are undergoing transformations as the flow of paperwork is replaced with the increasing use of electronic communications. This lessens the need to place office workers in a layout that optimizes the physical transfer of information or paperwork. Another trend is to create an image of openness; office walls are giving way to low-rise partitions, which also facilitate communication among workers.

Restaurant Layouts. There are many different types of restaurants, ranging from food trucks to posh establishments. Many belong to chains, and some of those are franchises. That type of restaurant typically adheres to a floor plan established by the company. Independent restaurants and bars have their own floor plans. Some have what could be considered very good designs, while others do not. Ed Norman of MVP Services Group, Inc., in Dubuque, Iowa, offers this valuable observation: “The single most important element is process workflow. Food and non-food products should transition easily through the operation from the receiving door to the customer with all phases of storage, pre-preparation, cooking, holding, and service, unimpaired or minimized due to good design.”

Hospital Layouts. Key elements of hospital layout design are patient care and safety, with easy access to critical resources such as X-ray, CAT scan, and MRI equipment. General layout of the hospital is one aspect of layout, while layout of patient rooms is another. The following reading illustrates a safe hospital room of the future.

Automation in Services. One way to improve productivity and reduce costs in services is to remove the customer from the process as much as possible. Automated services is one increasingly used alternative. For example, financial services use ATMs, automated call answering, online banking, and electronic funds transfers; retail stores use optical scanning to process sales; and the travel industry uses electronic reservation systems. Other examples of automated services include shipping, mail processing, communication, and health care services.

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Automating services means more-standardized services and less need to involve the customer directly. However, service standardization brings trade-offs. Generally, costs are reduced and productivity increases, but the lack of customization and the inability to deal with a real person raise the risk of customer dissatisfaction.