written report

Module 5

Forecasting

Introduction:

Forecasts of future demand are needed at all levels of organisational decision-making. Operations managers need reliable estimates of the demand for goods and services, as well as estimates of the resources necessary to produce those goods and services and the time required to do so. Forecasting demand for services is just as important as forecasting demand for manufacturing products, especially when heavy capital investment is needed to provide those services. For example, how could airlines plan their purchases of aeroplanes without forecasts of demand for air travel?

Forecasting methods can be classified as either quantitative (statistical) or qualitative (judgmental).

Quantitative forecasting methods

Quantitative forecasting uses historical data and is based on the assumption that current trends will continue into the future. For example, if we need to forecast weekly sales of a product where it is reasonable to assume that recent trends will continue into the future, a quantitative technique should be used. This type of forecasting is based on the observation of a pattern in levels of demand over time. This pattern is known as a time series. There are five basic patterns of time series:

· Historical—this illustrates an average level of demand.

· Trend—a trend is represented by an upward or downward slope.

· Cyclical—business cycles often span four-to-eight years, while life cycles vary widely in duration. It is difficult to quantitatively address this component because a sufficient data history is rarely available.

· Seasonal—this usually repeats annually, but could be based on a day, week or month.

· Random—the magnitude of this component means it cannot be precisely forecast at a given time. However, the significance of the random factor's size can be observed, and it may be possible to predict the bounds of the random factor, which can help to determine the reliability of a forecast.

Any forecast based on a pattern of demand relies on that pattern continuing in the future. When using such forecasts it is important to identify the conditions that existed when the data were collected, and compare these against the conditions that now exist and those that are likely to exist in the future. The factors that cause changes in the patterns of demand for a particular product or service over time can be divided into two categories—external and internal:

· External factors are the ones management cannot directly control. Some economic factors, such as a booming economy, may influence demand, but their influence may not be equal for all products and services. Other factors may positively affect one product while reducing the demand for another. When interpreting statistical indicators it is useful to be able to distinguish between leading, coincident and lagging indicators; and

· Internal factors are easier for management to control. Examples include advertising and promotions to encourage customers to make purchases during off-peak demand periods, and developing products that have different seasonal peak demands in order to level production resource requirements.

The timing of demand is important for the efficient use of resources and production capacity. In terms of services, schedules of available capacity should ideally be matched to demand for services. When demand exceeds capacity, the lead-time to access a service lengthens until customers complain.

Long-range quantitative forecasting

For long-range forecasts (involving timeframes of more than one year), it is not sufficient to simply observe patterns of behaviour and analyse their likely causes. In this case, statistical techniques are necessary to quantify probable future demand. Three methods are commonly used to conduct long-range forecasts – simple linear regression analysis, multiple regression analysis and the coefficient of correlation. Linear regression analysis enables estimation of statistical relationships between variables. It is a causal method that relates a dependant variable to one or more independent variables by linear equation. The independent variables might be the external or internal variables that are assumed to affect demand. Multiple regression analysis is a more powerful extension of linear regression and reflects the relationship between a dependant variable and two or more independent variables. The coefficient of correlation is the statistical means of quantifying the relative importance of the relationship between two variables.

Short-range quantitative forecasting

Operations managers also need to make short-term forecasts, such as the number of workers to schedule next week, or the amount of inventory to order for next month. These are affected not so much by trend patterns and cycles as by random or short-term factors. Different methods of forecasting are therefore required. Common methods in this regard include the simple moving average and weighted-moving average methods, the exponential smoothing method and the adjusted exponential smoothing. The simple moving average method involves finding an average of the data from a few recent periods, and using the average as your forecast for the next period. The exponential smoothing method involves taking the forecast from the previous period and finding how much it varies from reality, and then multiplying this error by a 'smoothing constant'. The result is then added to the actual amount for the previous period to create the forecast.

Qualitative forecasting methods

However, if historical data is not available or current trends are likely to change, qualitative forecasting based on judgment and opinion may be used. For example, if we need to forecast the length of time before a current technology becomes obsolete, we may base such a forecast on the opinions and expertise of people who are knowledgeable about changing technology. Such techniques include market research, surveys, opinions and the Dephi method. Qualitative forecasting methods may also be used in combination with quantitative methods to adjust quantitative forecasts when their record of accomplishment is poor, when the decision maker has important contextual knowledge or to compensate for specific events.

Selecting a forecasting method

It is important to select the forecasting method carefully, and to control the method you choose. Failure to do so can produce ineffective forecasting systems, Gaither and Frazier (2002: 92–95) suggest the following factors are imperative in the selection of a forecasting method:

· Cost and accuracy—a trade-off is sometimes necessary between these two factors.

· Data available—this will determine which type of qualitative or quantitative forecasting method should be used.

· Time span—long-range forecasts have different factors to short-range forecasts.

· Nature of products and services—different forecasting methods are appropriate for different products and services.

· Impulse response and noise dampening—the performance of the selected model must be continuously tracked because forecasting models differ in their impulse response and noise dampening capabilities.

For deciding when to use quantitative methods and when to use qualitative methods, the following guidelines are helpful:

· Short-term forecasts - Quantitative time series methods are commonly used in the short-term as it is inexpensive to generate large numbers of forecasts, and good quality results (minimal errors) can be obtained. Causal models are not used as extensively for short-term forecasts. Whilst they are more accurate than simple time series forecasts, they take more time to develop, they are less likely to be understood and used, and they require more training. Qualitative judgment methods are rarely used. They are too costly to apply to thousands of routine short-term forecasts.

· Medium-term forecasts - Quantitative time series methods should not be used for medium-term forecasts, as it is unlikely that existing patterns will continue very far into the future. Causal models are most often used in the medium-term, as they are better at identifying turning points in trends. Qualitative judgment methods can also identify turning points and can be used when historical data are not available.

· Long-term forecasts - For long-term forecasts, aggregate demand for a product family expressed in homogeneous units, such as dollars of sales or tonnes of steel is more appropriate than forecasts for individual products or services. Causal models (adjusted for judgment) and judgment methods are preferred for long-term forecasting.

Essential Resources:

Min, H. & Wen-Bin, V.Y. (2008).  Collaborative Planning, Forecasting and Replenishment: Demand Planning in Supply Chain Management.  International Journal of Information Technology and Management, 7(1), 4-20.

This article looks at forecasting from a whole of supply chain perspective.

Acar, Y. & Gardner, E.S. (2012).  Forecasting method selection in a global supply chain. International Journal of Forecasting, 28, 842-848.

This article examines appropriate forecasting methods from a global supply chain perspective.

Weatherford, L.R. & Kimes, S.E. (2003).  A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting, 19, 401-405.

This article compares a number of forecasting methods in the hotel industry.

Rai. B. (2016). Introduction to forecasting –With Examples. YouTube. Retrieved from  https://www.youtube.com/watch?v=98K7AG32qv8&feature=youtu.be

This quite substantial video will provide you with a thorough understanding of forecasting including types of forecasting techniques and forecasting accuracy.

Capacity Planning

Introduction:

Capacity planning is a crucial element of operations strategy because it has major cost and operational implications. However, capacity decisions are made when there is uncertainty about future demand. Capacity decisions depend on the growth (or decline) of demand for products and services, characterised by the product life cycle.

Capacity decisions are aimed at using an organisation's resources to maximise long-term profit while meeting cash-flow requirements. The cost of potential excess capacity must be balanced against the cost of potential lost sales due to too little capacity. Excess capacity costs money, especially for capital-intensive organisations such as paper mills, utilities and steel mills. However, too little capacity can result in the inability to meet customer demand, particularly in service industries. Tight capacity does allow for higher equipment utilisation and a better return on investment.

Gaither and Frazier (2002: 165) have identified four activities that are relevant to capacity-planning decisions:

· Estimating the capacities of the present facilities

· Forecasting the long-range future capacity needs for all products and services

· Identifying and analysing sources of capacity to meet future capacity needs

· Selecting from the alternative sources of capacity.

These activities and the important issues attached to them will be discussed in the remainder of this module.

Production capacity

Gaither and Frazier (2002: 165) define production capacity as 'the maximum production rate of an organisation'. However, organisations rarely operate at maximum rates of production, even when there are strong reasons for doing so. Capacity is measured in different ways, depending on the nature of the goods or services and other operational factors. Two useful concepts are:

· Theoretical capacity—the maximum output capability possible, allowing no adjustments for preventive maintenance, unplanned downtime, shutdowns and so on; and

· Demonstrated capacity—the proven capacity calculated from actual output performance data. Demonstrated capacity is generally less than theoretical capacity when production losses due to machine breakdowns, rework, sick time and so on are taken into account.

Some measures of capacity

Capacity can be expressed in terms of outputs or inputs. It is important to note that no single capacity measure is universally applicable. Commonly used methods include:

· Output measures—these are the usual choice for line flow processes and are best when there is a low amount of customisation. Product mix becomes an issue when the output is not uniform in work content.

· Input measures—these are used for flexible flow processes, i.e. when there is a high amount of customisation. In this situation, output is not a measure of the amount of work each unit demands, so a measure of total units produced is meaningless.

· Average utilisation rate - a measure of the average output rate against maximum capacity.

· Peak capacity—when we call for peak capacity we are really calling for extraordinary effort under ideal conditions, which are not sustainable. When capacity is measured relative to equipment alone, the appropriate measure is the nameplate-rated capacity, engineering design.

· Effective capacity—this is the capacity that can be economically sustainable under normal conditions. The effective capacities of multiple operations within the same facility are different.

· Bottlenecks—A bottleneck is '… an operation that has the lowest effective capacity of any operation in the process and thus limits the system's output' (Krajewski & Ritzman, 2002: 328). Expansion of a facility's capacity occurs only when bottleneck capacity is increased. Flexible flow processes may have floating bottlenecks due to widely varying workloads on different operations at different times.

Capacity planning strategy

Creating effective capacity-planning strategies involves the following:

· Assessing existing capacity

· Forecasting future capacity requirements

· Choosing between alternative ways to build capacity

· Evaluating the financial options

In developing a long-range capacity plan, an organisation must make a basic economic trade-off between the cost of capacity and the opportunity cost of not having adequate capacity. Capacity cost includes both the initial investment in facilities and the annual cost of operating and maintaining those facilities. The cost of not having sufficient capacity is the opportunity cost incurred from lost sales and reduced market share. Capacity strategy is therefore concerned with how much 'capacity cushion' is best for various processes. Conditions leading to large capacity cushions include:

· where the demand is variable or uncertain, or where product mix changes

· where finished goods inventory cannot be stored

· where customer service is important

· where capacity comes in large increments

· where supply of material or human resources is uncertain.

The concept of economies of scale is central to capacity planning. According to this concept, for any given production facility there will be a level of outputs that result in the least average unit cost. This level of output is called the facility's 'best operating level'. As the best operating level is approached for a particular facility, economies of scale are achieved. Arguments based on economies of scale are often used to justify large facilities. When economies of scale occur:

· Fixed costs are spread further—as the facility utilisation rate increases, the average unit cost drops because fixed costs are spread over more units. Increments of capacity are often rather large.

· Purchased materials may be cheaper—higher volumes give the purchaser more bargaining power and the opportunity for quantity discounts.

· Process advantages may develop—as volume increases, processes shift toward a line flow. High volume may justify investment in more efficient technology. Benefits of dedicated resources include reduced inventory, reduced set-up costs, enhanced learning effects and process improvements.

There are disadvantages, however, to large facilities, or diseconomies of scale. A larger workforce requires more supervisors and managers, leading to a higher bureaucracy. Large facilities can also lead to the following diseconomies:

· Excessive size can bring complexity, loss of focus and inefficiencies, which raise the average unit cost.

· Large facilities are characterised by loss of agility, less innovation, risk avoidance and excessive analysis and planning at the expense of action.

Economies of scope result when the value chains of two separate products or services share activities, such as the same distribution channels or the same manufacturing facilities. For example, if a particular product is not being produced at a high enough level to reach economies of scale in distribution, another product could be made to share the same distribution channel. Gaither & Frazier (p. 173) define economies of scope as: ' ... the ability to produce many product models in one highly flexible production facility more cheaply than in separate production facilities.'

Capacity planning decisions

Capacity cushions, resource flexibility, surplus inventory and longer lead times all serve as buffers against uncertainty. In turn, an organisation's competitive priorities will demand certain capacity decisions be made; and a change in one area may affect decisions in the other areas. For example:

· Where a competitive priority is fast delivery, this requires large capacity cushions.

· Where quality management is a priority, uncertainty will be reduced and capacity may be affected.

· Where schedules are predictable, uncertainty will again be reduced and a smaller cushion may be allowed.

· Where capital expenditure is high, there will be pressure for high utilisation, and a lower capacity cushion.

· Where a capacity decision is made to build another facility, a suitable location will need to be found.

The following four-step procedure can help managers make capacity planning decisions:

· Step 1. Estimate capacity requirements—Begin with a long-range forecast of demand, productivity, competition and technological change (remembering that long-range forecast errors will be large), then convert demand into comparable units of capacity.

· Step 2. Identify gaps—A 'gap' is the difference between projected demand and current capacity. It will be important to use the correct capacity measure, which is determined by what is critical to the bottleneck operation. Capacity can be expanded only if the bottleneck is one of the expanded operations. Otherwise, expansion just increases idle time. Multiple operations and inputs add complexity because floating bottlenecks could change the dimensions of the capacity measure.

· Step 3. Develop options—The base case is to do nothing. Beyond that, options include varying the timing and size of capacity additions/closings. You could consider an expansionist strategy, a 'wait-and-see' strategy, or try expanding at a different location. You could also consider short-term options, e.g. overtime, temporary workers or subcontracting.

· Step 4. Evaluate the options—Qualitative judgments include how each option fits with overall capacity planning strategy, and how it can affect uncertainties in demand, competitive reaction, technological change and cost. Possible quantitative judgment aids include net present value of after-tax cash flows, computer simulation, waiting line analysis and linear programming.

Decision tree analysis techniques can assist decision makers in selecting from several capacity alternatives with uncertain future outcomes. They are valuable for capacity planning decisions when demand is uncertain and when sequential decisions are involved.

Essential Resources:

Ashayeri, J. & Selen, W. (2018).  An application of a unified capacity planning system.  International Journal of operations & Production Management, 25(9), 917–937.

This article examines a unified approach for effective capacity management and finds that this approach not only reduced the number of capacity problems but also enhanced organisational capabilities.

Klassen, K.J & Rohleder, T.R. (2002).  Demand and capacity management decisions in services: how they impact on one another.  International Journal of Operations and Production Management, 22(5/6), 527 –549.

This article’s findings are based on modelling the impact of automation, customer participation, cross-training employees, informing customers about the operation and other factors, showing that demand and capacity decisions do indeed impact on each other, sometimes in ways that are not initially obvious

Poles, R. (2013). System Dynamics modelling of a production and inventory system for remanufacturing to evaluate system improvement strategies.  International Journal of Production Economics , 144(1), 189 –199.

This article uses a systems dynamics approach to suggest improvements in a reverse supply situation. At the end of the useful life of products, a reverse supply process is activated by some organisations in which unwanted materials and products are recovered from end users to recapture some of their value.

VanBerkel, P.T. & Blake, J.T. (2007).  A comprehensive simulation for wait time reduction and capacity planning applied in general surgery.  Health care Management Science, 10(4), 373–385.

This paper describes the use of operational research techniques to analyse the wait list for a hospital general surgery unit.

Module 6

Materials and Inventory Management

Introduction:

Supply chain management (SCM) is equivalent to inter-company operations management in that it involves cooperation and close coordination of various operations of suppliers and the receiving organisation. A prime objective is a speedy flow of materials along the supply chain, and to this end, 'just-in-time' principles have been widely adopted.

Supply chain management is a logical next step after first forecasting demand and planning capacity, and then making facility location and layout decisions. The ability to plan capacity strategically is central to the long-term efficiency and effectiveness of the supply chain. The cost of excess capacity must be balanced against the cost of potential lost sales due to having too little capacity. Likewise, location and layout decisions will affect logistical efficiency that is achievable in the supply chain. A key objective of supply chain management is to align an organisation's functions with those of its suppliers in order to match the flow of materials, services and information with customer demand. All members of an organisation's supply chain have a mutual interest in identifying what competitive factors (e.g. cost, flexibility, speed of delivery) customers value, and then in maximising the performance of the supply chain as a whole so as to deliver maximum value.

A large component of supply chain management is the efficient management of materials. This involves planning, coordinating and controlling the acquisition, storage, handling and movement of raw materials that are needed for the production process. There are three components to materials management:

· The management of raw materials and purchased parts (including purchasing, receiving, storage and retrieval of these materials);

· The management of finished goods (including packaging and shipping, storage and retrieval in warehouses, and distribution to the customer); and

· The management of materials during the conversion process (i.e. handling and storage of work-in-process inventories).

We will discuss methods of planning and scheduling future production levels over a time horizon of several months to one year. These methods will be applied to large manufacturing organisations, but they are also useful for both smaller organisations and service organisations.

For large manufacturing organisations in particular, there are several tasks contained within planning activities. First, working from long-range capacity plans involving facility locations, layouts and capacities, aggregate plans are drawn up to estimate and allocate resources over the medium-term to accomplish specific tasks. Then, production schedules are drawn up to meet the forecasted levels of demand. The workload that the specified levels of production will entail at various workstations is calculated, and action is taken as required to either augment (or reduce) resources at workstations, or to revise the schedule. Finally, the progress of jobs is monitored using planning and control systems to ensure that the planned schedule is in fact being achieved.

Some decades ago, the Japanese recognised that all stocks and work-in-progress represented waste, i.e. non-productive resources and introduced a manufacturing system whereby organisations aim to produce all parts in the right quantity and quality, just in time to meet usage requirements at the next stage of the production–distribution chain. This principle of manufacturing efficiency (fast throughput of materials) underpinning assembly line and repetitive batch production techniques has spread worldwide. The Just in Time (JIT) philosophy is concerned with recognising and tackling the real inefficiencies in the system, not 'optimising' the status quo. In repetitive, batch production environments in the past, there has been an emphasis (perhaps even an over-emphasis) on maintaining enough work-in-progress (WIP) to prevent operators and machines from falling idle. Cost accounting practices have stressed the need for high utilisation of the available direct labour hours and high (productive) utilisation of capital equipment. The overall effect of this thinking has been that items have been spending most of their manufacturing time in queues, or in stores, and the general workflow has been intermittent and lumpy.

The logical starting point for discussing supply chain management is the sourcing of raw materials and purchased parts. Critical issues to consider include whether it is better to buy-in or make items that are used in the supply chain; and whether a single supplier or multiple suppliers should be used. The purchasing, or procurement, function is responsible for acquiring raw materials, component parts, tools and other items required from outside suppliers. The purchasing function in any organisation acts as an interface between suppliers and the production function. Since materials are one of the largest sources of cash outlay in any manufacturing organisation, their acquisition requires careful management.

Essential Resources:

Cucchiella, F. & Gastaldi, M. (2006).  Risk management in supply chain: a real option approach.  Journal of Manufacturing Technology Management, 17(6), 700 –720.

Please read this article to gain a better understanding of individualising a framework for the management of risk.

Yasin, M.M., Wafa, M.A. & Small, M.H. (2001).  Just-in-time implementation in the public sector: An empirical examination.  International Journal of Operations and Production Management, 21(9), 1195 –1204.

This article explores the adoption of JIT in the US public sector and examines the relationships between modification efforts and any problems encountered.

Webster, M., Muhlemann, A.P. & Alder, C. (2000).  Decision support for the scheduling of subcontract manufacture.  International Journal of Operations & Production Management, 20(10), 1218 –1235.

This article presents work that addresses the issue of decision support for operational management and will help you understand the motivations and implications of decisions on the operational environment.

Leverage Points for System Intervention

Introduction:

We conclude this module, and this course, by considering how we can go about changing the structure of systems to produce more of what we want and less of that which is undesirable. The ‘trick’ is to look for leverage points – places in the system where small change could lead to a large shift in behaviour. In considering how to influence the behaviour of a system, Meadows (2008) has identified twelve leverage points ranging from ‘shallow’—places where interventions are relatively easy to implement yet bring about little change to the overall functioning of the system—to ‘deep’ leverage points that might be more difficult to alter but potentially result in transformational change:

12. Numbers - Constants and parameters (like subsidies, taxes, standards, minimum wage levels, investments, etc.) define the rate at which things happen in the system. Parameters are points of lowest leverage effects. Though they are the most clearly perceived among all leverages, they rarely change behaviours and therefore have little long-term effect. In operations management a parameter such as the production rate is easy to change but may not bring significant improvement as it is limited by the capacity of the plant. Another intervention might be to fir existing staff and hire new employees. This will not result in significant long-term improvement, however, if production technology is outdated.

11. Buffer Sizes – Another common leverage point is to stabilise a system by increasing the capacity of a buffer. A buffer’s ability to stabilise a system is important when the stock amount is much higher than the potential amount of inflows or outflows; ie: big buffers make the system more stable, small buffers make it more subject to change. A good example of a buffer is the money you keep in the bank - it helps you manage exceptional expenses. This has been a traditional business intervention, where in order to meet unexpected customer needs, businesses maintain inventory. However, building and maintaining inventory involves significant cost and storage.

10. Material Flows Structure - This represent the structure of the system itself, how material stocks move through the system itself. In order to improve product flow, for example, in modern manufacturing setups cellular layout is considered better than functional layouts. Real leverage in physical layout can occur during its design, where options can be properly evaluated in order to choose the optimal one. Once a structure is in place, it is hard to change and the leverage is in understanding its limitations and bottlenecks, using it with maximum efficiency and refraining from fluctuations or expansions that strain its capacity.

9. Delays – they determine how much time passes between the moment a change is made on the system and the moment when the effect of the change happens. Long delays make things challenging, so being able to shorten this time is beneficial. Changing delays can have a big impact but, similar to material flows structure, they are very hard to change. For example, if there are constant power shortages, then a new power plant needs to be constructed, which will take time. Delays in feedback loops are critical determinants of system behaviour. They are common causes of oscillations. If you are trying to adjust a stock level, for example, but receive only delayed information about what state the stock is in, you will either overshoot or undershoot your goal. In supply chain management this results in what is called the Bullwhip effect, where delays in communication of customer demands upstream can create havoc for all players in the supply chain and result in inventory build-up from distributor up to the manufacturer. The same is true if information is timely but your response isn’t.

8. Negative Feedback Loops - A negative feedback loop is a self-correcting logic composed of three elements: a goal a monitoring element and a response mechanism. It is a mechanism that tries to keep a specific measurement around a specific goal. For example a thermostat has a goal temperature and it turns heating on to keep that temperature. Societal feedback loops are often harder to discern. For example, a law that grants more protection for whistle blowers is something that makes the feedback loop that controls the neutrality of a democracy stronger.

7. Positive Feedback Loops - Positive feedback loops are similar to negative feedback loops, but instead of keeping a variable stable around a goal, they aim to reinforce it - the more it works, the more it gains power, driving system behaviour in one direction. For example giving bonuses for every sale is an incentive to sell more (even if we know that it damages the system as a whole more than the benefits of it), or the more you have in the bank the more interest you earn. Positive feedback loops are usually perceived as positive, but since they keep growing they can build up and damage the system in the long run if they aren’t controlled in some other way

6. Information Flows Structure - Information flow creates new positive or negative feedback loops. Missing information flows are among the most common causes of system malfunction. For example, if you place an energy counter in clear view then you and your family are more aware of how much is being consumed and the effect is, generally, that you will consume less. This creates a new negative feedback loop without changing any other parameter in the system. It is cheaper and easier to change information flows than it is to change structure.

5. Rules - The rules of a system define its scope, boundaries and degrees of freedom. Incentives, punishments and constraints are all system rules and are strong leverage points. If you want to understand the deepest malfunctions of a system pay attention to the rules and to those who have power over them. A classic example is the recent Royal Commission on banks where it was found that the way in which bank executives twisted bank policies and procedures encouraged erratic behaviour and greed and led to serious and significant financial bungling.

4. Self-organisation – Self-organisation describes a system's ability to change itself by creating new structures, adding new negative and positive feedback loops, promoting new information flows, or making new rules. This generally results from technological advancements or social revolution. Structural transformation of the system is usually due to new elements appearing (eg. computerisation). Disruptive innovations are a significant trigger for this variable. These are innovative products and services that create a new market and value network and eventually disrupt an existing market and value network, displacing established market-leading firms, products, and alliances. Not all innovations are disruptive, even if they are revolutionary. For example, the first automobiles in the late 19th century were not a disruptive innovation, because early automobiles were expensive luxury items that did not disrupt the market for horse-drawn vehicles. The market for transportation essentially remained intact until the debut of the lower-priced Ford Model T in 1908. The mass-production of automobiles was a disruptive innovation, because it changed the transportation market.

3. Goals - Goals have the power to transform and define each and every leverage point above. If you’re creating a system, like an organization, it’s relatively easy to see the goals because usually there’s someone to set them, and if there isn’t, then the organization is likely to have a problem. Leaders, managers, heads of state, have the power to modify or set new goals.

2. Context Paradigms - A paradigm is an idea, a shared unstated assumption, or a system of thought that is the foundation of complex social and business structures. Paradigms are the sources of systems. From them, from shared social agreements about the nature of reality come system goals and information flows, feedbacks, stocks, flows, and everything else about systems. Paradigms are very hard to change and can generally only be done by pointing out anomalies and failures. Intervention at the paradigm level will totally transform a system.

1.Transcending Paradigms – This involves realising that no paradigm is ‘true’ – every one (including the one that shapes our own worldview) involves a limited understanding of the operation of world that is far beyond human comprehension. It is also involves understanding that to think in paradigms is itself a paradigm. Transcending paradigms may go beyond challenging fundamental assumptions, into the realm of changing the values and priorities that lead to the assumptions, and being able to choose among value sets at will.

Meadows’ leverage points can be aggregated into four broad types of system characteristics that interventions can target (from shallowest to deepest) - parameters, feedbacks, design and intent, as outlined below:

Source: Abson, D.J., Fischer, J., Leventon, J., Newig, J., Schomerus, T., Vilsmaier, U., von Wehrden, H., Abernethy, P., Ives, C.D., Jager, N.W. and Lang, D.J. (2017). Leverage points for sustainability transformation, Ambio, 46, pp.30 – 39 at page 32.

Essential Resources:

readings icon

Hidbrand, S. & Bodhanya, S. (2015). Guidance on applying the Viable system model. Kybernetes. 44(2), 186 –201.

This article helps student understand the constituent elements and essential characteristics of a complex entity using Stafford Beer’s Viable System Model (VSM) and then explains how the model as a diagnostic tool to assess the organization’s viability as per VSM

Panagiotakopoulos, P. Espinosa, A, & Walker, J. (2016). Sustainability management: insights from the viable System Model. Journal of Cleaner production, 113, 792 –806.

This article explains the use of Viable System Model in managing sustainability in organization. The author explains the application of the model using a case study of operations of an organisation from the perspective of sustainability.

Pfifner, M. (2000). Five experiences with the viable system model. Kybernetes, 39(9/10), 1615 –1626.

This article documents five practical applications of the Viable System Model to see organization from no-reductionist and holistic perspective of systems thinking. This article also discusses the importance of information sharing in developing effective supply chains.

Azadeh, A., Darivandi, K. & Fathi, E. (2012). Diagnosing, Simulating and Improving Business Process using Cybenetic Laws and the Viable System Model: The Case of a purchasing process. Systems Research and Behavioural Science, 29, 66–86.

This article discusses the application of the Viable System Model in improving business process with some practical examples.

Bellinger, G. (2012). EaBT/Leverage Points. YouTube. Retrieved from https://www.youtube.com/watch?v=rZX1G5JMMDA

This video succinctly explains the systemic leverages where interventions create the changes sought to be achieved.