CHAPTER OVERVIEW
The terms “convergence” and “collaboration” have been popping up recently in discussions about improving logistics and supply chain management processes and the collection and synchronization of the data that can foster those improvements.
When discussing collaboration in terms of end-to-end logistics and supply chain processes, it is referred to the basics such as real-time contact with carriers, sharing long-term plans with 3PLs, clearly communicating data through collaborative planning with suppliers, etc.
Introduction
Outbound-to-customer logistics systems, also referred to as physical distribution, refer to the processes, systems, and capabilities that enhance an organization’s ability to serve its customers.
Correspondingly, the topic of inbound-to-operations logistics systems refers to the activities and processes that precede and facilitate value-adding activities such as procurement manufacturing and assembly.
Demand Management
Demand management might be thought of as “focused efforts to estimate and manage customers’ demand, with the intention of using this information to shape operating decisions.” Traditional supply chains typically begin at the point of manufacture or assembly and end with the sale of product to consumers or business buyers. The essence of demand management is to further the ability of firms throughout the supply chain—particularly manufacturing through the customer—to collaborate on activities related to the flow of product, services, information, and capital.
Demand management seeks to satisfy customers and solve customer problems such as:
· Gathering and analyzing knowledge about consumers, their problems, and their unmet needs
· Identifying partners to perform the functions needed in the demand chain
· Moving the functions that need to be done to the channel member that can perform them most effectively and efficiently
· Sharing with other supply chain members knowledge about consumers and customers, available technology, and logistics challenges and opportunities
· Developing products and services that solve customers’ problems
· Developing and executing the best logistics, transportation, and distribution methods to deliver products and services to consumers in the desired format
Demand data might be used strategically to enhance an organization’s growth, portfolio, positioning, and investment strategies. As suggested, effective use of demand data can help organizations guide strategic resources in a number of important ways.
Balancing Supply and Demand
The essence of demand management is to estimate and manage customer demand and use this information to make operating decisions.
There are four methods that are commonly used across many industries. Two of those, price and lead time, are referred to as external balancing methods. The other two, inventory and production flexibility, are called internal balancing methods. External balancing methods are used in an attempt to change the manner in which the customer orders in an attempt to balance the supply-demand gap. Internal balancing methods utilize an organization’s internal processes to manage the supply-demand gap. Production flexibility allows an organization to quickly and efficiently change its production lines from one product to another.
The tradeoff here is between production changeover costs and safety stock costs. Inventory is probably the most common, and maybe the most expensive, method used to manage the imbalance between supply and demand. Many organizations produce product to a forecast that includes safety stock to smooth the effects of both demand and lead time variability.
In today’s business environment, companies struggle to meet today’s growth objectives. Volatility in demand has become the norm, and companies understand that they need the ability to quickly calibrate because sustainable demand continues to be more challenging than expected./
Traditional Forecasting
A major component of demand management is forecasting the amount of product that will be purchased, when it will be purchased, and where it will be purchased by customers. Although various statistical techniques exist to forecast demand, the common thread for all forecasts is that they will ultimately be wrong.
Factors Affecting Demand
Two types of demand exist: independent and dependent. Independent demand is the demand for the primary item. Dependent demand is directly influenced by the demand for the independent item. Normally, the demand for independent demand items is known as base demand, that is, normal demand. However, all demand is subject to certain fluctuations.
Normally, the demand for independent demand items is known as base demand, that is, normal demand. However, all demand is subject to certain fluctuations. One type of demand fluctuation is caused by random variation. Random variation cannot be anticipated and is usually the cause to hold safety stocks to avoid stockouts. A second type of demand fluctuation is caused by trend. Trend is the gradual increase or decrease in demand over time for an organization. A third type of demand fluctuation is caused by seasonal patterns. Seasonal patterns will normally repeat themselves during a year for most organizations. Finally, demand fluctuations can be caused by normal business cycles. These are usually driven by the nation’s economy and can be growing, stagnant, or declining.
Forecast Errors
Almost all forecasts will be wrong. Some forecasts will be higher than demand, and some will be lower. Managing the forecasting process requires minimizing the errors between actual demand and forecasted demand. The key to successful forecasting is to choose the technique that provides the least amount of forecast error. To determine which forecasting technique is best for a set of data, the forecast error must be measured.
Four types of forecast error measures can be used. The first is called the cumulative sum of forecast errors (CFE) and can be calculated using the formula below:
n
CFE = ∑et
t−1
CFE calculates the total forecast error for a set of data, taking into consideration both negative and positive errors. This is also referred to as bias and was used in Tables 7-2 through 7-8. This gives an overall measure of forecast error. However, taking into consideration both negative and positive errors, this method can produce an overall low error total although individual period forecasts can either be much higher or much lower than actual demand.
The second measure of forecast error is mean squared error (MSE). This measure is introduced in Table 7-9 and can be calculated using Formula 7.7.
The second measure of forecast error is mean squared error (MSE). This measure is introduced in Table 7-9 and can be calculated using Formula 7.7.
∑ Et2
MSE = ----------
n
This measure squares each period error so the negative and positive errors do not cancel each other out. MSE also provides a good indication of the average error per period over a set of demand data. Closely related to MSE is the third type of forecast error measure: mean absolute deviation (MAD). It can be calculated using Formula 7.8.
∑|Et|
MAD = -----------
n
This measure is also calculated in Table 7-9. By taking the absolute value of each error, the negative and positive signs are removed and a good indication of average error per period is calculated. This measure is popular because it is easy to understand and provides a good indication of the accuracy of the forecast. The final measure of forecast error is mean absolute percent error (MAPE). MAPE can be calculated using Formula 7.9.
(|Et|/Dt)100
MAPE = ---------------
n
Forecasting demand is a highly scientific art. Rigorous quantitative techniques exist to manipulate historical data to predict the future. However, the assumption made here is that the future will repeat the past. This is normally not the case. As such, it is important to choose the technique that best fits the data in order to minimize the forecast error. Minimizing this error will result in the most accurate forecast.
Forecasting Techniques
There are many different statistical techniques companies use to generate forecast. All of these techniques require accurate data and rely on the assumption that the future will repeat the past. However, these requirements are usually violated and the forecast will be generating a forecast error. The key to good forecasting is to minimize forecast error by utilizing a forecasting technique that best fits the nature of the data.
Simple Moving Average
The simple moving average is probably the simplest to develop method in basic time series forecasting. It makes forecasts based on recent demand history and allows for the removal of random effects. The simple moving average method does not accommodate seasonal, trend, or business cycle influences.
Formula:
At = Sum of last n demands
N = Dt + Dt−1 + Dt−2 + … Dt−n+
Where :
Dt = Actual demand in period t
n = Total number of periods in the average
At = Average for period t
Adding together the error terms for the forecasts results in what can be called bias. Bias measures how accurate the forecast is compared to actual demand. A positive bias means that the demand was higher than forecast during the forecast period, resulting in stockouts; a negative bias means the demand was lower than the forecast, resulting in excess inventories. The closer the bias term is to zero, the better the forecast.
Weighted Moving Average
In the simple moving average method, each previous demand period was given an equal weight. The weighted moving average method assigns a weight to each previous period with higher weights usually given to more recent demand.
The formula is:
At = 0.60Dt + 0.25Dt−1 + 0.15Dt−2
Exponential Smoothing
Exponential smoothing is one of the most commonly used techniques because of its simplicity and its limited requirements for data. Exponential smoothing needs three types of data: an average of previous demand, the most recent demand, and a smoothing constant. The smoothing constant must be between 0 and 1. Using a higher constant assumes that the most recent demand is a better predictor of future demand.
This formula is used to calculate the forecast.
At = α (Demand this period) + (1 − α)(Average calculated last period)
= α Dt + (1 − α)At − 1 7.3
Sales and Operations Planning
Many organizations developed several functional forecasts for the same products during the same time period such as a financial forecast, a manufacturing forecast, a marketing forecast, and a distribution forecast. What compounds the complexity of having multiple forecasts is that most times these functional forecasts did not agree. It is necessary for an organization to arrive at a forecast internally that all functional areas agree upon and can execute. A process that can be used to arrive at this consensus forecast is called the sales and operations planning process (S&OP).
The S&OP Benchmarking Consortium in the Center for Supply Chain Research adopted a five step process in arriving at this consensus forecast. The five-step S&OP process can be seen in Figure 7-2.
Collaborative Planning, Forecasting, and Replenishment
Many industry initiatives have attempted to create efficiency and effectiveness through the integration of supply chain activities and processes which has been identified by such names as quick response (QR), vendor-managed inventory (VMI), continuous replenishment planning (CRP), and efficient consumer response (ECR).
One of the most recent initiatives aimed at achieving true supply chain integration is collaborative planning, forecasting, and replenishment (CPFR). CPFR has become recognized as a breakthrough business model for planning, forecasting, and replenishment.
Using this approach, retailers, distributors, and manufacturers can utilize available Internet-based technologies to collaborate on operational planning through execution. Transportation providers have now been included with the concept of collaborative transportation management (CTM). Simply put, CPFR allows trading partners to agree to a single forecast for an item where each partner translates this forecast into a single execution plan. This replaces the traditional method of forecasting where each trading partner developed its own forecast for an item and each forecast was different for each partner.
The CPFR process begins with the sharing of marketing plans between trading partners. Once an agreement is reached on the timing and planned sales of specific products, and a commitment is made to follow that plan closely, the plan is then used to create a forecast, by stock-keeping unit (SKU), by week, and by quantity.
Theoretically, an accurate CPFR forecast could be translated directly into a production and replenishment schedule by the manufacturer since both quantity and timing are included in the CPFR forecast. This would allow the manufacturer to make the products to order (based on the quantity and timing of demand) rather than making them to inventory, thus reducing total inventories for the manufacturer.
SUMMARY
· Outbound-to-customer logistics systems have received the most attention in many companies; but, even in today’s customer service environment, outbound and inbound logistics systems must be coordinated.
· Demand management may be thought of as “focused efforts to estimate and manage customers’ demand, with the intention of using this information to shape operating decisions.
· Although many forecasts are made throughout the supply chain, the forecast of primary demand from the end user or consumer will be the most important. It is essential that this demand information be shared with trading partners throughout the supply chain and be the basis for collaborative decision making.
· Various approaches to forecasting are available, each serving different purposes. The S&OP process has gained much attention in industry today. It serves the purpose of allowing a firm to operate from a single forecast.
· The S&OP process is a continual loop involving participation from sales, operations, and finance to arrive at an internal consensus forecast.
· CPFR is a method to allow trading partners in the supply chain to collaboratively develop and agree upon a forecast of sales. This allows for the elimination of inventories held because of uncertainty in the supply chain.
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