Starbucks forecasting methods

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18 FORECASTING Learning Objectives LO18–1 Understand how forecasting is essential to supply chain planning. LO18–2 Evaluate demand using quantitative forecasting models. LO18–3 Apply qualitative techniques to forecast demand. LO18–4 Apply collaborative techniques to forecast demand.

FROM BEAN TO CUP: STARBUCKS GLOBAL SUPPLY CHAIN CHALLENGE Starbucks Corporation is the largest coffeehouse company in the world with over 17,000 stores in more than 50 countries. The company serves some 50 million customers each week. Forecasting demand for a Starbucks is an amazing challenge. The product line goes well beyond drip-brewed coffee sold on demand in the stores. It includes espresso-based hot drinks, other hot and cold drinks, coffee beans, salads, hot and cold sandwiches and panini, pastries, snacks, and items such as mugs and tumblers. Through its entertainment division and the Hear Music brand, the company also markets books, music, and videos. Many of the company’s products are seasonal or specific to the locality of the store. Starbucks-branded ice cream and coffee are also offered at grocery stores around the world.

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STARBUCKS CAFE IN BUR JUMAN CENTER SHOPPING MALL, DUBAI, UNITED ARAB EMIRATES. The creation of a single, global logistics system was important for Starbucks because of its far-flung supply chain. The company generally brings coffee beans from Latin America, Africa, and Asia to the United States and Europe in ocean containers. From the port of entry, the “green” (unroasted) beans are trucked to six storage sites, either at a roasting plant or nearby. After the beans are roasted and packaged, the finished product is trucked to regional distribution centers, which range from 200,000 to 300,000 square feet in

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443 size. Starbucks runs five regional distribution centers (DCs) in the United States. It has two DCs in Europe and two more in Asia. Coffee, however, is only one of the many products held at these warehouses. They also handle other items required by Starbucks retail outlets, everything from furniture to cappuccino mix.

In the Analytics Exercise at the end of the chapter, we consider the challenging demand forecasting problem that Starbucks must solve to be able to successfully run this complex supply chain.

Source: Based on James A. Cooke, “From bean to cup: How Starbucks transformed its supply chain,” CSCUPs Supply Chain Quarterly, Fourth Quarter, 2010.

LO18–1 Understand how forecasting is essential to supply chain planning.

FORECASTING IN OPERATIONS AND SUPPLY CHAIN MANAGEMENT Forecasts are vital to every business organization and for every significant management decision. Forecasting is the basis of corporate planning and control. In the functional areas of finance and accounting, forecasts provide the basis for budgetary planning and cost control. Marketing relies on sales forecasting to plan new products, compensate sales personnel, and make other key decisions. Production and operations personnel use forecasts to make periodic decisions involving supplier selection, process selection, capacity planning, and facility layout, as well as for continual decisions about purchasing, production planning, scheduling, and inventory.

In considering what forecasting approach to use, it is important to consider the purpose of the forecast. Some forecasts are for very high-level demand analysis. What do we expect the demand to be for a group of products over the next year, for example? Some forecasts are used to help set the strategy of how, in an aggregate sense, we will meet demand. We will call these strategic forecasts. Relative to the material in the book, strategic forecasts are most appropriate when making decisions related to overall strategy (Chapter 2), capacity (Chapter 5), manufacturing process design (Chapter 7), service process design (Chapter 9), location and distribution design (Chapter 15), sourcing (Chapter 16), and in sales and operations planning (Chapter 19). These all involve medium and long-term decisions that relate to how demand will be met strategically.

Strategic forecasts Medium and long-term forecasts that are used for decisions related to strategy and aggregate demand.

Forecasts are also needed to determine how a firm operates processes on a day-to-day basis. For example, when should the inventory for an item be replenished, or how much production should we schedule for an item next week? These are tactical forecasts where the goal is to estimate demand in the relatively short term, a few weeks or months. These forecasts are important to ensure that in the short term we are able to meet customer lead time expectations and other criteria related to the availability of our products and services.

Tactical forecasts Short-term forecasts used for making day-to-day decisions related to meeting demand.

In Chapter 7, the concept of decoupling points is discussed. These are points within the supply chain where inventory is positioned to allow processes or entities in the supply chain to operate independently. For example, if a product is stocked at a retailer, the customer pulls the item from the shelf and the manufacturer never sees a customer order. Inventory acts as a buffer to separate the customer from the manufacturing process. Selection of decoupling points is a strategic decision that determines customer lead times and can greatly impact inventory investment. The closer this point is to the customer, the quicker the customer can be served. Typically, a trade-off is involved where quicker response to customer demand comes at the expense of greater inventory investment because finished goods inventory is more expensive than raw material inventory.

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Strategy Forecasting is needed at these decoupling points to set appropriate inventory levels for these buffers. The actual setting of these

levels is the topic of Chapter 20, “Inventory Management,” but an essential input into those decisions is a forecast of expected demand and the expected error associated with that demand. If, for example, we are able to forecast demand very accurately, then inventory levels can be set precisely to expected customer demand. On the other hand, if predicting short-term demand is difficult, then extra inventory to cover this uncertainty will be needed.

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FORECASTING IS CRITICAL IN DETERMINING HOW MUCH INVENTORY TO KEEP TO MEET CUSTOMER NEEDS.

The same is true relative to service settings where inventory is not used to buffer demand. Here capacity availability relative to expected demand is the issue. If we can predict demand in a service setting very accurately, then tactically all we need to do is ensure that we have the appropriate capacity in the short term. When demand is not predictable, then excess capacity may be needed if servicing customers quickly is important.

Bear in mind that a perfect forecast is virtually impossible. Too many factors in the business environment cannot be predicted with certainty. Therefore, rather than search for the perfect forecast, it is far more important to establish the practice of continual review of forecasts and to learn to live with inaccurate forecasts. This is not to say that we should not try to improve the forecasting model or methodology or even to try to influence demand in a way that reduces demand uncertainty. When forecasting, a good strategy is to use two or three methods and look at them for the commonsense view. Will expected changes in the general economy affect the forecast? Are there changes in our customers’ behaviors that will impact demand that are not being captured by our current approaches? In this chapter, we look at both qualitative techniques that use managerial judgment and also quantitative techniques that rely on mathematical models. It is our view that combining these techniques is essential to a good forecasting process that is appropriate to the decisions being made.

LO18–2 Evaluate demand using quantitative forecasting models.

QUANTITATIVE FORECASTING MODELS Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation. Qualitative techniques are covered later in the chapter. Time series analysis, the primary focus of this chapter, is based on the idea that data relating to past demand can be used to predict future demand. Past data may include several components, such as trend, seasonal, or cyclical influences, and are described in the following section. Causal forecasting, which we discuss using the linear regression technique, assumes that demand is related to some underlying factor or factors in the environment. Simulation models allow the

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forecaster to run through a range of assumptions about the condition of the forecast. In this chapter we focus on qualitative and time series techniques since these are most often used in supply chain planning and control.

Time series analysis A forecast in which past demand data is used to predict future demand.

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Components of Demand In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal element, cyclical elements, random variation, and autocorrelation. Exhibit 18.1 illustrates a demand over a four-year period, showing the average, trend, and seasonal components and randomness around the smoothed demand curve.

Cyclical factors are more difficult to determine because the time span may be unknown or the cause of the cycle may not be considered. Cyclical influence on demand may come from such occurrences as political elections, war, economic conditions, or sociological pressures.

Random variations are caused by chance events. Statistically, when all the known causes for demand (average, trend, seasonal, cyclical, and autocorrelative) are subtracted from total demand, what remains is the unexplained portion of demand. If we cannot identify the cause of this remainder, it is assumed to be purely random chance.

Autocorrelation denotes the persistence of occurrence. More specifically, the value expected at any point is highly correlated with its own past values. In waiting line theory, the length of a waiting line is highly autocorrelated. That is, if a line is relatively long at one time, then shortly after that time, we would expect the line still to be long.

When demand is random, it may vary widely from one week to another. Where high autocorrelation exists, the rate of change in demand is not expected to change very much from one week to the next.

Trend lines are the usual starting point in developing a forecast. These trend lines are then adjusted for seasonal effects, cyclical elements, and any other expected events that may influence the final forecast. Exhibit 18.2 shows four of the most common types of trends. A linear trend is obviously a straight continuous relationship. An S-curve is typical of a product growth and maturity cycle. The most important point in the S-curve is where the trend changes from slow growth to fast growth or from fast to slow. An asymptotic trend starts with the highest demand growth at the beginning but then tapers off. Such a curve could happen when a firm enters an existing market with the objective of saturating and capturing a large share of the market. An exponential curve is common in products with explosive growth. The exponential trend suggests that sales will grow at an ever-increasing rate—an assumption that may not be safe to make.

KEY IDEA

Remember that we are illustrating long-term patterns so they should be considered when making long-term forecasts. When making short-term forecasts, these patterns are often not so strong.

A widely used forecasting method plots data and then searches for the curve pattern (such as linear, S-curve, asymptotic, or exponential) that fits best. The attractiveness of this method is that because the mathematics for the curve are known, solving for values for future time periods is easy.

Sometimes our data do not seem to fit any standard curve. This may be due to several causes essentially beating the data from several directions at the same time. For these cases, a simplistic but often effective forecast can be obtained by simply plotting data.

exhibit 18.1 Historical Product Demand Consisting of a Growth Trend and Seasonal Demand

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For the Excel template, visit www.mhhe.com/jacobs14e.

http://www.mhhe.com/jacobs14e

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exhibit 18.2 Common Types of Trends

Time Series Analysis Time series forecasting models try to predict the future based on past data. For example, sales figures collected for the past six weeks can be used to forecast sales for the seventh week. Quarterly sales figures collected for the past several years can be used to forecast future quarters. Even though both examples contain sales, different forecasting time series models would likely be used.

Exhibit 18.3 shows the time series models discussed in the chapter and some of their characteristics. Terms such as short, medium, and long are relative to the context in which they are used. However, in business forecasting short term usually refers to under three months; medium term, three months to two years; and long term, greater than two years. We would generally use short-

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1. 2. 3.

term forecasts for tactical decisions such as replenishing inventory or scheduling employees in the near term, and medium-term forecasts for planning a strategy for meeting demand over the next six months to a year and a half. In general, the short-term models compensate for random variation and adjust for short-term changes (such as consumers’ responses to a new product). They are especially good for measuring the current variability in demand, which is useful for setting safety stock levels or estimating peak loads in a service setting. Medium-term forecasts are useful for capturing seasonal effects, and long-term models detect general trends and are especially useful in identifying major turning points.

Which forecasting model a firm should choose depends on:

Time horizon to forecast Data availability Accuracy required

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4. 5.

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exhibit 18.3 A Guide to Selecting an Appropriate Forecasting Method

Size of forecasting budget Availability of qualified personnel

To view a tutorial on Forecasting, visit www.mhhe.com/jacobs14e_tutorial_ch18.

In selecting a forecasting model, there are other issues such as the firm’s degree of flexibility. (The greater the ability to react quickly to changes, the less accurate the forecast needs to be.) Another item is the consequence of a bad forecast. If a large capital investment decision is to be based on a forecast, it should be a good forecast.

Simple Moving Average When demand for a product is neither growing nor declining rapidly, and if it does not have seasonal characteristics, a moving average can be useful in removing the random fluctuations for forecasting. The idea here is to simply calculate the average demand over the most recent periods. Each time a new forecast is made, the oldest period is discarded in the average and the newest period included. Thus, if we want to forecast June with a five-month moving average, we can take the average of January, February, March, April, and May. When June passes, the forecast for July would be the average of February, March, April, May, and June. An example using weekly demand is shown in Exhibit 18.4. Here, 3-week and 9-week moving average forecasts are calculated. Notice how the forecast is shown in the period following the data used. The 3-week moving average for week 4 uses actual demand from weeks 1, 2, and 3.