FORECASTING AND DEMAND PLANNING

1. OM2 CHAPTER 11 FORECASTING AND DEMAND PLANNING DAVID A. COLLIER AND JAMES R. EVANS

2. Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1Describe the importance of forecasting to thevalue chain. LO2Explain basic concepts of forecasting and timeseries. LO3Explain how to apply single moving average andexponential smoothing models. LO4Describe how to apply regression as a forecastingapproach. LO5Explain the role of judgment in forecasting. LO6Describe how statistical and judgmental forecasting techniques are applied in practice.

3. Chapter 11 Forecasting and Demand Planning he demand for rental cars in Florida and other warm climates peaks during college spring break season. Call centers and rental offices are flooded with customerswanting to rent a vehicle. National Car Rental took a unique approach by developing a customer-identification forecasting model, by which it identifies all customers who are young and rent cars only once or twice a year. These demand analysis models allow National to call this target market segment in February, when call volumes are lower, to sign them up again. The proactive strategy is designed to both boost repeat rentals and smooth out the peaks and valleys in call center volumes. What do you think?Think of a pizza delivery franchise located near a college campus. What factors that influence demand do you think should be included in trying to forecast demand for pizzas?

4. Chapter 11 Forecasting and Demand Planning • Forecastingis the process of projecting the values of one or more variables into the future. • Poor forecasting can result in poor inventory and staffing decisions, resulting in part shortages, inadequate customer service, and many customer complaints.

5. Chapter 11 Forecasting and Demand Planning • Many firms integrate forecasting with value chain and capacity management systems to make better operational decisions. • Accurate forecasts are needed throughout the value chain, and are used by all functional areas of the organization, including accounting, finance, marketing, operations, and distribution.

6. Chapter 11 Forecasting and Demand Planning • One of the biggest problems with forecasting systems is that they are driven by different departmental needs and incentive systems. • Demand planning software systems integrate marketing, inventory, sales, operations planning, and financial data.

7. The Need for Forecasts in a Value Chain Exhibit 11.1

8. Colgate-Palmolive is a global consumer products company that manufactures such products as toothpaste, laundry detergents, pet foods, and soap, and it operates in over 200 countries. To reduce supply chain costs, Colgate-Palmolive implemented a supply chain planning process with its suppliers and customers to manage promotional demand, improve forecasts, and synchronize activities along the supply chain. These initiatives have improved on-time order performance from 70 to 98 percent for vendor managed inventories, reduced total inventories by 10 percent, and improved customer order fulfillment rates to 95 percent. Chapter 11 Forecasting and Demand Planning Colgate-Palmolive

9. Chapter 11 Forecasting and Demand Planning • Basic Concepts in Forecasting • The planning horizonis the length of time on which a forecast is based. This spans from short-range forecasts with a planning horizon of under 3 months to long-range forecasts of 1 to 10 years.

10. Chapter 11 Forecasting and Demand Planning • Basic Concepts in Forecasting • A time seriesis a set of observations measured at successive points in time or over successive periods of time. A time series pattern may have one or more of the following five characteristics: • Trend • Seasonal patterns • Cyclical patterns • Random variation (or noise) • Irregular (one time) variation

11. Example of Linear and Nonlinear Trend Patterns Exhibit 11.2

12. Seasonal Pattern of Home Natural Gas Usage Exhibit 11.3 Seasonal patterns are characterized by repeatable periods of ups and downs over short periods of time.

13. Trend and Business Cycle Characteristics (each data point is 1 year apart) Exhibit Extra Cyclical patternsare regular patterns in a data series that take place over long periods of time.

14. Chapter 11 Forecasting and Demand Planning Random variation (sometimes called noise) is the unexplained deviation of a time series from a predictable pattern, such as a trend, seasonal, or cyclical pattern. Because of these random variations, forecasts are never 100 percent accurate.

15. Chapter 11 Forecasting and Demand Planning Basic Concepts in Forecasting Irregular variationis a one-time variation that is explainable. For example, a hurricane can cause a surge in demand for building materials, food, and water.

16. Call Center Volume Exhibit 11.4

17. Chart of Call Volume Exhibit 11.5 There is an increasing trend over the six years, along with seasonal patterns within each year.

18. Σ(At – Ft )2 [11.1] MSE = T Σ׀(At – Ft ) ׀ [11.2] MAD = T Σ׀(At – Ft )/At ׀X 100 [11.3] MAPE = T Chapter 11 Forecasting and Demand Planning • Forecast erroris the difference between the observed value of the time series and the forecast, orAt – Ft. • Mean Square Error (MSE) • Mean Absolute Deviation Error (MAD) • Mean Absolute Percentage Error (MAPE)

19. Forecast Error of Example Time Series Data Exhibit 11.6

20. Chapter 11 Forecasting and Demand Planning • Forecast Errors and Accuracy • A major difference between MSE and MAD is that MSE is influenced much more by large forecasts errors than by small errors (because the errors are squared). • MAPE is different in that the measurement scale factor is eliminated by dividing the absolute error by the time-series data value. This makes the measure easier to interpret. • The selection of the best measure of forecast accuracy is not a simple matter; indeed, forecasting experts often disagree on which measure should be used.

21. Chapter 11 Forecasting and Demand Planning SolvedProblem Develop three-period and four-period moving-average forecasts and single exponential smoothing forecasts with α= 0.5. Compute the MAD, MAPE, and MSE for each. Which method provides a better forecast?

22. Chapter 11 Forecasting and Demand Planning Based on these error metrics (MAD, MSE, MAPE), the 3-month moving average is the best method among the three.

23. Chapter 11 Forecasting and Demand Planning • Types of Forecasting Approaches • Statistical forecastingis based on the assumption that the future will be an extrapolation of the past. • Judgmental forecastingrelies upon opinions and expertise of people in developing forecasts.

24. Chapter 11 Forecasting and Demand Planning • Single Moving Average • A moving average (MA)forecast is an average of the most recent “k” observations in a time series. • MA methods work best for short planning horizons when there is no major trend, seasonal, or business cycle pattern. • As the value of “k” increases, the forecast reacts slowly to recent changes in the time series data.

25. Summary of 3-Month Moving-Average Forecasts Exhibit 11.7

26. Milk Sales Forecast Error Analysis Exhibit 11.8

27. Chapter 11 Forecasting and Demand Planning Single Exponential Smoothing (SES)is a forecasting technique that uses a weighted average of past time-series values to forecast the value of the time series in the next period. • The forecast “smoothes out” the irregular fluctuations in the time series.

28. Summary of Single Exponential Smoothing Milk SalesForecasts with α = 0.2 Exhibit 11.9

29. Graph of Single Exponential Smoothing Milk Sales Forecasts with α = 0.2 Exhibit 11.10

30. Chapter 11 Forecasting and Demand Planning • Regression analysisis a method for building a statistical model that defines a relationship between a single dependent variable and one or more independent variables, all of which are numerical. • Yt = a + bt(11.7) • Simple linear regression finds the best values of a and b using the method of least squares. • Excel provides a very simple tool to find the best-fitting regression model for a time series by selecting the Add Trendline option from the Chartmenu.

31. Factory Energy Costs Exhibit 11.11

32. Add Trendline Dialog Exhibit 11.12

33. Add Trendline Options Tab Exhibit 11.13

34. Least-Squares Regression Model for Energy Cost Forecasting Exhibit 11.14

35. 2004 Gasoline Sales Data Exhibit 11.15

36. Chart of Sales versus Time Exhibit 11.16

37. Multiple Regression Results Exhibit 11.17

38. Chapter 11 Forecasting and Demand Planning • Judgmental Forecasting • When no historical data is available, only judgmental forecasting is possible. • The Delphi methodconsists of forecasting by expert opinion by gathering judgments and opinions of key personnel based on their experience and knowledge of the situation.

39. Chapter 11 Forecasting and Demand Planning • Judgmental Forecasting • Another common approach to gathering data is a survey. Sample sizes are usually much larger than with Delphi, however, and the cost of such surveys can be high. • The major reasons for using judgmental methods are: • Greater accuracy • Ability to incorporate unusual or one-time events • The difficultly of obtaining the data necessary for quantitative techniques

40. Chapter 11 Forecasting and Demand Planning • Forecasting in Practice • Managers use a variety of judgmental and quantitative forecasting techniques. • Statistical methods alone cannot account for such factors as sales promotions, competitive strategies, unusual economic disturbances, new products, large one-time orders, natural disasters, or labor complications.

41. Chapter 11 Forecasting and Demand Planning • Forecasting in Practice • The first step in developing a practical forecast is to understand the purpose, time horizon, and level of aggregation. • Different forecasting methods require different levels of technical ability and understanding of mathematical principles and assumptions.

42. A tracking signal provides a method for doing this by quantifying bias—the tendency of forecasts to consistently be larger or smaller than the actual values of the time series. Tracking signal = Σ(At – Ft)/MAD Tracking signals between plus and minus 4 indicated an adequate forecasting model. Chapter 11 Forecasting and Demand Planning TrackingSignals

43. Example Call Volume Data by Day for BankUSA Case Study Exhibit 11.18 CALL VOLUME Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 413 536 495 451 490 400 525 490 492 519 402 616 495 527 461 370

44. BankUSA: Forecasting Help Desk Demand by Day Case Study 1. What are the service management characteristics of the CSR job? 2. Define the mission statement and strategy of the Help Desk contact center. Why is the Help Desk important? Who are its customers? 3. How would you handle the customer affected by the inaccurate stock price in the banks trust account system? Would you take a passive or proactive approach? Justify your answer. 4. Using the information in Exhibit 11.18, how would you forecast short-term demand?