CHAPTER 11 FORECASTING AND DEMAND PLANNING

Transcription

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

2 Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1 Describe the importance of forecasting to the value chain. LO2 Explain basic concepts of forecasting and time series. LO3 Explain how to apply single moving average and exponential smoothing models. LO4 Describe how to apply regression as a forecasting approach. LO5 Explain the role of judgment in forecasting. LO6 Describe how statistical and judgmental forecasting techniques are applied in practice. 2

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 customers wanting 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? 3

4 Chapter 11 Forecasting and Demand Planning Forecasting is 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. 4

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. 5

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. 6

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

8 Chapter 11 Forecasting and Demand Planning Basic Concepts in Forecasting The planning horizon is 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. 8

9 Chapter 11 Forecasting and Demand Planning Basic Concepts in Forecasting A time series is 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 9

10 Exhibit 11.2 Example of Linear and Nonlinear Trend Patterns 10

11 Exhibit 11.3 Seasonal Pattern of Home Natural Gas Usage Seasonal patterns are characterized by repeatable periods of ups and downs over short periods of time. 11

12 Exhibit Extra Trend and Business Cycle Characteristics (each data point is 1 year apart) Cyclical patterns are regular patterns in a data series that take place over long periods of time. 12

13 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. 13

14 Chapter 11 Forecasting and Demand Planning Basic Concepts in Forecasting Irregular variation is a one-time variation that is explainable. For example, a hurricane can cause a surge in demand for building materials, food, and water. 14

15 Exhibit 11.4 Call Center Volume 15

16 Exhibit 11.5 Chart of Call Volume There is an increasing trend over the six years, along with seasonal patterns within each year. 16

17 Chapter 11 Forecasting and Demand Planning Forecast error is the difference between the observed value of the time series and the forecast, or A t F t. Mean Square Error (MSE) Σ(A t F t ) MSE = 2 [11.1] T Mean Absolute Deviation Error (MAD) Σ (A t F t ) MAD = [11.2] T Mean Absolute Percentage Error (MAPE) Σ (A t F t )/A t X 100 MAPE = [11.3] T 17

18 Exhibit 11.6 Forecast Error of Example Time Series Data 18

19 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 timeseries 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. 19

20 Chapter 11 Forecasting and Demand Planning Solved Problem: Develop three-period and fourperiod 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? Period Demand Period Demand

21 Chapter 11 Solved Problem Moving Average Forecasts Period Based on these error metrics (MAD, MSE, MAPE), the 3-month moving average is the best method among the three. 21

22 Chapter 11 Forecasting and Demand Planning Types of Forecasting Approaches Statistical forecasting is based on the assumption that the future will be an extrapolation of the past. Judgmental forecasting relies upon opinions and expertise of people in developing forecasts. 22

23 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. 23

24 Exhibit 11.7 Summary of 3-Month Moving-Average Forecasts 24

25 Exhibit 11.8 Milk Sales Forecast Error Analysis 25

26 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. 26

27 Exhibit 11.9 Summary of Single Exponential Smoothing Milk Sales Forecasts with α =

28 Exhibit Graph of Single Exponential Smoothing Milk Sales Forecasts with α =

29 Chapter 11 Forecasting and Demand Planning Regression analysis is 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. Y t = 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 bestfitting regression model for a time series by selecting the Add Trendline option from the Chart menu. 29

30 Exhibit Factory Energy Costs 30

31 Exhibit Add Trendline Dialog 31

32 Exhibit Add Trendline Options Tab 32

33 Exhibit Least-Squares Regression Model for Energy Cost Forecasting 33

34 Exhibit Gasoline Sales Data 34

35 Exhibit Chart of Sales versus Time 35

36 Exhibit Multiple Regression Results 36

37 Chapter 11 Forecasting and Demand Planning Judgmental Forecasting When no historical data is available, only judgmental forecasting is possible. The Delphi method consists of forecasting by expert opinion by gathering judgments and opinions of key personnel based on their experience and knowledge of the situation. 37

38 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 38

39 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. 39

40 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. 40

41 Exhibit Example Call Volume Data by Day for BankUSA Case Study Day CALL VOLUME