1. Chapter 11 – With Woodruff Modications Demand Management and Forecasting Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

2. 11-2 Demand Management Strategic forecasts: forecasts used to help set the strategy of how demand will be met Tactical forecasts: forecasted needed for how a firm operates processes on a day-to- day basis The purpose of demand management is to coordinate and control all sources of demand Two basic sources of demand –Dependent demand: the demand for a product or service caused by the demand for other products or services –Independent demand: the demand for a product or service that cannot be derived directly from that of other products

3. 11-3 Components of Demand 1.Average demand for a period of time 2.Trend 3.Seasonal element 4.Cyclical elements 5.Random variation 6.Autocorrelation

4. 11-4 Why OM People Forecst Supplies (internal) Replacement Parts Sub-Contract Work Marketing may provide a goal rather than a forecast Marketing may use too long a period (e.g. quarters rather than months)

5. 11-5 Common Types of Trends

6. 11-6 Time Series Analysis Short term: forecast under three months –Tactical decisions Medium term: three months to two years –Capturing seasonal effects Long term: forecast longer than two years –Detecting general trends –Identifying major turning points

7. 11-7 Linear Regression Analysis Regression: functional relationship between two or more correlated variables It is used to predict one variable given the other Y = a + bX –Y is the value of the dependent variable –a is the Y intercept –b is the slope –X is the independent variable Assumes data falls in a straight line

8. 11-8 Example 11.1: The Data and Least Squares Regression Line

9. 11-9 Example 11.1: Equations and Calculating Totals

10. 11-10 Example 11.1: Calculating the Forecast

11. 11-11 Decomposition of a Time Series Time series: chronologically ordered data that may contain one or more components of demand Decomposition: identifying and separating the time series data into these components Seasonal variation –Additive: the seasonal amount is constant –Multiplicative: the seasonal variation is a percentage of demand

12. 11-12 Example 11.3: The Data and Hand Fitting

13. 11-13 Example 11.3: Computing Seasonal Factors and Computing Forecast

14. 11-14 Decomposition Using Least Squares Regression 1.Determine the seasonal factor 2.Deseasonalize the original data 3.Develop a least squares regression line for the deseasonalized data 4.Project the regression line through the period of the forecast 5.Create the final forecast by adjusting the regression line by the seasonal factor

15. 11-15 Simple Moving Average Useful when demand is neither growing nor declining rapidly and does not have seasonal characteristics Moving averages can be centered or used to predict the following period Important to select the best period –Longer gives more smoothing –Shorter reacts quicker to trends

16. 11-16 Simple Moving Average Formula

17. 11-17 Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average

18. 11-18 Weighted Moving Average The moving average formula implies an equal weight being placed on each value that is being averaged The weighted moving average permits an unequal weighting on prior time periods –All the weights must sum to one

19. 11-19 Exponential Smoothing Most used of all forecasting techniques Integral part of all computerized forecasting programs Widely used in retail and service Widely accepted because… 1.Exponential models are surprisingly accurate 2.Formulating an exponential model is relatively easy 3.The user can understand how the model works 4.Little computation is required to use the model 5.Computer storage requirements are small 6.Tests for accuracy are easy to compute

20. 11-20 Exponential Smoothing Model

21. 11-21 Exponential Smoothing Example (  =0.20)

22. 11-22 Tips For F 0 (or, say, F 4 ) use something like (A 1 +A 2 +A 3 +A 4 )/4 I.e., for the first F, use the average of the first N observations For performance similar to an N period moving average, use α= 2 / (N+1)

23. 11-23 Trend Effects in Exponential Smoothing An trend in data causes the exponential forecast to always lag the actual data Can be corrected somewhat by adding in a trend adjustment To correct the trend, we need two smoothing constants –Smoothing constant alpha (  ) –Trend smoothing constant delta (δ)

24. 11-24 Trend Effects Equations

25. 11-25 Forecast Error Bias errors: when a consistent mistake is made Random errors: errors that cannot be explained by the forecast model being used Measures of error –Mean absolute deviation (MAD) –Mean absolute percent error (MAPE) –Tracking signal

26. 11-26 The MAD Statistic to Determine Forecasting Error The ideal MAD is zero which would mean there is no forecasting error The larger the MAD, the less the accurate the resulting model

27. 11-27 Causal Relationship Forecasting Causal relationship forecasting: using independent variables other than time to predict future demand –The independent variable must be a leading indicator Must find those occurrences that are really the causes

28. 11-28 Qualitative Techniques in Forecasting Qualitative forecasting techniques take advantage of the knowledge of experts Most useful when the product is new or there is little experience with selling into a new region The following are samples of qualitative forecasting techniques –Market research –Panel consensus –Historical analogy –Delphi method