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Prepared by Lee Revere and John Large

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1 Prepared by Lee Revere and John Large
Forecasting Prepared by Lee Revere and John Large To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-1

2 What is Forecasting? Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities Sales will be $200 Million! To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-2

3 Learning Objectives Students will be able to:
Understand and know when to use various families of forecasting models. Compare moving averages, exponential smoothing, and trend time-series models. Seasonally adjust data. Understand Delphi and other qualitative decision-making approaches. Compute a variety of error measures. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-3

4 Introduction Eight steps to forecasting:
Determine the use of the forecast. Select the items or quantities to be forecasted. Determine the time horizon of the forecast. Select the forecasting model or models. Gather the data needed to make the forecast. Validate the forecasting model. Make the forecast. Implement the results. These steps provide a systematic way of initiating, designing, and implementing a forecasting system. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-4

5 Time Series Components
Trend Seasonal Cyclical Random To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-5

6 Trend Component Persistent, overall upward or downward pattern
Due to population, technology etc. Several years duration Mo., Qtr., Yr. Response © T/Maker Co. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-6

7 Seasonal Component Regular pattern of up & down fluctuations
Due to weather, customs etc. Occurs within 1 year Mo., Qtr. Response Summer © T/Maker Co. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-7

8 Common Seasonal Patterns
Period of Pattern “Season” Length Number of “Seasons” in Pattern Week Day 7 Month 4 – 4 ½ 28 – 31 Year Quarter 4 12 52 To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-8

9  Cyclical Component Repeating up & down movements
Due to interactions of factors influencing economy Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-9

10 Random Component Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events Union strike Tornado Short duration & nonrepeating © T/Maker Co. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-10

11 Exponential Smoothing
Types of Forecasts Forecasting Techniques No single method is superior Qualitative Models: attempt to include subjective factors Time-Series Methods: include historical data over a time interval Causal Methods: include a variety of factors Delphi Methods Moving Average Regression Analysis Jury of Executive Opinion Exponential Smoothing Multiple Regression Trend Projections Sales Force Composite Decomposition Consumer Market Survey To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-11

12 Qualitative Methods Delphi Method interactive group process consisting of obtaining information from a group of respondents through questionnaires and surveys Jury of Executive Opinion obtains opinions of a small group of high-level managers in combination with statistical models Sales Force Composite allows each sales person to estimate the sales for his/her region and then compiles the data at a district or national level Consumer Market Survey solicits input from customers or potential customers regarding their future purchasing plans To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-12

13 Delphi Method Iterative group process 3 types of people
Decision makers Staff Respondents Reduces ‘group-think’ Decision Makers (Sales?) (Sales will be 50!) Staff (What will sales be? survey) Respondents (Sales will be 45, 50, 55) To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-13

14 Jury of Executive Opinion
Involves small group of high-level managers Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’ disadvantage To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-14 © 1995 Corel Corp.

15 Sales Force Composite Each salesperson projects his or her sales
Combined at district & national levels Sales reps know customers’ wants Tends to be overly optimistic Sales © 1995 Corel Corp. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-15

16 Consumer Market Survey
Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer How many hours will you use the Internet next week? © 1995 Corel Corp. To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-16

17 Scatter Diagrams Scatter diagrams are helpful when forecasting time-series data because they depict the relationship between variables. Radios Televisions Compact Discs To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-17

18 Measures of Forecast Accuracy
Forecast errors allow one to see how well the forecast model works and compare that model with other forecast models. Forecast error = actual value – forecast value To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-18

19 Measures of Forecast Accuracy (continued)
Measures of forecast accuracy include: Mean Absolute Deviation (MAD) Mean Squared Error (MSE) Mean Absolute Percent Error (MAPE) = å |forecast errors| n 2 = å (errors) n = å actual n error 100% To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-19

20 Hospital Days – Forecast Error Example
Ms. Smith forecasted total computers sale last year. Now that the actual data are known, she is reevaluating her forecasting model. Compute the MAD, MSE, and MAPE for her forecast. Month Forecast Actual JAN 250 243 FEB 320 315 MAR 275 286 APR 260 256 MAY 241 JUN 298 JUL 300 292 AUG 325 333 SEP 326 OCT 350 378 NOV 365 382 DEC 380 396 To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-20

21 Hospital Days – Forecast Error Example
Actual |error| error^2 |error/actual| JAN 250 243 7 49 0.03 FEB 320 315 5 25 0.02 MAR 275 286 11 121 0.04 APR 260 256 4 16 MAY 241 9 81 JUN 298 23 529 0.08 JUL 300 292 8 64 AUG 325 333 SEP 326 6 36 OCT 350 378 28 784 0.07 NOV 365 382 17 289 DEC 380 396 AVERAGE 11.83 192.83 3.68 To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 5-21

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