Presentation is loading. Please wait.

Presentation is loading. Please wait.

Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard.

Similar presentations


Presentation on theme: "Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard."— Presentation transcript:

1 Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard W. Taylor, III

2 Copyright 2006 John Wiley & Sons, Inc. Components of Demand  Average demand for a period of time  Trend  Seasonal element  Cyclical elements  Random variation  Autocorrelation

3 Seven Steps in Forecasting  Determine the use of the forecast  Select the items to be forecasted  Determine the time horizon of the forecast  Select the forecasting model(s)  Gather the data  Make the forecast  Validate and implement results

4 Copyright 2006 John Wiley & Sons, Inc.12-4 Forecasting Methods  Qualitative use management judgment, expertise, and opinion to predict future demand use management judgment, expertise, and opinion to predict future demand  Time series statistical techniques that use historical demand data to predict future demand statistical techniques that use historical demand data to predict future demand  Regression methods attempt to develop a mathematical relationship between demand and factors that cause its behavior attempt to develop a mathematical relationship between demand and factors that cause its behavior

5 Copyright 2006 John Wiley & Sons, Inc.12-5 Qualitative Methods   Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts   Delphi method involves soliciting forecasts about technological advances from experts

6 Copyright 2006 John Wiley & Sons, Inc.12-6 Qualitative Methods Grass Roots Market Research Panel Consensus Executive Judgment Historical analogy Delphi Method Qualitative Methods

7  Involves small group of high-level managers  Group estimates demand by working together  Combines managerial experience with statistical models  Relatively quick  ‘Group-think’ disadvantage Jury of Executive Opinion

8 Copyright 2006 John Wiley & Sons, Inc.12-8 Sales Force Composite  Each salesperson projects his or her sales  Combined at district and national levels  Sales reps know customers’ wants  Tends to be overly optimistic

9 Delphi Method l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas. 2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants. 3. Summarize the results and redistribute them to the participants along with appropriate new questions. 4. Summarize again, refining forecasts and conditions, and again develop new questions. 5. Repeat Step 4 if necessary. Distribute the final results to all participants.

10 Delphi Method  Iterative group process, continues until consensus is reached  3 types of participants  Decision makers  Staff  Respondents Staff (Administering survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments)

11 Copyright 2006 John Wiley & Sons, Inc.12-11 Consumer Market Survey  Ask customers about purchasing plans  What consumers say, and what they actually do are often different  Sometimes difficult to answer

12 Copyright 2006 John Wiley & Sons, Inc.12-12 Time Frame  Indicates how far into the future is forecast Short- to mid-range forecast Short- to mid-range forecast typically encompasses the immediate future typically encompasses the immediate future daily up to two years daily up to two years Long-range forecast Long-range forecast usually encompasses a period of time longer than two years usually encompasses a period of time longer than two years

13 Copyright 2006 John Wiley & Sons, Inc.12-13 Demand Behavior  Trend a gradual, long-term up or down movement of demand a gradual, long-term up or down movement of demand  Random variations movements in demand that do not follow a pattern movements in demand that do not follow a pattern  Cycle an up-and-down repetitive movement in demand an up-and-down repetitive movement in demand  Seasonal pattern an up-and-down repetitive movement in demand occurring periodically an up-and-down repetitive movement in demand occurring periodically

14 Copyright 2006 John Wiley & Sons, Inc.12-14 Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Demand Demand Demand Random movement Forms of Forecast Movement

15 Copyright 2006 John Wiley & Sons, Inc.12-15 Time Series   Assume that what has occurred in the past will continue to occur in the future   Relate the forecast to only one factor - time   Include moving average exponential smoothing linear trend line

16 Copyright 2006 John Wiley & Sons, Inc.12-16  Averaging method  Weights most recent data more strongly  Reacts more to recent changes  Widely used, accurate method Exponential Smoothing

17 Copyright 2006 John Wiley & Sons, Inc.12-17 F t +1 =  D t + (1 -  )F t where: F t +1 =forecast for next period D t =actual demand for present period F t =previously determined forecast for present period  =weighting factor, smoothing constant Exponential Smoothing (cont.)

18 Copyright 2006 John Wiley & Sons, Inc.12-18 Regression Methods   Linear regression a mathematical technique that relates a dependent variable to an independent variable in the form of a linear equation   Correlation a measure of the strength of the relationship between independent and dependent variables

19 Copyright 2006 John Wiley & Sons, Inc.12-19 Forecast Accuracy   Forecast error difference between forecast and actual demand MAD mean absolute deviation MAPD mean absolute percent deviation Cumulative error Average error or bias

20 Copyright 2006 John Wiley & Sons, Inc.12-20 Mean Absolute Deviation (MAD) where t = period number t = period number D t = demand in period t D t = demand in period t F t = forecast for period t F t = forecast for period t n = total number of periods n = total number of periods  = absolute value  D t - F t  n MAD =


Download ppt "Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard."

Similar presentations


Ads by Google