Business Processes Sales Order Management Aggregate Planning Master Scheduling Production Activity Control Quality Control Distribution Mngt. © 2001 Victor.

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Presentation transcript:

Business Processes Sales Order Management Aggregate Planning Master Scheduling Production Activity Control Quality Control Distribution Mngt. © 2001 Victor E. Sower, Ph.D., C.Q.E.

© 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Chapter 11 Forecasting

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Forecasting Predicting future events Usually demand behavior over a time frame Qualitative methods – based on subjective methods Quantitative methods – based on mathematical formulas

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Time Frame Short-range to medium-range –daily, weekly monthly forecasts of sales data –up to 2 years into the future Long-range –strategic planning of goals, products, markets –planning beyond 2 years into the future

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Demand Behavior Trend –gradual, long-term up or down movement Cycle –up & down movement repeating over long time frame Seasonal pattern –periodic oscillation in demand which repeats Random movements follow no pattern

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Forms Of Forecast Movement Demand Time Trend Random movement Demand Time Seasonal pattern Demand Time Demand Time Cycle Trend with seasonal pattern

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Forecasting Methods Qualitative methods –management judgment, expertise, opinion –use management, marketing, purchasing, engineering Delphi method –solicit forecasts from experts

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Forecasting Process 6. Check forecast accuracy with one or more measures 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 4. Select a forecast model that seems appropriate for data 5. Develop / compute forecast for period of historical data 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 7. Is accuracy of forecast acceptable?

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Time Series Methods Statistical methods using historical data –moving average –exponential smoothing –linear trend line Assume patterns will repeat Naive forecasts –forecast = data from last period Demand

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Forecast Accuracy Error = Actual - Forecast Find a method which minimizes error Mean Absolute Deviation (MAD) Mean Absolute Percent Deviation (MAPD) Cumulative Error (E) Bias

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Mean Absolute Deviation (MAD) MAD =   D t - F t  n where, t= the period number D t = demand in period t F t = the forecast for period t n= the total number of periods  = the absolute value

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Other Accuracy Measures Mean absolute percent deviation (MAPD) Cumulative error Average error or Bias Mean Squared Error (MSE)

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Tracking Signal Compute each period Compare to control limits Forecast is in control if within limits Use control limits of +/- 2 to +/- 5 MAD

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Monitoring Forecast Errors With Statistical Control Charts

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Regression Methods Study relationship between two or more variables Dependent variable depends on independent variable

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Linear Regression Formulas y = a + bx where, a=intercept (at period 0) b=slope of the line x=the independent variable y=forecast for demand given x b= a=y - b x where, n=number of periods x=  x, mean of x values n y=  y, mean of y values n  x 2 - nx 2  xy - nxy

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Linear Regression Line

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Correlation And Coefficient Of Determination Correlation, r –measure of strength of relationship –varies between and Coefficient of determination, r 2 –percentage of variation in dependent variable resulting form independent variable

Ch © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Multiple Regression Study relationship of demand to two or more independent variables … where y =  0 +  1 x 1 +  2 x 2 … +  k x k where,  0 = intercept  1, …,  k = parameters for independent variables x 1, …, x k = independent variables

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