Learning Objectives

2. Learning Objectives • Recommend the appropriate forecasting model for a given situation. • Conduct a Delphi forecasting exercise. • Describe the features of exponential smoothing. • Conduct time series forecasting using exponential smoothing with trend and seasonal adjustments.

3. DEMAND MANAGEMENT • DEMAND MANAGEMENT • marketing • finance • operations • human resources

4. TYPES OF DEMAND • Independent or dependent demand • Demand for outputs or inputs • Aggregate versus item demand

5. TIME DIMENSION • short term- 15-30 days • medium term -6-12 months • long term - 10-20 years

6. LEAD TIME REQUIREMENTS • Make to stock - short lead time • Make parts-to-stock/assemble -to-order industry • Make-to-order industry - long lead time

7. Data sources • Marketing projections • Economic projections • Historical demand projections

8. Forecasting • FORECASTING FOR SUPPORT SERVICES • Hiring • Layoffs and reassignments • Training • Payroll actions • Union contract negotiations

9. continued • FORECAST ERROR • Et = Dt - Ft • Et = error for period t • Dt = actual demand that occurred in period t • Ft = forecast for period t • Period t depends on the purpose of the forecast

10. Cont… • MEAN ABSOLUTE DEVIATION (MAD): simplest way of calculating average error • MAD = ΣEt n

11. HISTORICAL DEMAND PROJECTIONS • By time series we mean a series of demands over time. The main recognizable time-series components are: • Trend, or slope, defined as the positive or negative shift in series value over a certain time period • Seasonality, usually occurring within one year and recurring annually • Cyclical Pattern, also recurring, but usually spanning several years • Random Events: explained, such as effects of natural disasters or accidents • Unexplained, for which no known cause exists

12. Forecasting Models • Subjective Models Delphi Methods • Causal Models Regression Models • Time Series Models Moving Averages Exponential Smoothing

13. NAIVE METHOD OF FORECASTING • use the most recent period’s actual sales • jury of executive opinion • prompted by lack of good demand data

14. MULTIPERIOD PATTERN PROJECTION • MEAN AND TREND • used when the historical demand lacks trend and is not inherently seasonal

15. continued • SEASONAL : often an item showing a trend also has a history of demand seasonality, which calls for the seasonal index method of building seasonality into a demand forecast • Seasonal Index: example in handout • Seasonally adjusted trends: example in handout

16. PATTERNLESS PROJECTION • These techniques make no inferences about past demand data but merely react to the most recent demands. • These techniques – moving average, exponential smoothing, and simulation – typically produce a single value, which is the forecast for a single period into the future.

17. Moving Average • It is the arithmeticmean of a given number of the most recent actual demands • 3 period moving average - exhibit 4-13 (handout) • Mean absolute deviation (MAD) - exhibit 4-13

18. EXPONENTIAL SMOOTHING • Most widely used quantitative forecasting technique • smoothes the historical demand time series • assigns different weight to each period’s data; lower to points further away • Ft+1 = Ft + α(Dt - Ft) • Ft+1 = forecast for period t+1 • α = smoothing constant • Dt = actual demand that occurred in period t • Ft = forecast for period t

19. continued • next period forecast = last period forecast + α(last period demand - last period forecast) • the future forecasts are being adjusted for the forecast error in the last period • exhibit 4-16 (handout) • small α means each successive forecast is close to its predecessor - stable demand • large α means large up and down swings of actual demand - unstable demand

20. continued • note - how the exponential smoothing extends back into the past indefinitely, that is, the adjustments made in the past are carried forward in a diminishing manner • problem of startup forecast • moving average and exponential smoothing are based on the assumption that past demand data is the best indicator of the future • problem in exhibit 4.16 (handout)

21. ADAPTIVE SMOOTHING • used as an extension of exponential smoothing • forecasters may adjust the value of smoothing coefficient α if cumulative forecast error gets too large, thus adapting the forecasting model to changing conditions • running sum of forecast error is used for signaling, whether α needs to be changed • TRACKING SIGNAL = RSFE MAD • If RSFE is getting larger in the positive direction, implying, that actual demand is higher than the forecasted demand, then you want to increase the next period forecasted value. This can be done by increasing the value of α; and vice versa.

22. FORECASTING BY SIMULATION • using distributions of each variable, simulated runs are generated - suggesting the forecasted values. • forecast error is calculated by subtracting the actual demand from the forecasted demand • CORRELATION • REGRESSION

23. QUESTIONS TO PONDER 1. What are the purposes of demand management? 2. What are the short, medium, and long term purposes of demand forecasting? 3. How is forecast error measured? What are the limitations of this measure? 4. What is a time series? What are its principle components? 5. How is one forecasting model compared with another in selecting a model for future use? 6. Make sure you know how to do the problems.