1. PRODUCTION & OPERATIONS MANAGEMENT Module II Forecasting for operations Prof. A.Das, MIMTS

2. Forecasting Predicting future events Usually demand behavior over a time frame Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas

3. Strategic Role of Forecasting Focus on supply chain management Short term role of product demand Long term role of new products, processes, and technologies Focus on Total Quality Management Satisfy customer demand Uninterrupted product flow with no defective items Necessary for strategic planning

4. Time Frame Short-range, medium- range, long-range Demand Behavior Trends, cycles, seasonal patterns, random Components of Forecasting Demand

5. 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

6. 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

7. Forms of Forecast Movement Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Random movement

8. Forecasting Methods Time series Regression or causal modeling Qualitative methods Management judgment, expertise, opinion Use management, marketing, purchasing, engineering Delphi method Solicit forecasts from experts

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

10. 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

11. Moving Average Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern

12. Moving Average MA n = n i = 1  DiDi n where n =number of periods in the moving average D i =demand in period i Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern

13. Simple Moving Average

14. Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH

15. Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH MA 3 = 3 i = 1  DiDi 3 = 90 + 110 + 130 3 = 110 orders for Nov Simple Moving Average

16. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE Simple Moving Average

17. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE MA 5 = 5 i = 1  DiDi 5 = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Simple Moving Average

18. Jan120– – Feb90 – – Mar100 – – Apr75103.3 – May11088.3 – June5095.099.0 July7578.385.0 Aug13078.382.0 Sept11085.088.0 Oct90105.095.0 Nov –110.091.0 ORDERSTHREE-MONTHFIVE-MONTH MONTHPER MONTHMOVING AVERAGEMOVING AVERAGE

19. 150 150 – 125 125 – 100 100 – 75 75 – 50 50 – 25 25 – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov Orders Month Smoothing Effects

20. 150 150 – 125 125 – 100 100 – 75 75 – 50 50 – 25 25 – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov Orders Month Actual

21. Smoothing Effects 150 – 125 – 100 – 75 – 50 – 25 – 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov 3-month Actual Orders Month

22. Smoothing Effects 150 150 – 125 125 – 100 100 – 75 75 – 50 50 – 25 25 – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov 5-month 3-month Actual Orders Month

23. Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuations

24. Weighted Moving Average WMA n = i = 1  W i D i where W i = the weight for period i, between 0 and 100 percent  W i = 1.00 Adjusts moving average method to more closely reflect data fluctuations

25. Weighted Moving Average Example

26. MONTH WEIGHT DATA August 17%130 September 33%110 October 50%90

27. Weighted Moving Average Example MONTH WEIGHT DATA August 17%130 September 33%110 October 50%90 November forecast WMA 3 = 3 i = 1  W i D i = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders

28. Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method Exponential Smoothing

29. 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 Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method Exponential Smoothing

30. Effect of Smoothing Constant 0.0  1.0 If  = 0.20, then F t +1 = 0.20  D t + 0.80 F t If  = 0, then F t +1 = 0  D t + 1 F t 0 = F t Forecast does not reflect recent data If  = 1, then F t +1 = 1  D t + 0 F t =  D t Forecast based only on most recent data

31. PERIODMONTHDEMAND 1Jan37 2Feb40 3Mar41 4Apr37 5May 45 6Jun50 7Jul 43 8Aug 47 9Sep 56 10Oct52 11Nov55 12Dec 54 Exponential Smoothing

32. PERIODMONTHDEMAND 1Jan37 2Feb40 3Mar41 4Apr37 5May 45 6Jun50 7Jul 43 8Aug 47 9Sep 56 10Oct52 11Nov55 12Dec 54 F 2 =  D 1 + (1 -  )F 1 = (0.30)(37) + (0.70)(37) = 37 F 3 =  D 2 + (1 -  )F 2 = (0.30)(40) + (0.70)(37) = 37.9 F 13 =  D 12 + (1 -  )F 12 = (0.30)(54) + (0.70)(50.84) = 51.79 Exponential Smoothing

33. FORECAST, F t + 1 PERIODMONTHDEMAND(  = 0.3) 1Jan37– 2Feb4037.00 3Mar4137.90 4Apr3738.83 5May 4538.28 6Jun5040.29 7Jul 4343.20 8Aug 4743.14 9Sep 5644.30 10Oct5247.81 11Nov5549.06 12Dec 5450.84 13Jan–51.79 Exponential Smoothing

34. FORECAST, F t + 1 PERIODMONTHDEMAND(  = 0.3)(  = 0.5) 1Jan37–– 2Feb4037.0037.00 3Mar4137.9038.50 4Apr3738.8339.75 5May 4538.2838.37 6Jun5040.2941.68 7Jul 4343.2045.84 8Aug 4743.1444.42 9Sep 5644.3045.71 10Oct5247.8150.85 11Nov5549.0651.42 12Dec 5450.8453.21 13Jan–51.7953.61 Exponential Smoothing

35. 70 70 – 60 60 – 50 50 – 40 40 – 30 30 – 20 20 – 10 10 – 0 0 – ||||||||||||| 12345678910111213 Actual Orders Month Exponential Smoothing Forecasts

36. 70 70 – 60 60 – 50 50 – 40 40 – 30 30 – 20 20 – 10 10 – 0 0 – ||||||||||||| 12345678910111213 Actual Orders Month  = 0.30 Exponential Smoothing Forecasts

37. 70 70 – 60 60 – 50 50 – 40 40 – 30 30 – 20 20 – 10 10 – 0 0 – ||||||||||||| 12345678910111213  = 0.50 Actual Orders Month  = 0.30 Exponential Smoothing Forecasts