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

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

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

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

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

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

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

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

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

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

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

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

Simple Moving Average

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

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

Jan120– Feb90 – Mar100 – Apr May June July Aug Sept Oct Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE Simple Moving Average

Jan120– Feb90 – Mar100 – Apr May June July Aug Sept Oct Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE MA 5 = 5 i = 1  DiDi 5 = = 91 orders for Nov Simple Moving Average

Jan120– – Feb90 – – Mar100 – – Apr – May – June July Aug Sept Oct Nov – ORDERSTHREE-MONTHFIVE-MONTH MONTHPER MONTHMOVING AVERAGEMOVING AVERAGE

– – – – – – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov Orders Month Smoothing Effects

– – – – – – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov Orders Month Actual

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

Smoothing Effects – – – – – – 0 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov 5-month 3-month Actual Orders Month

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

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

Weighted Moving Average Example

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

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) = orders

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

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

Effect of Smoothing Constant 0.0  1.0 If  = 0.20, then F t +1 = 0.20  D t 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

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

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) = Exponential Smoothing

FORECAST, F t + 1 PERIODMONTHDEMAND(  = 0.3) 1Jan37– 2Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan–51.79 Exponential Smoothing

FORECAST, F t + 1 PERIODMONTHDEMAND(  = 0.3)(  = 0.5) 1Jan37–– 2Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan– Exponential Smoothing

70 70 – – – – – – – 0 0 – ||||||||||||| Actual Orders Month Exponential Smoothing Forecasts

70 70 – – – – – – – 0 0 – ||||||||||||| Actual Orders Month  = 0.30 Exponential Smoothing Forecasts

70 70 – – – – – – – 0 0 – |||||||||||||  = 0.50 Actual Orders Month  = 0.30 Exponential Smoothing Forecasts