1. CHAPTER: 3 FORECASTING (MSC 301) MD. TAMZIDUL ISLAM FACULTY, BBS

2. Forecasting Method Qualitative Forecasting Methods  Judgmental Methods  Sales force estimate  Executive Opinion  Market Surveys  Delphi  Curve Fitting S-Curve technique  Cross Impact Matrices  Relevance Trees 2

3. Forecasting Method 3 Quantitative Forecasting Methods (Time Series)  Causal Methods  Linear Regression  Naive  Simple Moving Average  Weighted Moving Average  Exponential Smoothing Single Double Etc.  Seasonal Patterns  Econometric Modelling

4. Qualitative Methods 4 Consider Scenario

5. The Use Of Qualitative Techniques 5  When the choice of method is restricted by lack of quantitative data of a suitable quality.  When quantitative data exists but there are other factors affecting the forecast that are better dealt with qualitatively.

6. Sales Force Estimate 6 Forecast is compiled from the feedback of sales force. Advantages:  Direct interface of the market  Can be divided into territories/regions Disadvantages:  Individual Bias  Underestimation

7. Executive Opinion Another useful technique using the opinion of managers/executives. Advantages:  Based on experience  Can be used to modify the existing forecast considering unexpected events Disadvantages:  Costly incurring expensive management hours  Conflicting interest 7

8. Market Survey Is a systematic data gathering from customers by creating and testing different hypotheses. Advantages:  More accurate backed by valid data  Helpful to management persuasion Disadvantages:  Expensive and time consuming  Dependency on third party 8

9. Delphi Method 9 The Delphi technique is an exploratory technique and is based on the consensus of a panel of experts, but does not allow for communication between group members. Advantages:  Considers the experts’ opinion thus has more validity  Useful for long term forecasting Disadvantages:  Takes longer time

10. Delphi Method-Process 10 (1) The chairman asks each expert of the group to make a forecast of the variable in question for the relevant time period. (2) The chairman collects the submissions of all participants and summarizes them. (3) The chairman asks the group to reconsider their forecasts, taking into account the information presented to them in the summary. (4) The process is repeated until the group has reached a consensus or until the participants are no longer prepared to adjust their forecast further.

11. Quantitative Methods-Naive Forecast 11  Forecast for the next period equals the demand for current period.  Also take into account the change in demand Advantages:  Simple and low cost Disadvantages:  Is not effective for random, seasonal variation

12. Simple Moving Average 12 Estimate based on taking the simple average of previous demands. Forecast=Sum of last n Demands/n

13. Weighted Moving Average Method 13  Assign more weights to recent demand and gradually decreases.

14. Classroom Exercise 14 PeriodNo. of visitors in Museum 1120 2100 3130 4150 580 690 7100 8135 9140 10150 11110 12120 3-M Forecasting6-M Forecasting 119 134 111 99106 93102 116113 131120 144130 128127 Requirement: Forecast the demand for the period between 4-5 when n=3 and 7-8 when n=6. Additional Information: 1.When n=3, Weights of 50%, 30% and 20% with 50% applying to the most recent demand 2.When n=6, Weights of 30%, 25%, 20%, 10%, 10% and 5% with 30% applying to the most recent demand

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16. Exponential Smoothing 16 Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method

17. Exponential Smoothing (cont.) 17

18. Effect of Smoothing Constant 18

19. Exponential Smoothing ( α =0.30) 19

20. Exponential Smoothing (cont.) 20

21. Exponential Smoothing (cont.) 21

22. Regression analysis 22

26. Seasonal Pattern 26  Multiplicative Seasonal Method: A method whereby seasonal factors are multiplied by an estimate of average demand to arrive at seasonal forecast.

31. Classroom Exercise 31  Royal Mail experiences a seasonal pattern of its daily mail volume every week. The following data for three representative weeks are expressed in hundreds of pieces of mail: DAYWeek 1Week 2Week 3Week 4 Sunday5108? Monday20 18? Tuesday303240? Wednesday354045? Thursday455052? Friday7075 ? Saturday152025? Total220247263? Requirement: Using the data of last three weeks forecast the daily demand of week 4.

32. 32 KFC experiences seasonal pattern of its daily Chicken Burger sales volume every week. The following data of seven days for two representative weeks are expressed in hundreds of pieces of burger sales: DAYWeek 1Week 2Week 3 Saturday56 Sunday1012 Monday3236 Tuesday1210 Wednesday810 Thursday2022 Friday7075 Requirement: Using the data of last two weeks, forecast the daily demand of week 3 (suggested Method: Seasonal Multiplicative).

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