1. Operations Management Demand Forecasting

2. Session Break Up Conceptual framework Software Demonstration Case Discussion

3. Demand forecasting Forecasting involves making calculated prediction that can be used in planning and decision making process It includes both long term investment of overall demand and short term estimates for each product and service

4. Forecasting Methods A- Qualitative Models Delphi Method Nominal group technique B -Time series Simple moving average Weighted moving average Exponential Smoothing C - Causal Model Regression Analysis

5. Demand Management A Independent Demand: Finished Goods B(4) C(2) D(2)E(1) D(3)F(2) Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc.

6. Delphi Method l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas. 2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants. 3. Summarize the results and redistribute them to the participants along with appropriate new questions. 4. Summarize again, refining forecasts and conditions, and again develop new questions. 5. Repeat Step 4 if necessary. Distribute the final results to all participants.

7. Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior. The formula for the simple moving average is: F t = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

8. Simple Moving Average Problem Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

9. Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods. w t = weight given to time period “t” occurrence. (Weights must add to one.) The formula for the moving average is:

10. Weighted Moving Average Problem Weights: t-1.5 t-2.3 t-3.2 Question: Given the weekly demand and weights, what is the forecast for the 4 th period or Week 4? Note that the weights place more emphasis on the most recent data, that is time period “t-1”.

11. Weighted Moving Average Problem (1) Solution F 4 = 0.5(720)+0.3(678)+0.2(650)=693.4

12. Exponential Smoothing Model Premise: The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting. F t = F t-1 +  (A t-1 - F t-1 )  = smoothing constant

13. Example A recent out break of Dengue in Delhi has resulted India’s premier institution AIIMS in chaotic situation. The Institute wants to forecast expected number of patient in the coming week with the help of past data to make necessary arrangement in terms of bed, medicine etc The MRO has furnished following information

14. Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5? Assume F 1 =D 1

15. F 1 =820+(0.5)(820-820)=820 F 3 =820+(0.5)(775-820)=797.75

16. Simple Linear Regression Question: Given the data below, what is the simple linear regression model that can be used to predict sales?

17. Error measures Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) – Average absolute error Mean Squared Error (MSE) – Average of squared error Mean Absolute Percent Error (MAPE) – Average absolute percent error

18. MAD, MSE, and MAPE MAD = Actualforecast   n MSE = Actualforecast ) - 2   n (

19. Example

20. Question: What is the MAD value given the forecast values in the table below? Mont h SalesForecast 1220N/A 2250255 3210205 4300320 5325315

22. The ideal MAD is zero. That would mean there is no forecasting error. The larger the MAD, the less the desirable the resulting model.

23. Thanks