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- 1. DEMAND FORECASTING -PRESEN TED BY- 2. Gautam Agarwal 3. Hitesh Agarwal 11. Kandarp Desai 15. Vaibhav Gumaste 26. Omkar Kelkar 29. Aditya Krishnan
- 2. OBJECTIVES FOR DEMANDFORECASTING• Understand the role of demand forecasting• Identify reasons for demand forecasting• Study of Forecasting methodologies• Selecting the right forecasting method.• Measurement of forecasting errors.
- 3. INTRODUCTION Predicting future demand of products/services of an organisation Forecast = To estimate/calculate in advance. Guiding factor- for deciding the capacity and location of new facility. The staffing decisions should be in line with the demand forecasts. It affects administrative plans and policies.
- 4. To minimize Maximize losses of gains for To offset uncontrollabl actions of the actions e events organisation of competitor Maximize gains for external Materialenvironmen REASONS FOR requiremen t DEMAND t planning FORECASTING To develop In decision policies making for To provide budgeting To develop adequate administrativ staff to e plans support requirement s
- 5. VARIOUS METHODS
- 6. Qualitative Analysis1) Consumers Survey: Complete Enumeration Method The forecaster undertakes a complete survey of allconsumers whose demand he intends to forecast.Once this information is collected, the sales forecastsare obtained by simply adding the probable demandsof all consumers.The principle merit of this method is that theforecaster does not introduce any bias or valuejudgment of his own.But it is a very tedious and cumbersome process; it isnot feasible where a large number of consumers areinvolved
- 7. 2) Consumer Survey-Sample Survey MethodUnder this method, the forecaster selects a fewconsuming units out of the relevant population and thencollects data on their probable demands for the productduring the forecast period.The total demand of sample units is finally blown up togenerate the total demand forecast.Compared to the former survey, this method is lesstedious and less costly, and subject to less data error;but the choice of sample is very critical. If the sample is properly chosen, then it will yielddependable results; otherwise there may be samplingerror.
- 8. 3) Sales Force CompositeThe sales force composite method of forecastingstarts with the forecaster asking for opinions aboutfuture sales from every member of the sales staffcurrently working in the field.Each sales force member states how many salesshe thinks shell make during the given forecastingperiod.Department managers look over and adjustsalespeoples predictions before turning thenumbers over for forecasting.Predictions are usually checked against historical
- 9. 4) Executive Opinion Poll Forecasters using the executive opinion or expertopinion method poll executives or experts fromwithin the company and ask their opinion on theoptional sales for the given forecasting time period.The forecaster will then average the individualjudgments or try for a group consensus.Executive opinion polls are often used to verify (orinvalidate) other qualitative methods, especiallysales force composites.
- 10. 5) Delphi Method Dis-advantages: Biased , non-response situation , time consuming. Advantages: No pressure.
- 11. 6) Past AnalogiesSometimes data on the exact time of a particularevent (or events) are available.Experts use cases where similar events haveoccurred at different times or in different geographicareas and compare them to the issue at hand. If occurrence or no-occurrence of an event is on aregular basis, then the data can be thought of ashaving a repeated measurement structure. It helps to select a large number of similar situations,rather than basing a decision on comparison with onlyone case.
- 12. Quantitative analysis Forecast future demand by using quantitative data from the past and extrapolating it to make forecasts of future levels. Demand for existing products can be forecasted by using this method. They are used when historical data is available. There are of two types of techniques 1. Time series analysis 2. Causal analysis
- 13. Time series analysis Time series of historical demand data with respect to time intervals (periods) in the past is used to make predictions for the future demand.Following are the five popular methods Simple moving average Simple exponential smoothing Holt’s double- exponential smoothing Winters’ triple- exponential smoothing Forecasting by Linear regression analysis
- 14. Simple moving average It is suitable under situations where there is neither a growth nor a decline trend shown by the actual past data for forecasting. For eg : If we have past data of the actual sales of a product for the months of Jan, Feb and March, we take the simple average of these sales figures for the three months. This simple average becomes the forecast for the next month i.e April.
- 15. Simple Moving Average Method Example : four week moving averageExample: Three Period Moving average.Given below are the actual sale of a toy for the past 5 weeks. We need to predict the sales forthe 6th week. W EEK ACTUAL FORECAST CALCULATION SALES (IN UNITS) (IN UNITS)1 16342 18213 20694 19525 2178 1869 (1634+1821+2069+ 1952)/46 2005 (1821+2069+1952+2178)/4
- 16. Weighted Moving Average MethodThe data in the recent past periods should be given more weight orimportance compared to the data in the periods far off from thecurrent time.W EEK ACTUAL FOR ECAST CALCULATION SALE (IN (IN UNITS) UNITS)1 1634(0.1)2 1821(0.2)3 2069(0.3)4 1952(0.4)5 1929 (1634*0.1+1821*0.2+20 69*0.3+1952*0.4)/ 1
- 17. Linear Regression Analysis It is applied in situations where two variables are linearly correlated to each other. In time series analysis, the independent variable is time while the dependent variable is the actual demand in the past. A graph showing the points for the corresponding values of two variables is called scatter diagram. These points should display an approximately linear trend.
- 18. Example of linear regressionY= 1060X + 440 is the regression equationInterpretation: As the age of the car increase by 1 yearthe maintenance cost is expected to increase by Rs1060.
- 19. How to choose a demand forecastingtechnique Objectives of a forecast Cost involved Time perspective (short run or long run) Complexity of the technique Nature and quality of available data
- 20. QUANTITATIVEANALYSIS EXPONENTIAL SMOOTHING METHODS
- 21. The problem with Moving AveragesMethodsForecast lags with increasing demandForecast leads with decreasing demand
- 22. Exponential SmoothingMethods Single Exponential Smoothing–– Similar to single Moving Average Double (Holt’s) Exponential Smoothing–– Similar to double Moving Average–– Estimates trend Triple (Winter’s) Exponential Smoothing–– Estimates trend and seasonality
- 23. Single Exponential Smoothing
- 24. Holt’s Exponential smoothing(Double Exponential Smoothing) Sometimes called exponential smoothing with trend. If trend exists, single exponential smoothing may need adjustment. There is a need to add a second smoothing constant to account for trend. It adds a growth factor (or trend factor) to the smoothing equation as a way of adjusting for the trend
- 25. Winter’s Exponential Smoothing(Triple Exponential Smoothing) Winter’s exponential smoothing model is the second extension of the basic Exponential smoothing model. It is used for data that exhibit both trend and seasonality. It is a three parameter model that is an extension of Holt’s method. An additional equation adjusts the model for the seasonal component.
- 26. TREND ANALYSIS Forecasting method used in causal quantitative analysis based upon linear regression analysis. The dependent variable should have a causal relationship with the independent variable. For eg. Dependent variable : No. of units produced Independent variable : No. of labors present
- 27. Trend Analysis Chart
- 28. MEASUREMENT OFFORECASTING ERRORS Running sum of forecast errors Mean forecast error Mean absolute deviation Mean squared error Mean absolute percentage error Tracking signal
- 29. Tracking signal Dynamic measure of forecasting errors as can be updated after every time new actual demand data is added. TS=RSFE/MAD In ideal forecast system ,TS should hover closely around zero. Region above centre zero line shows Actual demand > forecast Region below centre zero line shows Actual demand < forecast
- 30. Tracking signal plotted against number of days
- 31. Forecast Control Limits Used in controlling the forecasting errors. Here assumed that forecasting errors follow a normal distribution curve and are randomly distributed around the mean(assumed,=0). Forecasting system is said to be performing well if all the forecast error points fall within the control limit. Upper control limit= 0+3s (s=(MSE)½) Lower control limit= 0-3s (s=(MSE)½) Any point not lying in the limit is a signal to forecaster to look for cause of variation.

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