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Demand forecasting
Demand forecasting is a crucial tool for businesses to predict future product demand, thereby mitigating uncertainty in a volatile environment. The document outlines various objectives and methods for both short-term and long-term demand forecasting, including statistical, survey, and econometric approaches. Key factors influencing demand forecasts and the characteristics of effective forecasting are also discussed.
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Demand forecasting
- 1. Demand Forecasting JITHIN KTHOMAS Berchmans Institute of management Studies
- 2. Why Demand Forecasting •Business environment is uncertain, volatile, dynamic and risky. • Better business decisions can be taken if uncertainty can be eliminated or reduced. • Demand forecasting predicting the future demand for a firms product, is one way to reduce uncertainty.
- 3. Demand Forecasting • Forecastingis predication of a future event • Demand Forecasting is the prediction of a future situation under given constraints.
- 4. Objectives of DemandForecasting • Short term objectives 1. Price Policy Formulation 2. Proper control of sales 3. Arrangement of finance 4. Regular supply of raw material 5. Regular availability of labor 6. Formulation of production policy
- 5. Objectives of DemandForecasting • Long term Objectives 1. Labor requirement 2. Arrangement of Finance 3. Expansion
- 6. Types of DemandForecasting • Short term demand forecasting • Long term demand forecasting • Medium term demand forecasting
- 7. Methods of DemandForecasting Survey method Consu mer Survey Collect ive Survey Statistical method Trend Method Regression Method Least Square Leading Indicator Simultaneous Equation
- 8. Subjective (Qualitative) Method •Relay on human judgment and opinion. – Consumers Survey • Complete Enumeration survey • Sample survey • End user method – Sales Force Composite – Market Simulation – Test Marketing – Expert Opinion
- 9. Trend Projection • Assumption– – future events are a continuation of the past – Historical data can be used to predict the future • Predictions – Finding a trend for a specific year – Finding seasonal fluctuations in the variable – Predicting turning points in the future movement of the variable
- 10. Methods of FindingTrend 1. Fitting Trend Line by Observation – Plotting of annual sales on a graph and then estimating by observation where the trend line lies 2. Time Series Analysis employing Least Squares Method – With the help of statistic a trend line is fitted to the data called ‘the line of best fit’ and then extrapolated.
- 11. Methods of FindingTrend 3. Forecasting through Decomposing a Time Series 4. Smoothing Methods – Moving averages – Exponential Smoothing 5. ARIMA Method – Auto Regressive Integrated Moving Averages
- 12. Barometric or LeadingIndicator Technique • Relationship can exist among various economic time series • Lagging series – Lagging Indicators – Data moves up and down behind the series being compared • Coincident series – Coincident Indicators – Data moves up and down with some other series
- 13. Barometric or LeadingIndicator Technique • Leading series – Leading Indicators – Data moves ahead of the series being compared Application for housing loan Demand for construction material Birth rate of children Demand of seats in school
- 14. Correlation and RegressionMethod • This method recognises the fact a number of factors like own advertising, competitors advertising, competitors price, weather condition etc may affect sales. • Variables influencing sales is identified through correlation • Regression equation specifies relationship between each variable and sales.
- 15. Steps in DemandForecasting 1. Problem definition 2. Gathering information 3. Preliminary exploratory analysis 4. Choosing the fitting model 5. Using the evaluating model
- 16. Econometric Models • EconometricModels tries to identify all those economic and demographic variables that influences the future value of the variable under forecasting and build up a cause-effect relationship.
- 17. Simulations Equation method •Simulations Equation method involves simulations consideration of all the variables, as it is believed that every variable influences the other variable in an econometric decision environment.
- 18. Range Long Medium Short Horizon 5 years 1 –2 years Up to 1 year Application Facility, capacity, product planning Staffing plan, Aggregate production Plan Purchasing , Detailed job scheduling Methods Economic, Demographic, Market information, Technology Time series, Regression Trend exploration , Graphical Method, Exponential smoothing
- 19. Qualities of goodDemand Forecasting 1. Simple 2. Accurate 3. Easy Availability 4. Economy 5. Capacity to update forecasts
- 20. Limitations • Change inFashion • Consumers Psychology • Lack of Past Data • Uneconomical • Lack of Experienced Experts