Demand forecasting

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excellent ppt made by me related to demand forecasting under operations management..

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Demand forecasting

  1. 1. Presented by - Ajay Chandak 65 Rajesh Devadiga 73 Kunal Hiwanj 75 Navin Kumar 84 Milind Wagh 117 Vaibhav Yevlekar 118
  2. 2. What is demand Forecasting ? “PREDICTIONS ARE USUALLY DIFFICULT, ESPECIALLY ABOUT THE FUTURE” Prediction or estimation of a future situation, under given conditions. Classified into categories: (i) Passive forecasts (ii)Active forecasts Important aid in effective and efficient planning It is backbone of any business
  3. 3. Importance of demand forecasting Crucial to supplier, manufacturer or retailer Business decisions Planning for future finished goods accurate demand forecasts lead to efficient operations and high levels of customer service Improve quality & effectiveness of product
  4. 4. Levels of Demand Forecasting1) Micro Level- Demand forecasting by individuals business firm for forecasting the demand for its product.2) Industrial Level- Demand estimate for the product of the industry3) Macro Level- Aggregate demand forecasting for industrial output at the national level- it is based on the national income/ aggregate expenditure of the company.
  5. 5. Types Of Demand Forecasting
  6. 6. Factors determining demand forecasting Time factor Level of forecasting General or Specific forecasting Problems & methods of forecasting Classification of goods Knowledge of different market conditions Other factors
  7. 7. Approach of forecasting Qualitative FORECASTING Quantitative
  8. 8. Qualitative Forecasting approach I. Judgmental approach  Surveys  Consensus methods  Delphi method II. Experimental approach  Test marketing  Customer buying database  Customer panels
  9. 9. Advantages & Disadvantages ofQualitative ForecastingAdvantages :-o Ability to predict changeso Flexibilityo AmbiguityDisadvantages :-o Accurate forecast is not possibleo Judgmental approacho False/ inadequate information
  10. 10. Quantitative Forecasting Approach Relationship approach  Econometric models  Life cycle models  Input-output models Time series approach  Static models  Adaptive models
  11. 11. Forecasting Examples Examples from Projects:  Demand for tellers in a bank;  Traffic flow at a major junction  Pre-poll opinion survey amongst voters  Demand for automobiles or consumer durables  Segmented demand for varying food types in a restaurant  Area demand for frozen foods within a locality Example from Retail Industry: American Hospital Supply Corp.  70,000 items;  25 stocking locations;  Store 3 years of data (63 million data points);  Update forecasts monthly;  21 million forecast updates per year.

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