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

1. Presented by -
Ajay Chandak 65
Rajesh Devadiga 73
Kunal Hiwanj 75
Navin Kumar 84
Milind Wagh 117
Vaibhav Yevlekar 118

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. 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. Levels of Demand Forecasting
1) 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 industry
3) 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. Types Of Demand Forecasting

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. Approach of forecasting
Qualitative
FORECASTING
Quantitative

8. Qualitative Forecasting approach
I. Judgmental approach
 Surveys
 Consensus methods
 Delphi method
II. Experimental approach
 Test marketing
 Customer buying database
 Customer panels

9. Advantages & Disadvantages of
Qualitative Forecasting
Advantages :-
o Ability to predict changes
o Flexibility
o Ambiguity
Disadvantages :-
o Accurate forecast is not possible
o Judgmental approach
o False/ inadequate information

10. Quantitative Forecasting Approach
 Relationship approach
 Econometric models
 Life cycle models
 Input-output models
 Time series approach
 Static models
 Adaptive models

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.