• Like
Demand forecasting
Upcoming SlideShare
Loading in...5
×
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to like this
No Downloads

Views

Total Views
457
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
23
Comments
1
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. ECONOMICS FOR ENGINEERS
  • 2. PRESENTATION ON •DEMAND FORECASTING •PROBLEMS IN DEMAND FORECASTING •HOW TO OVERCOME THEM
  • 3. TERMINOLOGY • DEMAND  The demand for anything at a given price, is the amount of it which will be bought per unit of a time at that price. • FORECASTING  Forecasting generally means prediction or estimation or a guess.
  • 4. DEMAND FORECASTING
  • 5. SO, DEMAND FORECASTING?? • DEMAND FORECASTING = DEMAND + FORECASTING. • Estimating the demand(increase in need) which could probably arise prior to the release or introduction of a new product. • Prediction of the result of introduction of a new product into the market(to make the product a success).
  • 6. BASIC TYPES DEMAND FORECASTING SHORT TERM FORECAST LONG TERM FORECAST
  • 7. • The basic types are based on the duration of time over which the demand is forecasted. 1. SHORT TERM FORECAST: • • Forecasting the demand for about a year and is periodically reviewed quarterly or half yearly. Used in marketing activities, which don’t require a huge investment. 2. LONG TERM FORECAST: • • • Forecast the demand for over a long period of time. Basically useful in large industrial sectors during investment on a new unit or expansion of current unit. Margin of error is large due to the duration involved.
  • 8. TECHNIQUES IN DEMAND FORECASTING TECHNIQUES ANALYTICAL STATISTICAL
  • 9. DIFFERENT ANALYTICAL METHODS 
  • 10. 1. USER EXPECTATION METHOD • • • • • • Depends upon the survey of customers intention. Direct interaction with customers. An easy and inexpensive way of knowing customer requirements. Conclusions can be done after knowing the desires of consumers. Requires huge manpower or technical knowledge. Misjudgment among people about the product.
  • 11. 2. COLLECTIVE OPINION METHOD • Depends on the opinion from the salesmen about the demand for a particular type of commodity in a locality. • The salesmen report to the firm about the probability of success of the firm’s product and their expectation of results. • Known as the HUNCH method of forecasting. HUNCH  A feeling by intuition. • Requires a comparatively lesser manpower. • Commissions/periodic settlement to the salesmen.
  • 12. 3. EXPERTS OPINION METHOD • A group are experts in that particular field of the product are consulted for their opinions. • The opinions are then discussed by a panel to arrive at a conclusion which is basically the average of all the opinions. • Experts are consulted, so there is no risk involved. • Based on opinions from multiple people, so there is one way or the other to succeed always.
  • 13. VARIOUS STATISTICAL METHODS 
  • 14. 1. TREND PROJECTION METHOD • Based on the past demand record of a product. • The demand for the product are arranged w.r.to time and this is called as the “TIME SERIES”. • No knowledge of economics is required since it is based on trend. • Valuable tool for freshers in the market. • Past can’t predict the future always. • Suitable only for Long term forecast.
  • 15. 2. GRAPHICAL METHOD • Plotting the sales of a firm over a period of years and the improvement in sales of the type of commodity to be introduced.
  • 16. SIGNIFICANCE OF FORECASTING • It provides appropriate production scheduling so as to avoid the problem of over-production & problem of short supply. • Helping the firms to reduce the cost for purchasing raw material. • Manufacturers prefer “Make to Stock” rather than “Make to Order". Demand Forecasting helps to plan ahead and provide the finished goods to their customers as soon as possible.
  • 17. PROBLEMS IN FORECASTING DEMAND FOR A NEW PRODUCT • “TO ERR IS HUMAN”. • “ONE CANNOT BE RIGHT ALWAYS UNLESS HE IS GOD”.
  • 18. ERRORS • There are many technical, human, data accuracy and prediction errors which may lead to inaccuracy in demand forecasting. • This diminishes the purpose of forecasting the demand for a product.
  • 19. COMMON ERRORS • Probability of success is just the wish of the producer and the benefit of doubt is favored towards profit. • Prediction is normally based on the past history of the commodity demand. History need not be the case at all times in future.
  • 20. MEASUREMENT ERRORS • Record of data need not be accurate always(depends on the type of record). • Rounding off concept often affects the accuracy of data, which is used in forecasting. • Changes that occur at the time of measurement is not considered and is always assumed that there is no change during measurement. • Common human data entry errors.
  • 21. DEMAND VARIABILITY • Expected demand may sometimes be different from the actual demand. • Demand is affected by various factors such as Price, Season, Quality, Govt policies…,
  • 22. SCENARIO • Suppose a jewellery shop is to be opened shortly. The demand forecast insists a certain period for the opening of the shop based on the calculated demand. • BUT unexpectedly too many jewel shops open at that time with attractive offers. • Here, the DEMAND BECOMES LESSER THAN THE EXPECTED DEMAND DUE TO COMPETITION.
  • 23. SUPPLY INCONSISTENCY • Due to various factors, a firm may not be able to meet with the demands believing the demand forecast. • At that time the actual demand might have been very much larger or much smaller than the expected demand.
  • 24. SCENARIO • Suppose a HELMET manufacturer has expected a certain demand and has manufactured accordingly. • Suddenly the GOVERNMENT ISSUES AN ORDER that it is mandatory that both the persons must wear helmet. • This increases the demand for the helmets but the manufacturer was not prepared to increase the firm’s production. • Thus it leads to inability to supply accordingly due to GOVT POLICY CHANGE.
  • 25. THE BULLWHIP EFFECT • Fluctuation in the demand - supply equilibrium.
  • 26. Reduction in raw material purchase Demand decreases Reduction in the supply Purchase and production increases Increase in Demand Rise in raw material and product price
  • 27. MODEL MISMATCHES • The products which we introduce might be suitable only for a certain range of people. • The range may refer to either the affordability or the need for the product. • Sometimes demand forecasting may concentrate only on the people who need it which results in the success of the product only among such people. (Partial success only)
  • 28. HOW TO OVERCOME ERRORS IN DEMAND FORECASTING • Two approaches can reduce the impact and effects of the inaccuracy in demand forecasting.  Collaboration between customers and suppliers to improve efficiency of forecasting.  Quicker response to demand changes.
  • 29. SUPPLY MANAGEMENT • The supplier must be able to meet the situations where the actual demand is higher than the expected demand. • To meet this, the supplier can account with the concept of:  CYCLE STOCK: To meet with the forecasted demand. BUFFER STOCK: To manage higher demand situations.
  • 30. IMPROVING FORECAST ACCURACY • This can be achieved by COLLABORATION. • Multilevel forecasting is performed by multiple teams and increase inputs for forecasting. Forecast from salesmen. Survey from Consumers. Past demand history. Current trend. • Conclusion based on the above forecasts will be more accurate than the single level forecast.
  • 31. AVOIDING COMPROMISES • Compromises at various stages of forecasting must be avoided. • Examples of compromises:  Forecast review quarterly instead of monthly.  Avoiding data records of minorities.  Reducing forecast to reduce cost.
  • 32. MORE FORECAST IN LESSER TIME • Longer the forecast period, greater the deviation of quantity of demand estimated. • Performing forecasts frequently and reacting to it instantaneously is important. Content Daily Weekly Monthly Days in the cycle 1 5 22 Buffer stock requirement in % 10 23 46
  • 33. CHANGES TO BE DONE • Larger focus on the source that varies the demand variability the most. • Parallel processing of various steps in forecast enabled. • Processes in forecast that does not add any value must be avoided. • The step which adds more value and that takes more time must be crucially dealt with large time. • Changes in technology that increases forecast accuracy and reduces time is essential.