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Operations
Management
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UNIT II
Forecasting:
It is an estimate of the future; It is an
inference based on a large mass of data onpast performance and the prospects of
future scope .
Forecasting the future Sales is an essentialrequirement of any organization.
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Types of Forecasts
Economic forecasts
Address business cycle – inflation rate, money
supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts Predict sales of existing products and services
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Forecasting:
Types: (Based on the period of forecast)
Short Term Forecast : Less than One year
(can be monthly, weekly, fortnightly,Quarterly, half-yearly etc.,)
Long Term Forecast : > 1 year
(say, 5 years, 10 years, 15 years etc.,)
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Forecasting
Why Short –term Forecast:?
For materials control, loading, scheduling
Why Long term forecast?For strategic planning, financial & investment
planning
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Forecasting
Methods of Forecasting:
Qualitative Techniques : Subjective in nature; based on
expert opinions. (Example: Delphi technique)
Analytical Techniques: past data are taken as a guidefor predicting the future. (Example: Time-series
analysis)
Causal forecasting: Cause & effect relationship is
determined between the dependent variable (eg.: Sales)and the independent variable(s) and expressed in the
form of a mathematical relationship
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Forecasting
Qualitative Methods:
Ex: Market Research ( useful for new products)
Panel Consensus (or Jury of experts’ opinion)
– A panel of experts meets, discusses and arrives at theforecast.
Delphi Technique: A panel of experts is drawn; only the Moderator
knows who are the panel members; The panel members do not
meet face to face ; questions are sent to panel members -> Answers obtained -> Answers of all members distributed to panel
members -> panel members refine/modify their estimate ->process
repeated till consensus is reached. Moderator’s Role is important.
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Forecasting
Quantitative Method (or Analytical Techniques).
Historical Analogy:
Relies on using data of similar existing product to predict
demand for another product.
Time Series Analysis: (Uses past data on a product to
predict the future forecast for that product)
1.Curve Fitting Method
2. Moving Average (or Simple Moving Average) Method
3. Weighted Moving Average Method
4. Exponential Smoothing Method
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Forecasting
Causal Method:
Demand depends on a number of factors like price, price
of substitutes, competitor’s price, income level of
consumers ……. Establish a relationship between demand and the factors
that affect demand.
Ex:
Econometric models,
Linear Regression
Multiple Regression
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Forecasting
Seven Steps in Forecasting”
1. Determine the use of the forecast
2. Select the items to be forecast3.Determine the time horizon of the forecast
4. Select the forecasting Models
5. Gather the Data6. Make the forecast
7. Validate and Implement the results
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Monitoring & controlling forecast
Adaptive smoothing
Focus Forecasting
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Adaptive Smoothing
It’s possible to use the computer tocontinually monitor forecast error andadjust the values of the and coefficients
used in exponential smoothing tocontinually minimize forecast error
This technique is called adaptivesmoothing
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Focus Forecasting
Principles:
1. Sophisticated forecasting models are
not always better than simple ones
2. There is no single technique thatshould be used for all products or
services
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Focus Forecasting……
This approach uses historical data totest multiple forecasting models forindividual items
The forecasting model with the lowesterror is then used to forecast the nextdemand
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Forecasting in Service Sector
Presents unusual challenges
Special need for short term records
Needs differ greatly as function of industryand product
Holidays and other calendar events
Unusual events
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© 2011 Pearson Education, Inc.publishing as Prentice Hall
FedEx Call Center Forecast
Figure 4.12
12% –
10% –
8% –
6% –
4% –
2% –
0% –
Hour of dayA.M. P.M.
2 4 6 8 10 12 2 4 6 8 10 12
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© 2011 Pearson Education, Inc.publishing as Prentice Hall
Fast Food Restaurant Forecast20% –
15% –
10% –
5% –
11-12 1-2 3-4 5-6 7-8 9-1012-1 2-3 4-5 6-7 8-9 10-11
(Lunchtime) (Dinnertime)
Hour of day
P e r c e n t a
g e
o f s a l e s
Figure 4.12
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Essentials of a good ForecastingMethod
Simplicity
Accuracy
Economy Stability (i.e, the method should be such
that expected future changes are kept at
minimum)