unit 2 - operations mgt - forecastinf

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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)