Forecasting (Operation Planning & Control)

8/10/2019 Forecasting (Operation Planning & Control)

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Forecasting

VEIMSRClass 6 Forecasting

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Forecasting

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FO

RE

CA

STING

N

EED

Input to aggregate production planning and MRP

Decision making with regard to facility capacity planning and capital budgeting

To maximize gain from events occurring in the external environment

To maximize gain from events, which are result of action of organization

To minimize losses associated with events in the external environment

To offset actions of competitor organization

To help develop policies for external entities

Assist frame organizations policies and plans


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Forecasting

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Demand Forecasting

Qualitative analysis Quantitative analysis

Causal analysisTime series

Simple

MovingAverage

Simple

Exponentialsmoothening

Holts double

exponential

smoothening

Winters

triple

exponential

smoothening

Forecast by

linear regression

analysis

Trend

analysis

Customer

Survey

Sales force

composite

Executive

opinion

Delphi

Method

Past

analogy


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Qualitative Analysis

Subjective in nature

Based on the judgment of experts(at times)

Useful when no historical data available to do quantitative analysis

No Quick fix forecasts

Ex: launch of new plant, innovative product

Economical and political turmoilhistorical data redundant


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Forecasting

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Customer Survey

Demand estimated from customer

Sampling - Practically not possible to connect to all potential

Questionnaireframe questions to elicit true response

Implementation and analysiscarefully handled to ensure

conclusions reflect exact pulse

Surveytime consuming, expensive,

changing preferences info valuable


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Forecasting

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Sales Force Composite

Sales forceestimate of their territories

3 forecastspessimistic, most likely and optimistic

Disadvantages

May not be as accurate as customers preferences or

intentions

Influenced by recent experiences

Less expensive than customer survey


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Executive opinion

Top executive juryall functional area give opinion/ forecast

Usually for new productno past history available

Dependent on individual experiencecertain opinions may

overshadow

Accountabilityentire group rather than individual


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Delphi Method

Questionnaire sent to experts from diverse streamsseeking opinion on

forecast

Usually highly advanced technologyusage of broad band internet

May not be only top executivesarea experts

Responses kept anonymous

Responses compiled and summarized

New questionnaire with summary of these responses, extreme vies of forecast

as compared to average forecast

Sent back to same expertsdiscretion given to revisitarrive at consensus

Advexperts global without much expense

Disadvanonymitytakes away accountability and responsibility


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Forecasting

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Past Analogy

Usually for new products, analogy of sales growth trends of other existing

products taken

Productssubstitutes , complementary products, products belonging to same

income group customers

Co launching new designer watchesstudies buying patterns of fashionable

sunglasses, clothes and designer products


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Forecasting

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Quantitative Analysis

Simple Moving Average

Suitable in situations where there is neither any growth nor decline

Horizontal trend line

There could be seasonal variations experience


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Forecasting

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Simple exponential smoothening

Simple in application

Needs limited datalast periods actual demand and its forecast

Ft= Ft-1+ (At-1Ft-1)

is the smoothening constantvalue range between 0 -1

termed exponential because every period in the past is given a

weight age of (1- ) n number of periods in the past


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Forecasting

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Holts double exponential smoothening

Suitable when actual demand follows increasing or decreasing demand

In simple smootheningforecast lags behind actual demand, whenever

actual demand follows increasing or decreasing trend

Here use of 2 smoothening constants and to adjust trend effect

Winters triple exponential smoothening

Used when there is increasing or decreasing trend with seasonal

variation


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Forecasting

14VEIMSR Class 6 Forecasting

Introduction:

Important activity in manufacturing and service organization

Various departments involvedfinance, marketing, production, materials,

operations and HR

Marketingproduction - materialsfinance roles

e.g. Petrochemicalslong term contracts for feedstockcompetitive advantage

Poly ethylene forecastingexchange rate, geo political impact on demand/supply

Domesticcapacity projection of competitors, excise, custom tariffs

Aggregate datamatch with avl and requirement, tertiaryregionalnational

Important and vital role in every organization


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Forecasting


Introduction:

Output used forcapacity balancing for planning yeardebottlenecking,

reschedule maintenance programs, technology improvement programs, detailed

production planning schedule for year (disaggregate data)

Help establish performance targets of various departments such as production,

materials and marketing setting up control systems.


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Forecasting


Forecasting role

An estimation tool

Way of addressing complex and uncertain environmentbus. decision making

Tool for predicting events related to operations planning and control

Vital pre requisite for the planning process in organization


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Forecasting


Forecasting Horizon

Short term forecasting

Medium term forecasting

Long term forecasting

Forecasting time horizon - some implications

Criterion Short Term Medium Term Long Term

Typical Duration 1 - 3 months 12 - 18 months 5 - 10 years

Nature of decisions Purely tactical Tactical as well strategic Purely strategic

Key considerations Random effects (short term) Seasonal and cyclical effectLong term trends and business

cycles

Nature of Data Mostly quantitative Subjective and quantitative Largely subjective

Degree of

uncertainityLow Significant High

Some examples

-Revising quarterly production

plan

-Rescheduling supply of raw

mtrl

-Annual production planning

-Capacity augmentation

-New Business Development

-New product introduction

-Facilities location decision


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Forecasting


Design of Forecasting System Identify anappropriatetime horizon

Build and validate a forecast model

Use output withmanagerial judgement


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Develop forecasting logic


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Sources of Data

Sales force estimatesshort term, mid course correction

POS Data system

Supply Chain Partners

Trade/ Industry Association JournalORG-MARG, Mgmt Cons

B2B Portals/ Market Places

Economic Surveys and Indicatorsmacro economic trends (HDTVs)

CSO, CMIE

Subjective Knowledge


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Forecasting


Models for forecasting

Extrapolative methodsextrapolate past data

Causal modelscause-effect relationship (new housing)

Subjective judgment using qualitative data


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Forecasting


Extrapolative methods for forecastingtime series

Very useful for short term forecastspredicting weekly/ monthly

Moving Average (MA)

Single parameternumber of periods

Simple to setup

Limitations:

If significant changes in pattern it reacts slowly (shift in

demand in Junereaction in September

For this you have Weighted Moving Average

Logical choice of n demand stable high value of n

appropriate and vice versa


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Extrapolative methods for forecastingtime series


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Forecasting


Exponential Smoothening Method

Past data are weighed in unequal fashion while estimating future

period forecast

Weights of past data die down in exponential fashion

Forecast of next period computed on the basis of the forecast for

the current period and the actual demand during current period

Difference is incorporated into next period


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Forecasting


Exponential Smoothening Method


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Forecasting


Extracting components of time series

Forecasting techniqueemploys time series data and deciphers

patterns in demand in the past & uses as the basis for forecasting

Components

Trend (T)long term secular movement in pattern

Seasonality(S)Fixed cycles time series - move period to period

Cyclic (C)Changes in business cycles

Random ( R)noise in the system

Forecast for demand = TxCxSxR


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Estimating Trend using Linear Regression


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Forecasting



Problem:A manufacturer of critical

components for two wheelers in the

Automotive sector is interested in

forecasting the trend of demand

during the next year as a key input

to its annual planning exercise.Information of past sales is

available for the last three years.

Extract trend component of time

series data and use it for predicting

future demand.

Actual Demand in the last three years ('000

units)

Period Actual Demand

Year 1 - Q1 360

Year 1 - Q2 438

Year 1 - Q3 359

Year 1 - Q4 406

Year 2 - Q1 393

Year 2 - Q2 465

Year 2 - Q3 387

Year 2 - Q4 464

Year 3 - Q1 505

Year 3 - Q2 618

Year 3 - Q3 443

Year 3 - Q4 540

i


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Forecasting



Problem:A manufacturer of

critical components

for two wheelers in

the Automotive

sector is interested in

forecasting the trend

of demand during thenext year as a key

input to its annual

planning exercise.

Information of past

sales is available for

the last three years.Extract trend

component of time

series data and use it

for predicting future

demand.

F i


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Forecasting



Problem:A manufacturer of

critical components

for two wheelers in

the Automotive


forecasting the trend

of demand during thenext year as a key

input to its annual

planning exercise.

Information of past

sales is available for

the last three years.Extract trend

component of time

series data and use it

for predicting future

demand.

Period X Y XY X

Year 1 - Q1 1 360 360 1

Year 1 - Q2 2 438 876 4

Year 1 - Q3 3 359 1077 9

Year 1 - Q4 4 406 1624 16

Year 2 - Q1 5 393 1965 25

Year 2 - Q2 6 465 2790 36

Year 2 - Q3 7 387 2709 49

Year 2 - Q4 8 464 3712 64

Year 3 - Q1 9 505 4545 81

Year 3 - Q2 10 618 6180 100

Year 3 - Q3 11 443 4873 121

Year 3 - Q4 12 540 6480 144

78 5378 37191 650


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Forecasting


Extracting Seasonal component

Requires developing indices for seasonality

Seasonality indices adjust forecast scaling up during periods of high

demand and scaling down during periods of low demand

Seasonality index is ratio of actual period demand with average demandfor the period

Use simple MA for obtaining average demand for every period in a time

series data that use that for computing the seasonality index

Example1: Extract seasonal component and adjust the forecast

obtained for seasonality

F ti


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Forecasting



Problem:A manufacturer of critical components for

two wheelers in the Automotive sector is

interested in forecasting the trend of

demand during the next year as a key

input to its annual planning exercise.

Information of past sales is available for

the last three years. Extract trendcomponent of time series data and use it

for predicting future demand. Extract

seasonal component and adjust the

forecast obtained for seasonality

F ti


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Forecasting




components for two

wheelers in the Automotive


forecasting the trend of

demand during the next

year as a key input to itsannual planning exercise.

Information of past sales is

available for the last three

years. Extract trend

component of time series

data and use it forpredicting future demand.

Extract seasonal

component and adjust the

forecast obtained for

seasonality

Period

Actual

Demand

4 Qtr movingavg of

demand

Avg demand

for period

Seasonality

index

Year 1 - Q1 360

Year 1 - Q2 438 390.75

Year 1 - Q3 359 399.00 394.875 0.909

Year 1 - Q4 406 405.75 402.375 1.009

Year 2 - Q1 393 412.75 409.250 0.960

Year 2 - Q2 465 427.25 420.000 1.107

Year 2 - Q3 387 455.25 441.250 0.877

Year 2 - Q4 464 493.50 474.375 0.978

Year 3 - Q1 505 507.50 500.500 1.009

Year 3 - Q2 618 526.50 517.000 1.195

Year 3 - Q3 443

Year 3 - Q4 540

F ti


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Forecasting




components for two

wheelers in the Automotive


forecasting the trend of

demand during the next

year as a key input to itsannual planning exercise.

Information of past sales is

available for the last three

years. Extract trend

component of time series

data and use it forpredicting future demand.

Extract seasonal

component and adjust the

forecast obtained for

seasonality

Year 1 Year 2 Year 3 Average

Quarter 1 0.960 1.009 0.985

Quarter 2 1.107 1.195 1.151

Quarter 3 0.909 0.877 0.893

Quarter 4 1.009 0.978 0.994

X Trend

Forecast

Seasonality

index

Seasonality

adjusted

forecast

Year 4 - Q1 13 550 0.985 541

Year 4 - Q2 14 565 1.151 651

Year 4 - Q3 15 581 0.893 519

Year 4 - Q4 16 597 0.994 593


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Forecasting


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Casual method

Problem:A manufacturer of tricycles for children

in the age group of two to four years

commissioned a market research firm to

understand the factors that influence the

demand for its product. After some

detailed studies the market researchfirm concluded that the demand is a

simple linear function of the number of

newly married couples in the city. Based

on the assumption build a casual

method for forecasting the demand for

the product using the data given belowcollected for a residential area in a city.

Also estimate the demand for tricycles if

the nymber of new marriages is 150 and

250

X Y

New marriages Demand for tricycle

200 165

235 184

210 180

197 145

225 190

240 169

217 180

225 170

Forecasting


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Forecasting


Casual methodProblem:

A manufacturer of tricycles for

children in the age group of two

to four years commissioned a

market research firm to

understand the factors that

influence the demand for its

product. After some detailedstudies the market research firm

concluded that the demand is a

simple linear function of the

number of newly married couples

in the city. Based on the

assumption build a casual

method for forecasting thedemand for the product using the

data given below collected for a

residential area in a city. Also

estimate the demand for tricycles

if the nymber of new marriages is

150 and 250

X Y

XY XNew marriages

Demand for

tricycle

200 165 33,000 40,000

235 184 43,240 55,225

210 180 37,800 44,100

197 145 28,565 38,809

225 190 42,750 50,625

240 169 40,560 57,600

217 180 39,060 47,089

225 170 38,250 50,625

1749 1383 303,225 384,073

218.625 172.875

Forecasting


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Forecasting


Accuracy of forecastsForecast Error (FE)

Mean Absolute Deviation (MAD)

Mean Absolute Percentage error (MAPE)

Mean Squared Error (MSE)

Tracking Signal (TS)

Forecasting


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Forecasting


Managerial issues in Forecasting

Plant Layout


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Plant Layout

Summary

End of Topic

Forecasting (Operation Planning & Control)

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