Forecasting (Operation Planning & Control)
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Transcript of 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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Forecasting
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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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Qualitative Analysis
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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Qualitative Analysis
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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Qualitative Analysis
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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Qualitative Analysis
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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Qualitative Analysis
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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Quantitative Analysis
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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Quantitative Analysis
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
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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
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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
19VEIMSR Class 6 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
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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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Models for forecasting
Extrapolative methodsextrapolate past data
Causal modelscause-effect relationship (new housing)
Subjective judgment using qualitative data
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Forecasting
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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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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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27VEIMSR Class 6 Forecasting
Exponential Smoothening Method
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Forecasting
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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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Forecasting
30VEIMSR Class 6 Forecasting
Extracting components of time series
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Extracting components of time series
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Estimating Trend using Linear Regression
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Estimating Trend using Linear Regression
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
34VEIMSR Class 6 Forecasting
Estimating Trend using Linear Regression
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
35VEIMSR Class 6 Forecasting
Estimating Trend using Linear Regression
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.
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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F ti
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Forecasting
37VEIMSR Class 6 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
38VEIMSR Class 6 Forecasting
Extracting Seasonal component
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
39VEIMSR Class 6 Forecasting
Extracting Seasonal component
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 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
40VEIMSR Class 6 Forecasting
Extracting Seasonal component
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 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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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
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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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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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Managerial issues in Forecasting
Plant Layout
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Plant Layout
Summary
End of Topic