visual 2-1 chapter 2 - forecasting fundamentals 2.1fundamental principles of forecasting 2.2major...
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Chapter 2 - Forecasting Fundamentals
2.1 Fundamental Principles of Forecasting
2.2 Major Categories of Forecasts
2.3 Forecast Errors
2.4 Computer Assistance
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Forecasting Introduction
Forecasting: is the starting point for all planning systems
– the actual customer demand– the expected demand
is an estimate made for some future period is necessary to develop future plans
This considers that the time it takes to produce an item exceeds the customers expectations for delivery.
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2.1 Fundamental Principles
Forecasting is a technique for using past experiences to project demand expectations for the future.
Not a prediction– a structured projected based on history
Several types of forecasts– long range, aggregated models (capacity)– short range (product demand)
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Principles of Forecasting
Forecasts Are almost always wrong Are more accurate for groups or families Are more accurate for nearer time periods Should include an estimate of error Are no substitute for calculated demand
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Why Forecast? To plan for the future by reducing uncertainty To anticipate and manage change To increase communication and integration of
planning teams To anticipate inventory and capacity demands and
manage lead times To project costs of operations into budgeting
processes To improve competitiveness and productivity through
decreased costs and improved delivery and responsiveness to customer needs
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What Is Riding on the Forecast?
Investment decisions Capital equipment decisions Inventory planning Capacity planning Operations budgets Lead-time management
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Planning Horizon and Time Periods
Time Periods (week numbers)
Forecast Length
Short Mid Long
Weeks Months Quarters
1 2 3 4 5 6 7 8 9 10111213 17 21 26 30 34 39 43 47 52 65 78 91 104
PlanningHorizon
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What Should Be Forecast?
Business plan Market direction 2 to 10 years
Sales and operations Product lines andfamilies
1 to 3 years
Master productionschedule
End item andoption
Months
Forecast Time Frame
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Sources of Demand
Demand can come from many sources: Consumers Customers Dealers Distributors Intercompany Service parts
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Decomposition of Data Purify the data Adjust the data Take out the baseline Identify demand components
– Trend
– Seasonality
– Nonannual cycle
– Random error Measure the random error Project the series Recompose
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Data Issues for Forecasting Availability of data Consistency of data Amount of history required Forecast frequency Frequency of model
reevaluation Cost and time issues Recording true demand Order date vs. ship date Product units vs. financial
units Level of aggregation Customer partnering
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2.1 Fundamental Principles
Forecasts are almost always wrong... The issue is not whether it is wrong The issue is how wrong will it be How do we plan to accommodate the error
– buffer stock– safety capacity
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2.1 Fundamental Principles
Forecasts are more accurate for groups or families of items...
Easier to develop a forecast for a product line rather than an individual item within in– MP3 market v. blue or white MP3
Individual errors cancel each other out as they are aggregated– more blue sold than white
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2.1 Fundamental Principles
Forecasts are more accurate for nearer time periods (shorter periods)...
Fewer disruptions in near period to impact product demand
Future period demand is usually less reliable– predict weather today v. late February
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2.1 Fundamental Principles
Every forecast should include an estimate of error...
First principle is how wrong is the forecast
Forecasts are no substitute for calculated demand...
Always use real data when available
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2.2 Major Categories of Forecasts
Two types of forecasts: Qualitative Quantitative
– time series– causal
Primary focus of this chapter is quantitative forecasting.
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2.2 Qualitative Forecasting
Generated from information that does not have a well-defined structure
Are useful when no past data is available– introduction of new product line– no sales history
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2.2 Qualitative Forecasting
Are based on intuition, informed opinion, or some external qualitative data
Tend to be subjective– developed (biased) from the experience of the
forecaster developing them• can be pessimistic or optimistic
Allows for rapid forecating May be only method available Used for individual products, not markets
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Qualitative Forecasting
Are used for business planning and forecasting for new products
Are used for medium-term to long-term forecasting
Common methods include:– Market surveys– Delphi or panel consensus– Life cycle analogies– Informed judgement
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2.1 Qualitative Forecasting
Market surveys– structured questionnaires given to potential
customers– solicit opinions about products or potentials– effective for short term forecasting
• if administered properly• if analyzed properly
– drawbacks include:• expensive• time-consuming
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2.1 Qualitative Forecasting
Delphi or panel consensus...– Uses a panel of experts in the market area of
interest– These experts use their experience and
knowledge of market issues to forecast and develop a consensus
– Panel consensus brings experts together for a consensus
– Delphi brings individual forecasts together for analysis
– Expensive, but accurate
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2.1 Qualitative Forecasting
Life cycle analogy...– Used when product or service is new– Assumes most products have a fairly defined
life cycle• growth during early stage• little growth during maturity stage• decline during latter stage
– What is the time frame?– How rapid will growth be?– How large will the demand be during maturity?
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2.1 Qualitative Forecasting
Informed judgement...– Quite common to use– One of the worst methods to use– Example:
• each salesperson develops own forecast• sales manager combines individual forecasts
– Some are too optimistic– Some will consider the forecast a quota– Some will be negatively or positively influenced
by recent events• higher or lower sales
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2.1 Anecdotal Example
Joe receives sales forecast...– 10,000 units of product X sold last few years– product X was sold to 6 user companies– the forecast is for 16,000 units of product X to
be sold in the coming year• no new customers• no new uses by existing customers• no new expansion plans by existing customers• no new expansion plans by customers of product
X• just because
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2.1 Anecdotal Example
What should Joe plan to make?– Some steel is very long lead time material– make 16,000 with demand 16,000…good– make 16,000 with demand at 10,000…bad
• expensive inventory left on hand
– make 10,000 with demand at 10,000…good– make 10,000 with demand at 16,000…bad
Correct answer is make 10,000 units.
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General Methods of Forecasting
Qualitative—based on intuitive or judgmental evaluation
Quantitative—based on computational projection of a numeric relationship
Intrinsic—based on historical patterns of the data itself from company data
Extrinsic—based on external patterns from information outside the company
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2.1 Quantitative Forecasting - Causal
Causal forecasting key characteristics...– Based on concept that one variable causes
another– Assumes causal variables can be measured
• leading indicators
– When accurate leading indicators are developed, they produce excellent results
– Development of the causal models educates the forecaster to other elements of the market
– Causal methods are typically used for markets– Time consuming and expensive to develop
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Leading Indicators
Indicator
Housing startsNumber of babiesHits on a Web siteHealth trends
Healthier lifestyle
Influences volume of
Building materialsBaby productse-commerce salesMedical suppliesNutritional productsFitness products
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Economic Cycle
0
5
10
15
20
25
30
35
1 3 5 7 9 11 13 15 17 19
Quarter
Sales by Quarter
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2.1 Quantitative Forecasting - Causal
Input-output models– large and complex models– examine flow of goods and services in the
entire economy– expensive to gather large volumes of data– used to project needs of entire markets, not
specific products Econometric models
– statistical analysis of various sectors of the economy
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2.1 Quantitative Forecasting - Causal Simulation models
– use of computers for simulations– require large data gathering– are fast and economical
• once data has populated the model
Regression analysis– a statistical method to define analytical
relationships between two or more variables– the independent or causal variable is the
leading indicator
Causal forecasts are called extrinsic forecasts.
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External (Extrinsic) Factors
New customers Plans of major
customers Government policies Regulatory concerns Economic conditions Environmental issues Global trends
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Factors Influencing Demand
Major factors influencing demand... General business and economic conditions Competitive factors Market trends Firm’s own plans…advertising, promotions,
pricing, product changes
____________________
____________________
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2.1 Quantitative F’cstg - Time Series Commonly used Assumes past is valid indicator of future Only real variable is time Are popular with operations managers
– they have little knowledge of external markets– used to make production plans– previous demand is typically readily available
Quantitative forecasts are based in internal data and are sometimes called intrinsic forecasts.
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Internal (Intrinsic) Factors
Product life-cycle management
Planned price changes
Changes in the sales force
Resource constraints Marketing and sales
promotion Advertising
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Demand Patterns
Dependent versus independent Only independent demand needs to be
forecast Dependent demand should never be
forecast
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Demand PatternsStable versus dynamic Stable demand retains same general
shape over time Dynamic demand tends to be erratic
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Characteristics of Demand
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Sources of Demand
Let’s look at demand.
All sources of demand must be identified:
Customers Spare parts Promotions Intracompany Other
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2.1 Quantitative F’cstg - Time Series Most time series forecasts capture
underlying patterns of past demand Random patterns
– assumes patterns are random– assumes customers do not demand products
and services in a uniform and predictable manner
– require some smoothing forecast method Trend patterns
– can be increasing or decreasing, linear or non– might be more easily forecast (up or down)
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2.1 Quantitative F’cstg - Time Series Seasonal pattern
– sometimes associated with seasons• summer gear• winter equipment
– are better defined as cyclical patterns• pattern of food sales at a restaurant
– breakfast, lunch, dinner– bread sales in a grocery store
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periods all for sales Average
sales average Period =index Seasonal
Seasonality
Measures the amount of seasonal variation of demand for a product
Relates the average demand in a particular period to the average demand for all periods
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Quarter Average Quarterly Sales/100 Seasonal Index
1 128/100 = 1.28
2 102/100 = 1.02
3 75/100 = 0.75
4 95/100 = 0.95
Total = 4.00
Sales History
Year Quarter Total
1 2 3 41 122 108 81 90 4012 130 100 73 96 3993 132 98 71 99 400
Average 128 102 75 95 400
Developing A Seasonal Sales Index
units 1004
400quarters all for sales Average ==
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Seasonality
0100200300400500600700800
J F M A M J J A S O N D
Sales in cases by month
Year 1
Year 2
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Seasonal Sales
Average Salesfor All Periods
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Data Preparation and Collection
Record data in terms needed for the forecast
Record circumstances relating to the data Record demand separately for different
customer groups
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Intrinsic Quantitative Techniques
Month SalesJanuary 92February 83March 66April 74May 75June 84July 84August 81September 75October 63November 91December 84January ?
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3
90) + 84 + (91
3
84) + 91 + (63 = forecastJanuary
Moving Averages
Forecast sales as an average of past months
An average of the past 3 months:
If January sales are 90, forecast for February
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Moving Average Forecasting
It can be used to filter out random variation Longer periods smooth out random
variation If a trend exists, it is hard to detect
– steel consumption, 12,000 MT v. 23,000 MT Manual calculations can be cumbersome
when dealing with more periods
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Moving Average Forecasting
Advantages A simple technique that is easy to calculate It can be used to filter out random variation Longer periods provide more smoothing
Limitations If a trend exists, it is hard to detect Moving averages lag trends
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Figure 2.5 - A Three-Period MA Forecast line is smoother than the actual
demand line– the more periods used, the smoother the
forecast line• less responsive to actual demand trend
– the fewer periods used, the more erratic the forecast line• less responsive to actual demand
– the forecast lags actual demand
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Figure 2.6 - Trend Analysis Forecast line lags the demand line
What are the implications of this if it is depicting the sale of a new product?
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3 Period Moving Averageperiod actual demand forecast
1 1342 1383 1404 1415 1606 1707 1788 188
19210 19911 20512 210
What is the forecast for period 4 through 12?
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3 Period Moving Averageperiod actual demand forecast
1 1342 1383 1404 1415 160 1376 170 1407 178 1478 188 157
192 16910 199 17911 205 18612 210 193
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Moving Average Graph
Forecast v. Demand with Trend
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12
Period
Dem
and
actual demand
forecast
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3 Period Moving Averageperiod actual demand forecast
1 1342 1323 1304 1255 120 1326 116 1297 110 1258 100 1209 90 11510 82 10911 74 10012 70 91
with negative trend
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Weighted Moving Average Graph
Forecast v. Demand with Trend
020406080
100120140160
1 2 3 4 5 6 7 8 9 10 11 12
Period
De
ma
nd
actual demand
forecast
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Weighted Moving Averages Same as moving average forecast Forecast line lags the demand line, but
some intelligence is added to improve the accuracy
A weight is added by the forecaster to help add more weight to some periods over others
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Weighted Moving Average
period actual demand forecast1 1342 1323 1304 125 1315 120 1286 116 1247 110 1198 100 1149 90 10610 82 9711 74 8812 70 80
0.5 0.3 0.2
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Weighted Moving Average
Forecast v. Demand with Trend
0
50
100
150
1 2 3 4 5 6 7 8 9 10 11 12
Period
Dem
and actual demand
forecast
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Weighted Moving Average
Forecast v. Demand with Trend
0
50
100
150
1 3 5 7 9 11Period
Dem
and actual demand
forecast
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New Forecast = (Actual Demand) + (1-)(Old Forecast)
Exponential Smoothing
Provides a routine method of updating item forecasts
Exponent is a weighting factor applied to the demand element
Works well for items with fairly constant demand Is satisfactory for short-range forecasts
Detects trends, but lags them
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Exponential Smoothingperiod actual demand forecast
1 242 263 22 254 25 24.45 19 24.56 31 23.47 26 24.98 18 25.19 29 23.710 24 24.811 30 24.612 23 25.7
0.2 0.8
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Exponential Smoothing
Forecast v. Demand with Trend
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12Period
Dem
and
actual demand
forecast
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Exponential Smoothing
Forecast v. Demand with Trend
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12
Period
Dem
and
actual demand
forecast
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Exponential Smoothing
Forecast v. Demand with Trend
010203040
1 2 3 4 5 6 7 8 9 10 11 12
Period
Dem
and
actual demand
forecast
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Tracking the Forecast
Forecasts are rarely 100% correct over time.
Why track the forecast? To plan around the error in the future To measure actual demand versus
forecasts To improve our forecasting methods
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Month Forecast Actual Variation
1 1,000 1,050 +50
2 1,000 940 –60
3 1,000 980 –20
4 1,000 1,040 +40
5 1,000 1,030 +30
6 1,000 960 –40
Total 6,000 6,000 0
Random variation: Sales will vary plus and minus about the average.
There is no bias, but there is random variation each month.
Forecasts Can Be Wrong in Two Ways (cont.)
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Problem 2.3 (Solution)
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Month Demand Next Month Forecast
1 102
2 91
3 95 96
4 105 97
5 94 98
6 101 100
7 108 101
8 91 100
9 101 100
10 99 97
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0.1 Low weighting -most smoothing
0.9 High weighting - close to actual
Choice of Exponential Smoothing Factors
Actual sales
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2.3 Forecast Errors
Every forecast should contain two elements…– the forecast – an estimate of its error
Remember the forecast is almost always wrong– use of buffer stock or capacity is used to
compensate for this error Calculations can be used to calculate the
error
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2.3 Mean Forecast Error (MFE)
Is a mathematical average of the forecast error over some period of time
The difference between the forecast and the actual demand is called forecast error
MFE sums all errors and divides them by the total of all forecast errors
If positive, then demand was greater If negative, then demand was lesser Is also called bias
– zero is no bias
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2.13 - Mean Forecast Error
Period Demand (A) Forecast (F) Error (A_F)1 12 14 -22 15 13 23 13 12 14 16 13 35 14 15 -16 11 14 -3
What is the MFE? Is there bias?
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2.13 - Mean Forecast Error
Period Demand (A) Forecast (F) Error (A_F)1 12 14 -22 15 13 23 13 12 14 16 13 35 14 15 -16 11 14 -3
MFE = (-2+2+1+3+(-1)+(-3) = 0/6 = 0
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2.3 Mean Absolute Deviation (MAD)
Is a mathematical average of the absolute forecast deviations
Indicates the average forecast error– is always positive
Is also called bias
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2.14 - Calculation of Absolute Errors
Period Demand (A) Forecast (F) Error (A_F)1 12 14 22 15 13 23 13 12 14 16 13 35 14 15 16 11 14 3
What is the MAD?
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2.14 - Calculation of Absolute Errors
Period Demand (A) Forecast (F) Error (A_F)1 12 14 22 15 13 23 13 12 14 16 13 35 14 15 16 11 14 3
MAD = (2+2+1+3+1+3) = 12/6 = 2
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2.3 Tracking Signal
Is similar to control limits used in SPC It helps one control the forecast by taking
actions at some established point– tracking signals
running sum of errors / MAD = tracking signal
It has no ratio or value, but is merely used as a subjective signal
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2.14 - Calculation of Tracking Error
Period Demand (A) Forecast (F) Error (A-F)1 12 14 22 15 13 23 13 12 14 16 13 35 14 15 16 11 14 3
12 MAD = (2+2+1+3+1+3) = 12/6 = 2
RSFE = 12Tracking Signal = 12 / 2 = 6
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2.4 Computer Assistance
Speed, reliability and relatively low cost allow for computerized modeling– take actuals and compare with different model
results– perform simulations– seek lowest MAD– must be cognizant of outliers
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Dealing with Outliers
X 500
0
5
10
15
20
25
J F M A M J J A S O N D J F M A M J J A S O N D
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Design Issues of the Forecast System
Determine information that needs to be forecasted Assign responsibility for the forecast Set up forecast system parameters Select forecasting models and techniques Collect data Test models Record actual demand Report accuracy Determine root cause of variance Review forecasting system for improved performance
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Pyramid Forecasting
SKU by Customer by Location
SKU by Customer
Stockkeeping Unit (SKU)
Package Size
Model/Brand
Product Subfamily
Product Family
BusinessUnit
TotalCompany
Roll upActual Demand
Force downForecasts
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Technique—Pyramid Forecasting
Total company:
Business unit:
Product family:
Subfamily:
Model/brand:
Package:
SKU:
SKU by customer:
SKU by cust.
by location:
Sales Forecast
SuperNet
Voice, data, media
Large business unit, small business unit, residential business unit
Encryption, storage, routers
Alpha, beta, gamma
Fiber, microwave
1210, 1220, 1230, 1240
1210 for customer 12456
1210 for customer 12456, location 4
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Data Preparation and Collection
Record sales data in same periods as forecast data
Daily, weekly, or monthly Track sales, not shipments Record the circumstances of
exceptional demand Record demand separately
for unique customer groupings and market sectors
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Homework
Chapter 1– Discussion questions 1,3,4,5,8– due 2/8/07
Chapter 2– Discussion question 1– Problems 1,7– due 2/8/07