bolton - forecasting 1

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Chapter 2 - Forecasting Fundamentals

2.1 Fundamental Principles of Forecasting2.2 Major Categories of Forecasts2.3 Forecast Errors2.4 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 plansThis 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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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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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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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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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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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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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

05

101520253035

1 3 5 7 9 11 13 15 17 19Quarter

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 Averagesales 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 Index1 128/100 = 1.282 102/100 = 1.023 75/100 = 0.754 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 1Year 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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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 demandforecast

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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


020406080

100120140160

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

and

actual demandforecast

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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 Averageperiod actual demand forecast

1 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


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


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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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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010203040

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

and

actual demand

forecast

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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 +502 1,000 940 –603 1,000 980 –204 1,000 1,040 +40

5 1,000 1,030 +30

6 1,000 960 –40Total 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

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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 LocationSKU 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 ForecastSuperNetVoice, data, mediaLarge business unit, small business unit, residential business unitEncryption, storage, routersAlpha, beta, gammaFiber, microwave1210, 1220, 1230, 12401210 for customer 124561210 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

bolton - forecasting 1

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