Demand Management Strategy and Development

Forecasting and Demand Management

Copyright 2000 by ECRU Technologies, Inc. 1

Forecasting

And

Demand Management

Strategy

And

Development

A concise writing on the actions needed to begin the implementation of world class forecasting and demand

management program.



Forecasting

And

Demand Management

Strategy

And

Development

J. Martin R. Roth ECRU Technologies Publisher

A portion of the KNOWLEDGE TRANSFER SERIES

from the WORLD OF LEARNING.



Copyright 2000 by ECRU Technologies, Inc. All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, write to the publisher, addressed Attention: Permissions Coordinator, at the address below. ECRU Technologies, Inc. 1475 Terminal Way, Suite E Reno, NV 89502-3225 Ordering Information Orders by individuals and organizations. ECRU Technologies, Inc. publications are available through bookstores or can be ordered direct from the publisher at the ECRU Technologies, Inc. address above or by email to [email protected]. Library of Congress Cataloging-in-Publication Data Martin, J./Roth, R. Forecasting and Demand Management Strategy and Development: A concise writing on the actions needed to begin the implementation of world class forecasting and demand management program / J. Martin / R. Roth. 1st ed. ISBN 1-931186-02-2 (papercover) 1. Science 2. Organization 3. Theory 4. Systems 5. Behavior I. Title First Edition First Printing September 2000 The information included in this book is further amplified when used in conjunction with other books from ECRU Technologies, Inc. Other titles are: Supply Chain Management Direction Strategy and Supply Chain Management Development Strategy.



Contents

1 INTRODUCTION........................................................................................................... 9

2 WHAT DO WE TRY TO ACCOMPLISH?............................................................... 11

2.1 DETERMINE FORECAST-ABILITY OF END PRODUCTS................................................ 12 2.2 INSTALL A FORECASTING TOOL AND PROVIDE TRAINING IN ITS USE........................ 13 2.3 DEVELOP DEMAND MANAGEMENT PROCESS OUTLINE............................................ 13 2.4 IDENTIFY NECESSARY SUPPORT REQUIREMENTS ..................................................... 13 2.5 DETERMINE KEY METRICS ...................................................................................... 14 2.6 DETERMINE TIME / RESOURCES TO IMPLEMENT THE PROCESS................................. 16 2.7 DOCUMENT DETAILS IN PROJECT DOCUMENT ...................................................... 16

3 PROCESS DEFINITION:............................................................................................ 18

THE ORGANIZATION VIEW: ................................................................................................. 18 THE FORECASTING SCOPE.................................................................................................... 20

4 FORECASTING SUPPORT REQUIREMENTS ...................................................... 21

4.1 IT PLATFORM ASSESSMENT..................................................................................... 21 4.2 INFORMATION SYSTEMS AND SERVICES INTEGRATION ............................................ 22 4.3 BUSINESS LOGISTICS AND PROCESSES ..................................................................... 23

5 PROCESS OUTLINE................................................................................................... 25

5.1 UNDERSTANDING THE FORECASTING PROCESS:....................................................... 25 5.2 BACKGROUND: WHAT FORECASTING IS .............................................................. 25

5.2.1 Statistical Forecast.......................................................................................... 26 5.2.2 Demand Management of the Statistical Forecast ....................................... 26 5.2.3 The Forecasting Process................................................................................. 27

5.3 DEFINING WHAT TO FORECAST THE SKU....................................................... 28 5.4 DISCIPLINE AND RESPONSIBILITY ............................................................................ 30 5.5 METRICS MEASURES OF PERFORMANCE ............................................................... 31

5.5.1 Forecast Metrics ............................................................................................. 31 5.5.2 Performance Metrics....................................................................................... 32

5.6 OTHER IMPLICATIONS.............................................................................................. 32

6 FORECASTINGS ROLE IN CORPORATE FUNCTIONS ................................... 33

6.1 MARKET & PRODUCT VIEW..................................................................................... 33 6.2 STRATEGIC / CORPORATE VIEW............................................................................... 35 6.3 TIME / SEQUENCE VIEW........................................................................................... 36 6.4 TACTICAL / BUSINESS VIEW .................................................................................... 38 6.5 OPERATIONAL / PROCESS VIEW ............................................................................... 40 6.6 FORECASTING PROCEDURES VIEW........................................................................... 40 6.7 ROLES, RESPONSIBILITIES, AND METRICS VIEW...................................................... 42 6.8 SUMMARY................................................................................................................ 43

7 ROLES & RESPONSIBILITIES DEVELOPMENT ................................................ 44

8 METRICS DEVELOPMENT PROCESS................................................................... 45

9 DEMAND MANAGEMENT DRIVERS..................................................................... 47

9.1 TERMINOLOGY: DRIVERS, ASSUMPTIONS, DEMAND MANAGEMENT...................... 47 9.2 TYPES OF DRIVERS................................................................................................... 49



9.2.1 External Drivers.............................................................................................. 49 9.2.2 Internal Drivers............................................................................................... 50 9.2.3 Sector Drivers ................................................................................................. 53

9.3 DRIVER IDENTIFICATION.......................................................................................... 55 9.4 DRIVER MANAGEMENT............................................................................................ 57 9.5 APPLYING DEMAND MANAGEMENT RESULTS ......................................................... 62

10 THE FORECASTING & DEMAND MANAGEMENT CYCLE ......................... 64

10.1 DOWNLOAD AND CYCLE START............................................................................... 65 10.2 FORECAST REVIEW & DEMAND MANAGEMENT ANALYSIS ........................................ 66 10.3 CHANGE CONSOLIDATION, FORECAST ADOPTION, UPLOAD....................................... 67



The Authors

J. Martin is an international consultant providing services in supply chain management, e-commerce, organization change management, and ERP system project management. He has worked with a variety of industries ranging from heavy equipment manufacture, transportation, paper, automobile, healthcare, and electronic components. He has spoken before many industry groups which include AT&T and IBM His educational background includes a B. S. in operations management, M. S. in computer science and a Ph.D. in psychology. He can be reached via email at [email protected]. R. Roth is founder and president of Systems Services International Ltd., providing business management and technology integration services since 1975. Clients are in many industries construction; automotive; communications; space and defense; manufacturing; governments; medical devices. R. Roth and his team have proven their multi-cultural experience and sensitivity in Europe, the Middle East, South East Asia, North East Asia, and North America. R. Roth is a seasoned economist with extensive experience in information technology implementation. He can be reached via email at [email protected]



1 Introduction

Accurate forecasting can play a decisive role in an organizations

planning, budgeting and performance monitoring process. The

meanings of forecast" are:

(1) the statistical forecast derived from historic data,

(2) the demand-managed forecast incorporating specific

assumptions into the statistical forecast, and

(3) the process used to develop and control the two former.

Statistical formulae are used to derive the forecast model, baseline,

trend and seasonality of the units as well as predict the likely future

quantities of the units. Historic data that has been accumulated

under past conditions of influence factors (Drivers) such as market

development, promotions, or other company actions are used. If

the past conditions continue to prevail, and if no changes are made

to influence the future development, then the forecasted quantities

are likely to occur. If the actions described are being taken in the

described quantity/value and time and if no other influences will

take effect, then the statistical forecast plus the changes resulting

from the described assumptions [heuristics] will likely occur.



In order to determine whether or not the assumed effect actually

takes place we must:

(1) measure the actual quantities/values observed (i.e. the

new historic data), (

(2) compare the actual data to the demand-managed forecast

data to determine the difference if any,

(3) analyze, in the event of a significant difference between

actual and forecast, the underlying assumptions to

determine whether they were correct.

At completion of the analysis, the resulting new assumptions (more

optimistic/pessimistic, different actions, etc.) must be applied to

the then current statistical forecast.

This process should be performed at least monthly, to achieve a

reasonable measure of actual performance early enough to

implement any corrective action. Companies may have thousands

of SKUs (Stock Keeping Units) subject to independent demand.

Clearly, forecasting all of them would require a tremendous effort.

Consequently, a selection process must be put in place to forecast

only meaningful data. Criteria for the selection could be any SKU

defined for forecasting purposes as any measurable unit that can be

extracted and/or composed from historic data.



The forecast process will result in improved business forecasts

accuracy if, and only if, the organization implements the process in

a disciplined fashion. Assumptions must be noted in detail; their

correctness must be analyzed; corresponding changes must be

defined.

This necessitates two implementation tools:

(1) a description of the forecast process (from timing through

data extraction, analysis, note taking, to applying changes),

and

(2) assignment of responsibility for each step in the process.

Typically, companies develop roles and responsibilities for the

process, and incorporate them into the position descriptions of the

responsible functions.

2 What do we try to accomplish?

This book outlines, at a high level, the forecast and demand

management process. It is intended as a comprehensive

summary, and as a guideline for developing your own work-in-

progress to be modified and amended as you progress with the

implementation.



Note that the forms referenced in this book, and shown as

examples, easily can be created in electronic format. This will

enable company-specific modifications to be made, and the forms

can be used as a blueprint for the development of automated

recording and tracking facilities.

This book is not a primer on statistical forecasting, forecasting

methodologies, or related theories. Numerous publications are

available that cover these subjects.

2.1 Determine Forecast-ability of End Products

As a first cut it is necessary to determine which independent

demand (the end products as sold to customers) are in effect

forecast-able with applicable forecast model, trend, seasonality

etc., and determination of A, B, etc. classification. This provides

the base for focus on the most important products. Spending 80%

of your effort on the top 20% of your products produces

measurable results, quickly!



2.2 Install a Forecasting Tool and provide Training in its use

Forecasting is best performed using automated tools. Ideally,

several forecasting applications available in the market, should be

tested and evaluated. After installation, the system can be used for

forecast processing (both initial statistical forecasting and

subsequent demand management), and as a training tool for

personnel.

2.3 Develop Demand Management Process Outline

The forecasting and demand management process starts with

processing the historic data, and ends with the preparation of

forecast data (including demand management input) for the next

period.

This book comprises the process outline. It can be used to develop

your own action plan.

2.4 Identify necessary Support Requirements

Requirements covering organization functions directly involved in

the forecast and demand management process (i.e. departments,

managers, etc.), as well as such requirements that need to be

addressed but are outside the scope of the topic, are identified.



Directly involved functions and their roles are described in this

document. Others need to be identified and documented as part of

the development process. Note that the organization should

address these requirements reasonably urgently, as the quality and

timing of many forecasting and demand management activities are

directly influenced by them.

2.5 Determine Key Metrics

Metrics measure the difference between established targets

and actual performance against these targets. This necessitates that

both target and performance are quantifiable in the same unit of

measure. Example:

a) TARGET: Improve Sales, month-over-month, by

10%

b) PERFORMANCE: Improved sales by $ 100, $110, $ 121,

etc.

Does this imply the target has been met? No since the

performance unit of measure (dollars) is different from the target

unit of measure (percent). Under these conditions, seemingly

conclusive data is meaningless or worse, misleading. The fault in

the example could be corrected in either of two ways:



a1) TARGET: Improve Sales, month-over-month, by

10% BASED ON $ 1000 start

b1) PERFORMANCE: Improved sales by $ 100, $110, $ 121,

etc.

OR

a2) TARGET: Improve Sales, month-over-month, by

10%

b2) PERFORMANCE: calculate based on following actual data:

Sales in Month1 Month2 Month3 etc.

Sales$ 1000 1100 1210 etc.

Sales% 100% +10% +10% etc.

Consequently, a system of metrics must be developed if the

company is to have accurate and meaningful performance

measurement capability.

Ironically, forecasting is the business process that least lends itself

to performance measurements. The reason: forecasting is a

scientifically calculated prediction of future sales UNDER

CONDITION THAT the assumptions and resulting actions that

were effective in the past and are reflected in the data history, will

REMAIN UNCHANGED. Yet it is the purpose of the demand

management process, to lead to assumption corrections and

different action conclusions. Paradoxically, the forecasting and

demand management process is most successful if actual

performance continually exceeds forecast, either with positive

(such as for sales) or negative (such as for costs) trends.



Initially, the success of the forecasting process, itself, usually is

measured by determining whether actual sales for the forecast

period were equal to the forecasted sales for that period (or within

a pre-determined percentage-band around the forecast, e.g. +/-

5%). In the long term, this inadequate measure should be replaced

by metrics aimed at business functions leading to, and resulting

from, the forecasting/demand management process.

2.6 Determine Time / Resources to implement the Process

Once the Action Plan is drafted, for planning, training and

implementation actions resulting or identified in this project phase,

a high-level estimate of required efforts and likely time frames can

be developed.

2.7 Document details in Project Document

Throughout your project, issues will be identified that are out of

scope of the topic but will require addressing in the short term.

SKU (Stock Keeping Unit) planning and related Information

Services requirements are a good example. All project-related

information should be documented in clearly identifiable separate

binders, such as:



a) The Forecasting and Demand Management Process

Implementation Manual

b) The Forecasting System Implementation Manual

c) The Forecasting System User Manual

d) The Training Plan (which might be combined with process

scripts)

e) The Action Plan



3 Process Definition:

Forecasting and corresponding performance monitoring are an

integral part of most organizations management functions. A

high-level indication of such functions can be represented as

follows:

3.1 The Organization View:

Note that forecasting and related functions (such as order

fulfillment, customer satisfaction, etc.) ultimately relate to virtually

ConcurrentDesign

Cash Management Credit Management

Dat

a Pr

ovis

ion

& A

cqui

sitio

n

Network /Security

Management

Data BaseAdminist-

ration

ApplicationsManagement

EDI & e-commerce

Hardware Utilities/MonitorsSystem Software

Demand ManagementChannels, Relationships,

Promotions, Events Forecasting / ModelingProduct X, Quantity Y,

Delivery Z

Product PlanningGroups & Families, Form,

Fit, Function, Cost

EDM - Engineering DataManagement

Design, Specifications,Revisions

Items / Kits / Assemblies

Bills of Materials

Routings/Ass'y Instruct./ Process Formul.

Joint Specifications &Development

Laboratory / Prototyping

Inventory / ServicePolicy

Product Structure,Configuratiog, Pricingn,Variants, Std. Costing

Suppliers / Vendors(& their Suppliers)

Suppliers / Vendors(& their Suppliers)

ConcurrentDesign

Constraint Planning

MPS - MasterProductionScheduling

Purchase Orders /Contracts

RCCP - Rough CutCapacity Planning

CRP - CapacityRequirements

Planning

MRP - MaterialsRequirements

Planning

FCP - Finite Capacity /FFC Finite Forward

Scheduling

Process &Production Management

& WIP Control

SFC - Shop Floor ControlLine, Cell, Job, Repetitive,

Process

Maintenance - preventiveand remedial

Production Orders

Sales Force / Automation

Sales Orders / Entry &Billing

DRP - DistributionRequirements

Planning

PRP - ProjectRequirements

Planning

Bid / Contract -Contracts Management

Parts / SubassembliesManagement

Inbound Logistics

Product InventoryManagement

Distribution / OutboundLogistics

(VMI?)

Replenishment /Distribution Centers,

Vendor ManagedInventory

Order FulfillmentOrder Fulfillment

ConcurrentDesign

Documentation

Materials Issue QA & StagingLabeling, Packaging

Actuals / CostAccounting

Standards &Metrics

Budget - P/L &Performane

Targets

Payroll / Human Resources Asset Management

Finance - GL, AP, AR

Hours Accounting BackflushingCompletion Reporting

LegacyManagement

Package Evaluation &Blueprint for Change

Project Implementation

Budget, Planning,Costing

Metrics &Measurements

Training & HelpDesk

Rollout Mgmt

Y2K Review

PackageImplementation

Related Topics

Performance MetricsPerformance Metrics

Continuous UpdatesALL Areas

Customers (& their Customers)

Customers (& their Customers)

Supply Chain Mgmt

Damaged and ReturnedProducts

Damaged and ReturnedProducts

Customer Service,Technical Services,

Maintenance

Customer Service,Technical Services,

MaintenanceClaims and RefundsClaims and Refunds

External Events / Trends

Transition Management

Distributors / VMI



all functions in the company. Accurate forecast data can drive

production and financial planning, logistics and distribution

management, service and parts management.

For the topic, the scope is limited to forecasting proper. This

includes the forecasting process, and the application of demand

drivers. It excludes any of the uses of forecast data, such as for

MRP (Materials Requirements Planning), production and

purchasing planning, etc. Forecasting can be considered a stand-

alone function concerning methodology, training, and process

execution. However, without translating the forecast results into

usable manufacturing planning data, and without monitoring the

accuracy of periodic forecasts and making corresponding policy

adjustments, the efforts expended on forecasting would be wasted.

Forecasting proper includes the activities in the periodic cycle

from accepting historic demand data through statistical

forecasting and demand management to providing forecast and

demand-managed data to other systems and functions. This

process is graphically represented as the High-Level Forecast

Process Flow: Note that the following explanations and

descriptions are based on this representation of the forecast and

demand management cycle.



3.2 The Forecasting Scope

Keep Record ofAssumptions and

Bases

Applic. 1X

Applic. 2Y

Applic. nZ

Systems and Applicationsthat store / provide data

Historic Data,Definitions,Calendars

extracted fromApplications Files

FlatFile X1 of n

FlatFile Y2 of n

FlatFile Z3 of n

Common Format suitable forForecast Processing

History File

ForecastingSYSTEM (1)

StatisticalForecast

(Snapshot - IFnothing is done

then...)

Apply "DriverUpdates" to theforecast data =

DemandManagement

Driver 1,Location 1

Driver X,Location 2

Driver Z,Location n

Keep Record ofAssumptions andBases - update as

needed

ForecastingSYSTEM (2)

ManagedForecast

(Snapshot -IFDemand Mgmtapplied, then ..)

Generate "FlatFiles" with Forecast

Data for sourcesystems andapplications

FlatFile X1 of n

FlatFile Y2 of n

FlatFile Z3 of n

MONITOR forDelta (FC/Actual)

Cycle as needed

High-Level Forecast Process Flow - HL-FC-Flow.vsd - 03/06Jan00 - RR

Periodic: ActualData Reports

StatisticalForecast

(Snapshot - IFnothing is done

then...)

ManagedForecast

(Snapshot -IFDemand Mgmtapplied, then ..)

"Driver" data usedto manage the

forecast process

His

toric

Dat

aEx

tract

ions

(mon

thly

dat

a)

Use

of f

orec

aste

d da

ta in

oth

er s

yste

ms

and

appl

icat

ions

"History" may be a single file, ormenu selection of multiple files

Forecast Scope

MultipleRegression

Analysis(occasional)

Create Operating Environment for ForecastingTools, on the Target Platform

Perform initial Data Setup and ForecastCalculation

Start-Up Data

Set-up and InitializationProcess (one-time)

Cyclical ForecastingProcess (monthly)

ForecastOutput Format

for MRP,Reporting etc.

Sales Input

OperationsInput



4 Forecasting Support Requirements

Forecasting and Demand Management are business functions

which require extensive cooperation between functions and tasks.

The forecast frequency itself (which depends, in turn, on the nature

of the business) usually is monthly. However, all related activities

must be performed and completed within a very short time frame.

If the forecast is not completed within at most two or three days

after month-end closing, the forecast intelligence could become

useless as not enough time would remain in the period for sales

and operations to take forecast-inspired actions.

To operate in this compressed time fence, three specific business

areas must be addressed.

(1) IT Platform Assessment

(2) Information Systems and Services Integration

(3) Business Logistics and Processes

4.1 IT Platform Assessment

Most of the forecast-related data can be collected from existing

systems, supplied from front-end input prepared by sales persons

and other functional heads, and distributed via existing information

systems. Therefore, the information technology platform should

be analyzed to determine its suitability as a support platform.



Specifically, this involves:

Inventory of and Access Mechanisms to Hosts, Servers,

Networks, PCs

Capabilities and Use of Extranet, Intranet, Internet, Virtual

Private Networks, etc.

Host and Server Interfaces capabilities and functions, for

data feeds and use

Interim Data Structures (data warehouses, data marts, etc.)

to facilitate focused data management

Development of Platform Architecture document with

Training and Development Plan

4.2 Information Systems and Services Integration

Forecasting is a tool consisting of limited and specific-purpose

software applications that perform only one set of functions:

assessing historic information, classifying items, determining their

forecast-ability, and forecasting anticipated future quantities (of

units, dollars, etc.) based on supplied history.

Specifically, the forecasting tool set does not address issues such

as how the SKU (the item or part) is defined, how the data is

massaged during extraction from the host data bases, how it is

summarized, and how transitions from old to new SKUs will

be accomplished.



For all these capabilities, the organization relies on its Information

Services department or provider. Specifically, this involves:

Design and implementation of pre-processing capabilities

Design and implementation of post-processing capabilities

Split-and-Bypass-and-Merge (Windowing) requirements

design and implementation

Cutoff, Transfer and Balance Management between host

applications and forecasting

Generational File Maintenance for Simulation, Restart,

Backup and Restore Purposes

Support and Analytical Software such as Multiple

Regression Analysis, automated Data Feeds (Duns, Dodge,

etc.)

On-line Documentation Support Capabilities for instant

access to assumptions, action decisions, etc.

Development of IS Support Structure Architecture document

with Training and Development Plan

4.3 Business Logistics and Processes

Forecasting has a very intimate relationship with other business

functions. For example, the independent demand quantity forecast

for any one product and period, will depend not only on historic

data but also on the companys decisions such as customer service



levels, safety stock requirements, distribution points and supply

logistics, etc.

To link these functions to the forecasting and demand management

process, it is necessary to define their contributions to the process,

through business logistics mapping. Typically, this involves:

Development of Business Functions Matrix with focus on

FC/DM

Organization Skills Assessment (Forecasting, Statistics,

Data Analysis etc.) and Training Development

Roles and Responsibilities Development and Position

Description Updates to reflect contributions

Updating of Compensation / Incentives programs to reflect

weight of FC/DM and related functions

External Interfaces Considerations such as reporting (SEC),

compliance (documentation), etc.

Automating Integration of FC/DM into the Business Flow

Development of to-be business process flows with

Training and Development Plan

Without these support processes firmly developed and in place, it

will be difficult, though not impossible, to maintain the discipline

of preparing monthly forecasts, reliably, with reproducible analysis

and assumption options.



These support functions are excluded from scope of this book -

they should be included, at a high level, in the Action Plan.

5 Process Outline

5.1 Understanding the Forecasting Process:

The term forecasting often is mis-used as a set of statistical

formulae, as a sales predictions process, or as an educated guess

as to what might happen. Properly used, forecasting can play a

decisive role in the organizations planning, budgeting and

performance monitoring process. Therefore, an outline of what

forecasting really is, is required.

5.2 Background: What forecasting is

For the purpose of this book, we distinguish three specific

meanings of forecast. They are:

(1) the statistical forecast derived from historic data,

(2) the demand-managed forecast incorporating specific

assumptions into the statistical forecast, and

(3) the process used to develop and control the two former.



5.2.1 Statistical Forecast

Statistical formulae are used to derive, from quantitative history of

independent demand for units, the forecast model, baseline,

trend and seasonality of the units, as well as a prediction of the

likely future quantities of the units. Historic data has accumulated

under past conditions of influence factors (or Drivers) such as

market development, promotions, or other company actions.

Consequently, the statistical forecast derived from this data, has

a narrow meaning:

IF the past conditions continue to prevail, AND IF no changes

are made to influence the future development, THEN the forecast

quantities are likely to occur.

5.2.2 Demand Management of the Statistical Forecast

To make the forecast more meaningful as a predictor of the

future, decisions must be made about the factors that influence

demand, the strength of these factors, and their likely impact on

future demand. For example: a targeted promotion estimated to

cost $ X for a specific market, will result in a Y% one-time sales

increase in that market, for a period of four months, commencing

one month after the start of the promotion. In practical terms: the

forecast quantity/value for the future periods 2, 3 and 4 would have



to be increased by Y%. The resulting demand-managed forecast

has the following meaning:

IF the actions described are being taken, in the described

quantity/value and time, AND IF no other influences will take

effect, THEN the statistical forecast plus the changes resulting

from the described assumptions, likely will occur.

5.2.3 The Forecasting Process

In order to determine whether or not the assumed effect actually

takes place, the following steps are required, at least at the end of

period 4 (in the above example):

a) Measuring the actual quantities/values observed (i.e. the new

historic data)

b) Comparing the actual data to the demand-managed forecast data

to determine the difference if any

c) In the event of a significant difference between actual and

forecast, analyzing the underlying assumptions to determine

whether they were correct. (Note that several assessments

must be made to determine, inter alia, whether there may have

been a systemic error in the assumption, whether the assumed



effects where weaker, stronger or in a different time frame than

expected, etc.)

d) At completion of the analysis, the conclusions drawn must be

noted (with the original assumptions), changes about the

assumptions must be noted, and the resulting new assumptions

(more optimistic/pessimistic, different actions, etc.) must be

applied to the then current statistical forecast.

This process should be performed at least monthly, to achieve a

reasonable measure of actual performance early enough to

implement corrective action if necessary.

Note that the repetitive period refinement of assumptions forces

the organization to continually improve its analysis and

conclusions process, thereby increasing its knowledge of

customers, markets, the competition and other influence factors

(drivers).

5.3 Defining what to forecast the SKU

In many instances, companies have thousands of SKUs (Stock

Keeping Units) subject to independent demand (by customers in

the market). Clearly, forecasting all of them would require a

tremendous effort. In most cases, the result would not be



significantly different than when only 25% (or some other

measure) of the SKUs would have been forecast. Consequently, a

selection process must be put in place to forecast only

meaningful data. Criteria for the selection could be:

a) SKUs that have the greatest effect on results. Typically,

about 10% of all SKUs account for more than 35% of sales,

with another 15% accounting for an additional 25% of sales.

Forecasting these 25% of all SKUs (often described as A and B

items) will result in a meaningful forecast of 60% of sales.

b) SKUs that allow the best possible assessment of influence

factors. In many instances, relatively few large customers

account for a very large proportion of all sales. Therefore,

seeking to influence demand by these few customers will have a

large effect on anticipated sales. Similarly, a few product

models out of thousands of end items, components and parts,

are much easier to control and forecast, than the total

population of thousands of SKUs.

Note that the definition of SKU changes with the desired degree

of control. Forecasting software will allow determination of A, B,

etc. status, forecast-ability, forecast model, for any measurable

unit. Therefore, the desired SKUs must be carefully designed and

corresponding data must be extracted from the historic data files.

Consequently, a stock keeping unit for forecast purposes is vastly

different than an SKU for production and inventory purposes:



An SKU defined for forecasting purposes, can be any measurable

unit that can be extracted and/or composed from historic data.

Note that forecasts for SKUs that are too narrowly defined, can

become meaningless because the number of items becomes to

small, for meaningful statistical analysis.

To enable tracking of actual performance against forecast for these

constructed SKUs, the same cycle of assumptions,

measurements, corrections (the demand management) must be

applied as for real SKUs.

5.4 Discipline and Responsibility

The forecast process as described, will result in improved business

forecasts, IF and only if the organization implements the process in

a disciplined fashion. Assumptions must be noted in detail; their

correctness must be analyzed; corresponding changes must be

defined.

This necessitates two implementation tools: (1) a description of

the forecast process (from timing through data extraction, analysis,

note taking, to applying changes), and (2) assignment of

responsibility for each step in the process.



Typically, companies develop roles and responsibilities for the

process, and incorporate them into the position descriptions of the

responsible functions.

5.5 Metrics Measures of Performance

Determining responsibility is not, by itself, sufficient to ensure the

discipline of the process. To do so, a set of performance

measurements are required. They fall into several basic categories:

5.5.1 Forecast Metrics

The quality of the forecast process itself can be measured by

constantly monitoring how close the observed actual data track

to the forecast data. The better the process, the smaller the

difference between them. Note that the forecast merely reflects the

organizations past history and its best judgment on how to

influence future demand. In and by itself, the forecast cannot be

right or wrong (unless underlying statistical formulae are

incorrectly applied).

If external influence factors are used to modify the statistical

forecast, additional statistical evaluations will be required to ensure

that these factors are correctly applied. For example, to determine

the effect of an interest rate rise on an interest-sensitive



organization, a multiple regression analysis might be necessary to

determine the degree and the time of the influence.

5.5.2 Performance Metrics

In many instances, forecasting can be accurate as measured by

forecast metrics, yet not reflect severe problems. For example,

actual total shipment volume can be exactly as forecast, while

many of the shipments could have been very late. In this instance,

customer dissatisfaction eventually would result in sales reductions

event though other sales and marketing assumptions might be

adequate.

Consequently, regular business metrics must be employed as part

of the forecasting process. Such metrics can be operationally

relevant (late shipments, partial shipments, excess damage, out-of-

stock situations etc.), reflective of market changes (new

competition, un-anticipated seasonal influences, etc.)

5.6 Other Implications

It is both probably and likely, that the forecast process as a

methodology, a discipline, and a set of usable results might

influence corporate decision-making more than any other

application of business knowledge. Customer Satisfaction the

ultimate measurement of success will be as good as the



organizations capability to anticipate the customers requirements

and expectations.

Forecasting is the process used to define and quantify these

expectations.

6 Forecastings Role in Corporate Functions

A top-down view of the corporation reveals a progressively

more detailed focus from the perception of the market, to the

metrics to be tracked for measuring performance in that market.

These views can be represented as follows:

6.1 Market & Product View



The first view indicates how the companys products/services must

compete in the market. Typically, production and distribution

characteristics as well as forecast time fences are a direct result

of the market environment.

Note that the forecast horizon the time available to forecast

likely demand, and to supply that demand, becomes ever more

Cus

tom

ers

Make to Stock

Make-to-StockAssemble-to-Order

EtO

FCH

Forecast Horizon

Production and Distribution Characteristics

Long Lead TimePoor Process CapabilityHigh Set-up / Tooling CostsIrregular Demand"Forever" UseCustomer-specificOne Customer - One Contractor

Short Delivery TimeHigh Reliability

Immediate ConsumptionHigh Throughput

1000's Suppliers, Millions CustomersLow Inventory Levels

"State" Risk Reduction

Market DriversDesign Drivers

Forecast Horizon

Supp

liers

Industry DriversManufacturing & Materials Lead Time

Reliable Fabrication / Assembly ProcessesContinuous Asset / Tools Utilization

Regular Demand"Replacement Lifetime " Use

Market-specificThousands Customers - Dozens Suppliers

continuousreplenishment

one-of-a-kind

OrderDelivery &Replace't

Forecast & Demand Management- Market & Product View

Models(2).vsd - 18Feb98 - RR

Life Cycle Product "Make" Product "Value"

Hours / Days MtS / POS Repl. cents / dollars

Days/Months MtS / AtO '00s / '000s

9 - 60+ months EtO / DBO '00,000s,millions/billions

Markets Competition RegulationCompliance



short as the corporation moves from an engineered-to-order

environment, to a make-to-stock operation.

6.2 Strategic / Corporate View

At the next level, the corporation identifies the business planning,

and sales and operations planning required to successfully operate

in the market.

The Tactical / Business View then defines how customers and

business partners become the focus of tactical planning, while the

Operational / Task View defines the production and operational

requirements.



Forecasting and demand management apply the time fences

around these views.

6.3 Time / Sequence View

ERP

Operational / Task View

Tactical / Business View

Strategic / Corporate View

Business Planning

Sales and Operations Planning (S&OP)

Customer Relationship Management (CRM)

Supply Chain Management (SCM)

Master Scheduling

Materials Requirements Planning (MRP / II)

Manufacturing Execution (MES)

Logistics and Service

FC &

DM

Forecast & Demand Management- Strategic / Corporate View




The forecast horizon is long-to-medium-term attempting to

predict the likely independent demand for the companys products

(services) based on business, and on sales and operations planning

(i.e. based on the Corporate View!). Note that this horizon shrinks

the closer the company comes to make-to-stock operations.

The accuracy of the forecast especially in a short-term delivery

environment cannot be proved using actual orders. Ideally,

ERP

Operational / Task View

Tactical / Business View

Strategic / Corporate View

Business Planning

Sales and Operations Planning (S&OP)

Customer Relationship Management (CRM)

Supply Chain Management (SCM)

Master Scheduling

Materials Requirements Planning (MRP / II)

Manufacturing Execution (MES)

Logistics and Service

FC &

DM

long term

near term

detailed

toda

y

Fore

cast

"H

oriz

on"

Proj

ectio

n

Ord

ers

Ship

ped

WIP

tom

orro

w

hist

ory

TIM

E H

OR

IZO

N

Forecasting & Demand Management- Time/Sequence View




orders booked should be equal to the independent demand that

has been forecast.

In reality, the periodic forecast is a snapshot that predicts what

will happen, based on past history, IF the company does not

change its underlying policies and procedures which are reflected

in that history.

Using the forecast result as an indicator, the company needs to

determine which factors or drivers it needs to change, in order to

improve its likely result, compared to the initial forecast.

These influence factors are identified and applied, as part of the

tactical / business operations.

6.4 Tactical / Business View

The forecasting process together with customer relationship

management commences the periodic (usually monthly) tactical

review process. It aims at quantifying the desired results in

dollars and high-level product units, and determines which

contributions are necessary to achieve these results.



This view makes it obvious that forecasting alone cannot deliver

results. Forecasting is customer and market oriented. Production,

back-office and logistics are parts oriented.

The gap between the two spheres must be bridged by a functioning

engineering and data management system. It allows rapid

sForecasting Process

Oriented towards Improving the Bottom Linei.e. Product Dollars and Quantities

Customers

Products

Markets

Competitors

Governments

Seasons

ProductionManagement

Marketing &Sales

Senior /Finance Mgmt

Planning / Scheduling ProcessOriented towards Operational Efficiencies

i.e. Production Costs and Units

Capacity

Equipment

Labor

Raw Materials

Governments

Seasons

Overlapping Drivers possible and likely

ManufacturingManagement

Purchasing /Logistics

Maintenance& Service

Bills ofMaterials

RoutingsContracts

MRP II /RCCP etc.

MIS / NetsDBMS etc.

Overlapping Interests / Functions possible and likely

Sales Force Commitment &Participation

CRM and Forecasting Systems

Engineering and Drawing Systems - Production Planning

Production and "Back-Office", Logistics Systems

MIS - Platform and Applications Support

DPAS

Forecasting & Demand Management- Tactical / Business View


Distribution /Logistics



translation of high-level unit forecast, into detailed parts

requirements.

6.5 Operational / Process View

The periodic nature of forecasting and the short time available to

satisfy the predicted independent demand necessitate that the

forecasting and demand management process can be performed

speedily, with as little effort as possible. The process needed to

meet these requirements is represented below.

Note that the process is bound between extraction and provision

of historic data (last periods), and the uploading of forecast data

for MRP and reporting purposes.

6.6 Forecasting Procedures View

At the most detailed level, the procedures view (relative to

forecasting and demand management) identifies the steps required

to implement the process, and the tasks that must be performed

regularly and repetitively.



The start of the forecasting and demand management cycle

produced the statistical forecast the calculated amount of

independent demand that would result from historic sales data

provided the company would not change underlying policies and

procedures.

Various business functions must assess these quantities and their

timing, and determine which factors likely would change the

quantities. These factors and their specific values are called

drivers. They exist in business and finance management

Initial Definitions & Periodic Maintenance

Platform MIS BusinessLogistics Startup

Forecast & Demand Management - Cycle Start

Business &Finance

Marketing&

Economics

Sales &CustomerRelations

Operations& Logistics

Consolidation - Assumptions, Contributions

Demand Management & Forecast Evaluation

Forecast Acceptance & Targets Update

Forecasting & Demand Management: Cycle End

Forecasting & Demand Management- Task / Procedures View




(interest rate sensitivity), marketing and economics (housing starts,

restrictive regulations, whether), sales and customer relations

(promotions, sales events, floor space support), operations and

logistics (distribution locations and distances, expediting costs,

materials lead times), and in any other corporate function.

Successful and reliable forecasting can be achieved IF these

drivers are vigorously tracked, analyzed, and used to shape

corporate policy and practice (service levels, inventory, etc.).

The ability to measure success, depends on two corporate

objectives: (1) setting targets for each function that identify and

quantify what is required of the function in order to meet the

companys targets, and (2) establishing data collection, calculation

and reporting capabilities that allow evaluation of the functions

actual performance against their respective targets. Usually, the

two objectives are addressed through the development of roles

and responsibilities, and of metrics. This is the most detailed

and common view of corporate operations:

6.7 Roles, Responsibilities, and Metrics View



6.8 Summary

Forecasting is a tool. It calculates likely outcomes based on

performance history. Business functions must determine how to

sustain or change the predicted outcomes, through active

management of the drivers. Setting specific performance targets,

and measuring each functions actual performance against target,

enables success control and remedial action.

President Objectives CSF - CriticalSuccess factors

Suggest, Direct,Guide

NegotiateAgree

Event

Task 1.1.1

Task n.1. 2

Etc.

Customers

BusinessProcesses

Defines Derives

ObjectivesVicePresident Defines

CSF - CriticalSuccess factors



Derives

Link

ObjectivesTeamLeader Defines Defines

NegotiateAgree

Link


ObjectivesTeamMember Defines Derives


NegotiateAgree

Link

My Team

Objective 1 CSF 1.1Task 1.1.1Task 1.1.2Task 1.1.3

Objective n CSF n.1Task n.1.1Task n.1.2

Objective On CSF On.1Task On.1.1TaskOn.1.3Close

Expectations

Require

Corporate Performance

From Corporate Objectivesthrough Roles and ResponsibilitiesDevelopment, to Accountability andPerformance Measurementsin performing Business Processes

Fulfillment

Tasks

Tasks

Roles &Responsibilities

Development

PositionDescriptions and

PerformanceTargets

AccountabilityDevelopment and

PerformanceMeasurements

Consistent &Dependable

Linkage

Consistent &Dependable

Linkage

Derives


Derives


Tasks

Tasks

Derives

Derives


RequireRequire

Other Teams'Objectives,

CSFs, Tasks

Other Teams'Objectives,

CSFs, Tasks



7 Roles & Responsibilities Development

Forecasting is a tool to calculate likely outcomes, based on past

performance, under the assumption that "nothing changes" that

all policies, procedures, market environments, customer relations,

etc. remain static. In reality, this is highly unlikely. Consequently,

each business function must be assigned specific targets. The

targets, when met, would enable the company to achieve its

objectives for results.

The development of roles and responsibilities is a four-step

process.

(1) For each corporate level, establish quantifiable objectives

(2) For each objective, establish its critical success factors

(CSF)

(3) The CSF of the higher level, become the objectives of the

respective lower level

(4) Operational tasks (procedures) are developed to ensure that

CSFs can be achieved.

The development process is out of scope for the purposes of this

topic.



8 Metrics Development Process

Performance Measurement is at the core of the organizations

determination whether or not its plans have met with success. The

assessment process involves four steps:

1. Setting Performance Targets for each business function

in the organization

2. Collecting data about actual performance, for all business

functions

3. Calculating actual performance based on collected data,

versus targets

4. Acknowledging success / non-success of target

achievement

The process of collection data for performance measurement

usually is referred to as metrics. Metrics measure the

difference between established targets and actual performance

against these targets. This necessitates that both target and

performance are quantifiable in the same unit of measure.

Initially, only the accuracy of the forecast is established as a

metric. It indicates that this specific metric (were actual sales

within x % of the forecast for the period?) is subject to company



action on the preceding periods forecast and, therefore, very

subjective and prone to change.

To develop meaningful metrics for the organization measures that

allow assessment of performance even in the short term requires

a development and implementation program to be put in place.

Development of the metrics and performance measurement process

is out of scope of this topic.



9 Demand Management Drivers

Statistical forecasts are a calculation of expected independent

demand, based on historic performance (sales). Policies and

procedures that were in effect in the past, are reflected in the

historic data.

9.1 Terminology: Drivers, Assumptions, Demand Management

For example, an earlier decision to heavily promote a specific

product, could be reflected in increased monthly sales quantities of

the product, since the promotion. Alternatively, the initial increase

could have been followed by a decline to traditional sales levels.

In the first instance, the promotion would have led to a permanent

gain, while in the second instance, the data would indicate the

specific promotion had a time-limited effect.

The statistical forecast would project a higher trend in the first

case, and a seasonality spike in the second. Whether or not this

projection would be accurate (better: whether actual sales would

by equal or similar to projected sales), depends entirely on the

analysis of the data, and on the conclusions the company draws

from the analysis.



The process of analysis and conclusion can be described as

demand management. Technically, the company attempts to

influence (manage) demand by creating favorable conditions.

These conditions are aimed at specific factors, the drivers.

Conclusions drawn from prior factor analysis and assessment were

assumptions at the time the conclusions were drawn. Since

then, actual sales history indicates whether or not these

assumptions were correct.

Any relevant new conclusions must be recorded, as well as specific

action decisions taken to better influence the factor(s). To achieve

measurable results, actions and expected results must be quantified

in terms of effort and time. For example: The most recent

product promotion has resulted in sustained sales increases, where

for each dollar spent, we have seen an increase of 150 dollars.

Analysis indicates that we need to increase, permanently, our

promotion budget for this product by $ x (the budget target) in

order to sustain improving sales every month by y % (the result

target), recognizing that the improvement effect will be noticeable

approximately 2 months after the initial promotion (the time

target).



The resulting actions would be reflected in these ways:

(1) the budget would show a promotion cost increase of $ x

every month,

(2) the sales forecast would be increased by y% every

month, but

(3) with a time delay of two months between the cost

increase and the sales increase

9.2 Types of Drivers

Specific business functions need to be responsible for identifying

and quantifying drivers on a regular basis. Which functions roles

and responsibilities will be so affected, depends on how the

drivers are classified, based on the companys past experience.

Some classification options are:

9.2.1 External Drivers

These are drivers that are beyond the companys control; they are

encountered in the many areas such as (with examples):

customer (vacation shutdown, short order lead time)

market (development permits, housing starts)



competition (aggressive promotions / pricing for brand

builders

government action (mortgage rate deductibility)

fiscal / monetary policy (interest rate change)

licensing requirements (gas installation inspections)

engineering standards etc. (new UL requirements)

whether (seasonal influences, snow predictable,

unpredictable)

etc.

Some of these drivers occur infrequently (such as interest rate

reductions / increases, changes in engineering/UL specifications,

etc. ), while others occur quite often (such as snow falls could

delay construction starts, our customer shuts down for vacation

could drop some demand or simply postpone it, etc.)

9.2.2 Internal Drivers

These are those that influence our demand because of actions we

take, internally. They can be found in many areas such as (with

negative examples):

Accounting (credit approvals take too long)

Distribution (too little stock in our distribution

warehouses)



Customer Service / Administration (installer calls kept on

hold, slow inquiry response)

Engineering (design not friendly for install ability,

insufficient specs, etc.)

Facilities (shipping dock too small, inadequate handling

equipment

Finance / Treasury (pre-determined price curve / price

points not flexible enough)

Forecasting (do not consistently analyze last periods

assumptions)

Human Resources (slow data entry causes backup of

orders)

Information Systems and Technology (no quick order

status inquiry)

Inventories (commodities safety stock too small for

ensured acceptable cycle time)

Logistics (competition for LTL space in regions increases

shipping costs)

Maintenance (equipment/tools breakdowns lengthen

average cycle time)

Marketing (we dont promote to brand builders for

profitability)

Materials (steel too thin excessive freight damage)

Management (too many special pricing requests real

exceptions take too long)



Manufacturing (capacity constraints, scheduling, labor

availability etc.)

Overhead (too many salaried heads, expensive but

ineffective controls on quality etc.)

Systems / Processes / Procedures (retroactive price break

application too time consuming)

Production / Shop Floor (scrap and efficiency not

controlled)

Projects (effects of special projects subdivision

development promotion? not reflected in production

scheduling / materials purchases)

Purchasing (long lead times, pricing, quality assurance on

receiving)

Quality (high warranty costs)

Sales (incentives promote volume regardless of

profitability)

Some of these drivers require quick action from period to period

(such as responding to competitive promotions), others may

necessitate internal improvements in order to maintain or expand

demand (customer service / installation support, credit policies and

approvals etc.); still others may require investments to improve

profitability (inventory levels for immediate availability allow

profitable pricing).



9.2.3 Sector Drivers

Other classifications can be made by sector or function. Some

examples follow.

9.2.3.1 Business and Finance Drivers

Existing Markets

New Markets

New Customers

New Product Potential

Interest Rates

Capacity and Inventory Financing Constraints

Production and Distribution Facilities Planning

Service Levels

Product Replacements / Phase outs

9.2.3.2 Economic and Market Drivers

Political Action

Fiscal / Monetary Policies

Economic Conditions

Competitive Environments / Imports

Distribution Structures

Market Outlooks

Industry Trends



Construction Starts

Whether

9.2.3.3 Customer Relationship and Sales Drivers

Existing Customers / Sales Profiles

Existing Sales Channels, Markets

Demand Potential Existing Products

Demand Potential New Products

Customer Relationships and Data Exchange

Promotion Campaigns

Co-packaging and co-marketing

9.2.3.4 Operations Drivers

Capacity Constraints short / long-term

Materials Acquisition Trends and Logistics

Labor Markets

Distribution Logistics Time and Cost

Manufacturing Scheduling

Assembly Scheduling

Inventory Policies



9.3 Driver Identification

The company needs to identify the business functions that are

responsible for the specific drivers by type and sector. The roles

and responsibilities for the respective positions need to be updated

to reflect driver-related objectives, and compensation and

incentives programs need to be adjusted to reward early change

detection and assumption management

Initially, the top 10 drivers in each area should be identified, as a

starting point. This allows the company to concentrate on the most

important focus areas right away. Over time, additional drivers

should be identified and included in the demand management

process.

It is particularly important that all functions cooperate in driver

identification and subsequent assumption management.

Forecasting is not a sales function, and cannot be used successfully

without input from many sources.

The forecast manager (the person or function assigned to plan

and manage the periodic/ monthly forecasting and demand

management process) must ensure that each identified function

participates in the analysis and assessment process.



The combined input from all functions will be consolidated in a

single forecast change for each affected product.



9.4 Driver Management

To facilitate identification and management of specific drivers, a

standard format should be used that would allow tracking of each

driver, by applicable product, customer, market, etc., and support

generational maintenance so that each forecasting cycle would

have access to preceding assumptions and resulting action

conclusions.

To facilitate the process, a manual work sheet is provided as an

example. It can be used for all drivers until such time as

Information Services can support on-line documentation.



Drivers and Assumption Work Sheet Ref.ID:

Subject: Briefly describe the "subject" to which this particular assumption / note applies:Internal:

ID. Identification / DescriptionCustomer:Model:Product:Part:Service:

External:ID. Identification / Description

Market:Industry:Competitor:Comp.Product:Government:

Date & Period: Identify the specific date and / or period to which this assumption / note applies:

Date: Week: Month: Year:

Period: from date: to date:

Background: Briefly describe the "environment" of the event or fact or development etc. dealt with:

Assumption / Note: Briefly describe the conclusions drawn / actions to be taken etc., as a result:

Anticipated Effect: How will the the the event or fact or development likely impact on us? (describe)

Positive:Neutral:Negative:Information Only:

Calculation Model How will "factors" be measured, and what calculations are used to arrive at :quantification"

Process: Determine Variables and their units of measureDetermine Result Values and their units of measureDetermine Calculation Formulae

using these variables as input,and producing Results as output

Identify Sources for each variable (time series, report field, responsible function, etc.)Identify Use of each Result (in forecast, other functional actions, etc.)

Quantification: Quantify the effect that we need to ancticipate, for the stated "Subject":

Month & Year In/Decrease Quantity In/Decrease Dollars other Comments:+/- units or % +/- value or %

expand as needed transfer these changes to the Change Form for the respective SKU

Follow-up: "Accuracy, Dependability, etc." of Assumption as observed from ACTUAL development:

Date:

Completed by Name: Position: Date:



The following example illustrates the possible use of such an

assumption work sheet for many different but related or

associated drivers.

For each applicable (important) driver, the company needs to

identify which events can occur, and what would be the time and

quantity and price impacts of each event on the demand. Initially,

the aim should be to concentrate on the top 10 drivers that will

have the greatest impact on performance.

The intended result is the eventual ability to answer the question:

IF event X happens, what will be the effect on my

demand (sales) ?

Which action can we take to enforce positive / mitigate

negative effects ?

How much will it cost us to take that action ?

How much more (sales volume, profitability, cost

reduction etc.) do we expect as a result?



An example of a forecast cycle involving market share would

be:

a) take expected housing starts (per state) as an objective

basis

b) develop an explosion for each type of start (i.e. for single,

multi family, commercial housing, how many units of our

models do we expect to result from each start

c) multiply the starts with the expected units of models

d) (Differentiate on the basis of quality and price based on

state, etc.)

e) this is the possible market size by month and state,

expressed in units of our models (with added average net

prices if required)

f) define the share of this market that the company wants to

attain, in the respective next forecast period (month, quarter,

etc.)

The result is a forecast market size

g) now derive a statistical forecast from historic data and

scrub it to remove known errors

h) note assumptions (drivers and their values) that would

increase, decrease the demand volume, change the

profitability in a market or for a model; estimate the effect of



these assumptions (by market, customer, brand, month etc.

as appropriate) and update the assumptions accordingly

i) apply the assumptions (with estimates as noted) to the

scrubbed statistical forecast

j) re-generate the forecast (in units, and apply average net

prices)

k) measure our forecast quantity and value against the size of

the potential market and note the resulting share (%)

The result is a forecast market share

l) For the period, track the actual sales (units with average net

prices) with required / desired degree of detail (by product

and customer per state and region etc.) and calculate actual

units and dollars

The result is an actual sales report for the respective period;

assuming that the forecast market size has not changed in or

prior to the period (refer to assumptions!), the company can

calculate its actual market share (sales versus forecast market

size)

m) measure the actual market share against the forecast market

share and note the difference if any

n) for differences, analyze assumptions and determine likeliest

cause(s) of difference(s)

o) refine assumptions to arrive at better forecast estimates.



9.5 Applying Demand Management Results

Once all inputs have been obtained, and their respective

quantitative impact on the statistical forecast has been determined,

the cumulative effect of the anticipated changes, on each affected

product, needs to be calculated.

Again, on-line facilities should be made available to track

assumption-based changes on a generational basis.

In the absence of an automated tool, the following work sheet may

be used to consolidate the various inputs for a product:

Forecasting - Demand Management Data Changes

Effective for Forecasting beginning with "Month 1" as month/year: _________________

This form is intended to capture all desired changes in the forecast of an SKU, as indicated in one or more Assumptions for the SKUThe form assumes "no integration" with front-/back-end processes, and allows you to apply changes in a controlled fashion.

1) Note that the definition of "SKU" may change depending on the forecast data you use (e.g. parts, customers, models, combinations, etc.)2) Prepare one of these forms, for each SKU for which you plan to apply demand management (e.g. make changes resulting from assumptions)3) For each assumption affecting this SKU, enter one "line of data changes", with cross-reference to the assumption form or file (for later tracking)4) The desired changes must be shown as 'modifying quantities' for the applicable month(s) - note that "month 1" is the next applicable month5) Change (or 'modifying" quantitites can be shown as absolute changes (e.g. +100 or -50), or as percent of the original forecast quantity (e.g. +/- 7%)

Note that your specific "front-end" and "back-end" processes may determine how to enter changes for subsequent automatic calculations.6) In the absence of front and back-end integration, add up all individual changes, per month (lines 1 through n) in line B, then add/subtract this change total from line A.7) Add/subtract the total changes (line B) to/from the original forecast quantity (line A) to arrive at the new, desired forecast quantity (line C)

Seq. SKU # SKU Description Assumpt. Xref. Mo.1 Mo.2 Mo.3 Mo.4 Mo.5 Mo.6 Mo.7 Mo.8 Mo.9 Mo.10 Mo.11 Mo.12

A DPAS Forecast Quantity before Changes:Changes resulting from Assumptions (1 line per A.), below

123

etc.

n

B Total Changes for this SKU (total for 1 through n):C Resulting new Forecast(s) for this SKU (C=A+B)



Line C the Resulting new Forecast(s) for this SKU will be

entered into the forecast system.

These values will replace the calculated statistical forecast

values and be used for subsequent processing.

Forecasting Strategy and Development


10 The Forecasting & Demand Management Cycle

On a periodic usually monthly basis, the forecasting and

demand management process is performed with least loss of time.

A forecast that takes too long to produce, becomes useless as a

predictive and tracking tool.

There are three phases in the periodic cycle. They should be

integrated in terms of required activities and their timing into

the companys normal month-end closing schedule (or such

other periodic calendar event as may be decided). The three

phases are:



10.1 Download and Cycle Start

These activities must be performed as soon as the financial month

is closed. All referenced information must be produced as quickly

as possibly and supplied to all parties involved in the forecasting

and demand management process.

Develop WEB ?distribution ?

Develop HRProcess

Develop Calendar

Develop Scripts

DevelopProcedures

Performance Targets - all R&R

Ensure Perform. Datacollection and Metrics

Calculations

Metrics from "FC systems"

Periodic Forecasting & DemandManagement Cycle

Forecast(s) forPeriod P-1

PARTINFO.TXT P-1

USETRAN.TXT P-1

FC/DM MANAGER:Ensure complete backup copy ofall FC/DM data as at the end-of-

the-la st-cycle da te (FC files,assumtion, change document etc.)

FC/DM MANAGER:Ensure complete backup copy ofall FC/DM data as at the end-of-

the-la st-cycle da te (FC files,assumtion, change document etc.)PARTINFO.

TXTPARTINFO.

TXTAll electronic

Files P -1

All non-electronicdocumentsfAll non-electronic

documents - P-1Generational

Maintenance ofForecast and

DemandManagement Data

Use to regenerate in theevent of system failures, and

for simulation purposesrequiring old / re-start

information

FC/DM MANAGER:receive / load P -1 data

FC/DM MANAGER:receive / load P -1 data

FROM: _______Sched. DATE/TIME _______Act. DATE/TIME: _______File Names received:1: ______________2: ______________3: ______________

etc.

FC/DM MANAGER:Prepare Cycle P Forecasting &

Demand Management Material forDistribution , in agreed format

FC/DM MANAGER:Prepare Cycle P Forecasting &

Demand Management Material forDistribution , in agreed format

PARTINFO.TXT

PARTINFO.TXT

DPAS Masterand Ref. Files

All non-electronicdocumentsfNon - / Electronic

Support Informat. Actual Sales forPeriod P-1

Mktg / SalesDrivers/Assumpt's

used in P-1 FCOper's Drivers /

Assumptions usedin P-1 FC

Forecast Reports:1: __________n: __________Sales Reports:1: __________n: __________Assumtion/Change Copies1: __________n: __________etc.

FC/DM MANAGER:From updated Organization Chart /List, prepa re Distribution List forprepa red ma teria ls, a nd distribute

in agreed format -set expectations,and return requirements dates

FC/DM MANAGER:From updated Organization Chart /List, prepa re Distribution List forprepa red ma teria ls, a nd distribute

in agreed format -set expectations,and return requirements dates

Organiz.Charts

Roles &Responsibilities

FC/DM MANAGER:produce "statistical forecast" for

periof P;produce Performa nce Metrics for

Period P-1

FC/DM MANAGER:produce "statistical forecast" for

periof P;produce Performa nce Metrics for

Period P-1

Calculated based on updatedhistory ASSUMING all priorAssumption remainunchangedPerformance Targets &Metrics - may be at differentperiods

DISTRIBUTION LISTAttachment List

Expected ActionsExpected Return Dates

Ensure Cut-offManagement

IS: CLOSE MonthExtract Reference Data Updates

Extract Shipment DataFormat as required

Transmit to Forecast Manager

Metrics from "other systems"

Perform.TargetsP-1

Perform.MetricsP-1

Stats. Forecast P

To "ResponsiblePositions" for

Analysis, Updates,Response

12Forecasting - Download & Cycle Start

(DPASFCTG.vsd (12) - 01Mar00 - RR)

Develop PMProcess

IF simulation desired:re-load backup files, adjust

using DPAS MenuSelections, re-start process



When these parties receive their documents, the second phase of

the periodic cycle takes place.

10.2 Forecast Review & Demand Management Analysis

Lastly, when all assumption updates and resulting forecasting

value changes have been completed, the third and last phase

consolidating all suggested changes takes place.

DevelopAnalyticalMethods

Assumpt. Updatesfor FC P

DevelopProcedures

May require re-issueof correctd FC

RESONSIBLE ROLES:collect "driver intelligence" a sa ppropria te for specific RoleAna lyze "Drivers" for Impa ct

RESONSIBLE ROLES:collect "driver intelligence" a sa ppropria te for specific RoleAna lyze "Drivers" for Impa ct

DevelopGuidelines

Periodic Forecasting & DemandManagement Cycle

Forecast(s) forPeriod P-1

RESPONSIBE ROLES:Receive "Foreca sting a nd Dema nd

Management Package" andensure completeness

RESPONSIBE ROLES:Receive "Foreca sting a nd Dema nd

Management Package" andensure completeness

RESPONSIBLE ROLES:review sta tistica l foreca st

ma ke correction cha nges a sneeded

provide Change Form to FC Mgr

RESPONSIBLE ROLES:review sta tistica l foreca st

ma ke correction cha nges a sneeded

provide Change Form to FC Mgr

Actual Sales forPeriod P-1

Mktg / SalesDrivers/Assumpt's

used in P-1 FCOper's Drivers /

Assumptions usedin P-1 FC

Show:a

Documents

Demand Management Strategy and Development