FORECASTING AND THE ENTERPRISE

EXECUTIVE SUMMARY Executives must include some form of forecasting in nearly all decisions they make as most operating decisions rely on “the future” as a significant input. As a result, good forecasting is a necessity. The more management understands forecasting techniques and processes and how they should manage and organize a successful forecasting function, the more successful the firm will be. This document addresses the key organizational, management, process, and operational aspects of forecasting that allow an enterprise to use it to drive corporate decisions. The main points or the areas on which management should most heavily focus their efforts include:

FORECASTING AND THE ENTERPRISE

Best practices for operating an effective forecasting function

Copyright 2002 - Inforte Corporation

This document addresses the key organizational, management, process, and operational aspects of forecasting that allow an enterprise to use it to drive corporate decisions The main points or the areas on which management should most heavily focus their efforts include: Organizational

• Full senior management commitment to forecasting as an enterprise-wide initiative is critical Management must be prepared to lead by example The behaviors and attitude that management portrays directly influences the way the rest of the organization

responds to the forecasting function By eliminating forecasting politics from its behavior, management can significantly reduce the amount of politicking that takes place

throughout the rest of the organization

• Forecasts should drive decisions in all functions Clear guidance should be given across all parts of the organization as to the importance of forecast results and the need to respond rapidly and appropriately All functions within the organization should become more demand-driven If senior management is committed to the process, reviewing results and basing decisions on forecasts, business area leadership will follow

• It is crucial that the forecasting function be centralized and objectified Although

forecasting should be collaborative, across both internal departments and external

partners, reporting relationships should remain independent from P&L areas to ensure objectivity

Management & Process Implementation

• One of the most important steps in implementing a successful management process for the forecasting function is performing a gap analysis between current capabilities and where the firm should be This should, in fact, be an ongoing process that allows

management to track the effectiveness of the process on an ongoing basis

• Creation of a formal plan for the forecasting function is crucial to its success within the organization During the drafting of the plan, management should help to scope and define expectations and goals for the forecasting function It is important that

management fosters an environment in which bias is minimized Formalizing the process and decision rules within the forecasting function and rewarding forecasting accuracy can minimize bias and keep both the preparation and the analysis of the forecasts as objective

as possible

• One of the most overlooked, but also one of the most important aspects, of the

forecasting process is the enterprise response process A formal enterprise response plan defines how managers should review forecasts and determine subsequent actions The creation of a formal response plan is critical for a firm to be able to respond quickly and in unison to demand conditions

Operational

• Forecast preparation requires that forecasters work with the users of the forecast to define and agree on the dynamics of the system being forecast It is important that users and forecasters collaboratively agree on the interrelations between variables, the constraints and risks of the forecast, the appropriate timeframe and the appropriate level of detail By working collaboratively, forecasters and users are able to establish clear lines of

communication, alleviating one of the most common problems in the forecasting process – the lack of trust and understanding between preparers and users

• During technique selection it is important to consider a number of factors including the characteristics of the situation being forecast, quality of available input and the type of output required It is also important to assess the known strengths and weaknesses of each technique Although they should be used selectively, judgmental techniques are occasionally the most appropriate Documented guidelines should be established and used

to determine when to correctly apply judgments during the forecasting process

• It is vital that forecast accuracy is carefully defined and tracked It not only forms the basis for many statistical projection models, it is also used at the corporate level to

determine the level of slack to be kept in assets, capital and resources Forecast accuracy

is used to determine the desired level of enterprise responsiveness – the enterprise should

be able to respond fast enough to make up for the average error in the forecast

TABLE OF CONTENTS

1.0 INTRODUCTION & OBJECTIVES 6

2.0 FORECASTING OVERVIEW 12

2.10 WHY IS FORECASTING IMPORTANT 12

2.20 FORECASTING HORIZONS 12

2.30 FORECASTING MODELS &TECHNIQUES 13

3.0 ORGANIZATION & CULTURE CONSIDERATIONS 14

3.10 LEVEL 2–REPEATABLE 14

3.101 Remove politics from the forecasting process 14

3.102 Limit influence of opinion on quantitative results 15

3.103 Formalize a structure for the forecasting function 15

3.104 Implement a career path for forecasters 15

3.105 Ensure the forecasting team has a comprehensive skill mix 15

3.106 Define the responsibilities of the forecasting team 16

3.20 LEVEL 3–DEFINED 16

3.201 Ensure full senior management commitment 16

3.202 Ensure strong leadership within the forecasting function 16

3.203 Implement a collaborative forecasting approach 17

3.204 Centralize and objectify the forecasting function 17

3.205 Ensure reporting relationships are independent 17

3.206 Rethink the training approach 17

3.207 Conduct training for management in forecasting 18

3.30 LEVEL 4–MANAGED 18

3.301 Measure and monitor forecasting performance 18

3.302 Implement demand-driven planning 18

3.303 Define responsiveness of the enterprise 19

3.304 Implement an executive steering committee 19

3.305 Align compensation to the firm’s demand-driven goals 20

3.306 Implement collaborative inter-firm forecasting 20

3.40 LEVEL 5–OPTIMIZING 20

3.401 Ensure forecasts drive decisions in all functions 20

4.0 IMPLEMENTATION CONSIDERATIONS 21

4.10 LEVEL 2–REPEATABLE 21

4.101 Conduct a diagnostic of current capabilities 21

4.102 Produce a gap analysis on current capabilities 21

4.103 Define the problem and needs for each forecast 22

4.104 Manage against bias 22

4.105 Produce a formal plan for the forecasting function 24

4.106 Define rules for management input 24

4.107 Plan adequate time, resources and access for data gathering and preparation 24 4.108 Objectify the data gathering process 25

4.109 Choose a standard forecasting model and supporting tool 26

4.20 LEVEL 3–DEFINED 26

4.201 Define and communicate expectations 26

4.202 Define, publish and communicate a methodology for the forecasting process 27 4.203 Identify critical communications points 27

4.204 Define formal rules for the interpretation of forecast results 27

4.205 Define a formal enterprise response process 27

4.30 LEVEL 4–MANAGED 28

4.301 Implement cross-functional performance measures 28

4.302 Implement a cross-functional performance measurement process 28

4.40 LEVEL 5–OPTIMIZING 29

4.401 Implement continuous forecasting 29

4.402 Identify a demand signal action map for all areas of the business 29

4.403 Monitor and continuously improve enterprise responsiveness 29

5.0 OPERATIONAL CONSIDERATIONS - FORECASTING PRACTICES 31

5.10 LEVEL 2–REPEATABLE 31

5.101 Forecast set-up 31

5.101.1 Define, justify and document assumptions 31

5.101.2 Collaboratively define and agree on the forecasting problem 31

5.101.3 Define appropriate timeframe for the forecast 32

5.101.4 Document constraints and risks 32

5.101.5 Define appropriate level of detail 32

5.101.6 Establish clear lines of communication between users and forecasters 33

5.102 Technique Selection 33

5.102.1 Determine the characteristics of the situation being forecast 33

5.102.2 Determine resources requirements 33

5.102.3 Assess quality of available input 33

5.102.4 Assess type of output required 34

5.102.5 Assess known strengths and weaknesses of techniques 34

5.102.6 Account for product life-cycle 36

5.102.7 Determine when to use judgmental techniques 36

5.104 Forecast accuracy 37

5.104.1 Lower uncertainty but be realistic 37

5.104.2 Evaluate the situation to determine accuracy requirements 37

5.104.3 Account for demand stimulation activity 38

5.105 Short term forecasting considerations 38

5.105.1 Define components of the forecast 38

5.105.2 Distinguish between sales, shipment and demand 38

5.105.3 Use statistical models 39

5.105.4 Determine granularity 39

5.105.5 Objectify the process 39

5.20 LEVEL 3–DEFINED 39

5.201 Define aggregation and combination rules 39

5.202 Identify turning points and trends 40

5.203 Present results simply and graphically 40

5.30 LEVEL 4–MANAGED 40

5.301 Participate in all major business area meetings regarding forecast interpretation 40

5.302 Review with users actual results versus forecast predictions 40

5.303 Forecast collaboratively across distribution channels 41

5.40 LEVEL 5–OPTIMIZING 41

5.401 Long term forecasting considerations 41

5.401.1 Use judgment but support it with quantitative methods 41

5.104.2 Account for economic cycles 42

6.0 REFERENCES 42

1.0 INTRODUCTION & OBJECTIVES

Forecasting, a critical operational function for virtually all organizations, provides an

organization with views of coming sales, changing patterns and long term trends It is nearly impossible to align the capacities, assets and resources of the firm appropriately without good forecasting practices This results in a failure to meet demand in robust economies and red ink in weak economies

While some companies rely on bottom-up forecasts from field sales, others depend on down planning from a central function However, very few companies have adopted a

top-systematic and well-organized approach to forecasting that accommodates forecasts from different parts of the organization with different levels of detail and different horizons

Furthermore, few organizations have established management processes allowing all functions within the firm to systematically review and update actions based on forecast changes Consequently, most enterprises do not move in unison with demand changes

This document, addresses the need for a systematic approach to forecasting It identifies the key organizational, management, process, and operational aspects of forecasting that allow a forecast to be the center of enterprise planning and the driver of corporate decisions

Companies that have successfully implemented these approaches are able to keep supply and demand in balance in any type of economic environment Because their profitability is less volatile than other firms, they meet their earnings projections, allowing management to focus

on strategic issues instead of fighting the fires caused by surprises and losses

Forecasting is at the heart of the demand-driven enterprise

Forecasting is at the heart of Demand Chain Management (DCM) - the operational process of projecting, capturing, stimulating and responding to demand in an integrated, enterprise-wide fashion Companies that do this well, tend to produce a consistent profit performance due to closer control over supply and demand

An effective DCM program begins with effective demand forecasting - an area that has been neglected within many corporations This document focuses on providing best practices for forecasting as a first and vital step toward DCM Research reveals the following observations about forecasting in the Fortune 500:

• Proven forecasting techniques are applied poorly in most organizations

• Forecast results are not communicated adequately across the organization

• Systematic processes that allow each and every department and business unit

to respond properly to projected demand levels do not exist or are developed

under-• Forecasting is currently static in most organizations and should be more continuous

• Due to poor forecasting practices, firms are missing a major opportunity to correlate costs and revenue much more closely, regardless of the prevailing demand environment

Good forecasting leads directly to higher revenue-cost correlation and higher profitability In addition, good forecasting practices can help highlight specific areas of high customer demand, even in poor demand environments It can also provide valuable feedback for product design and marketing as it detects emerging buying preferences

It is important to note, this document is not a comprehensive “how-to” manual on the various methods of forecasting It instead focuses on providing best practices for forecasting as a first

and vital step toward DCM The objectives of these best practices are to provide senior

management and forecasters with a fuller picture of how forecasting fits into the operations and strategy of the firm This document also provides guidelines for the initiatives and

procedures needed to become excellent at forecasting

This document also does not cover the many detailed processes required for the enterprise to effectively respond to the results of good forecasting We allude to those processes in this

document However, they are described in a separate paper by Inforte entitled Best Practice

for Enterprise Response to Demand Some of the processes outlined in the paper include:

• Supply Chain Responsiveness

Capability Maturity Model

The best practices contained within this document are framed within Inforte’s Demand

Forecasting Capability Maturity Model (CMM) The Capability Maturity Model (CMM) for

Demand Forecast & Response describes the principles and practices underlying demand

forecast and response process maturity It is intended to help companies improve the

maturity of their demand forecast/response processes in terms of an evolutionary path from

ad hoc, chaotic processes to mature, disciplined demand forecasting and response processes Once a firm has determined the level at which it resides, it is easier to determine the

processes and tools they must implement to achieve a more effective demand chain

management program

Inforte’s Demand Forecast & Response CMM is a top-down, assessment-based framework; it is not a bottom-up, business problem framework This is why it is important that a firm move upward from one level to the next Each level describes certain key processes that must be in place before residing on that level Additionally, for an organization to reside on a certain maturity level, they must have implemented all of the key processes for that level, and those

of the lower levels

The key processes are not intended to require a specific implementation or organizational structure Instead, they relate to activities that the organization must implement to reach a certain level of maturity The manner in which they are implemented can vary from firm to firm Additionally, the term key process simply means that these processes are key to reach the next level of maturity However, there may be additional “non-key” processes that are useful but not mandatory to reach the next level

Each section within this document outlines the activities and best practices that should be implemented at each stage of the Demand Forecast & Response CMM It does not, however, provide best practices for the Initial level as all forecasting this maturity level is ad-hoc – a practice not recommended for any firm

DEMAND FORECAST & RESPONSE CAPABILITY MATURITY MODEL

Standard enterprise demand forecast process for the organization; typical enterprise response is through quarterly departmental budget adjustments with business development initiatives periodically dynamically adjusted throughout the quarter

Objective, statistically-based demand forecasts; each business area

has formal forecasting processes; response processes include yearly departmental budgets updates with business development initiatives adjusted quarterly

Ad-hoc, subjective forecasting

1) Initial The demand forecast/response process is characterized as ad hoc, and occasionally

even chaotic Few processes are defined, and success depends on individual effort or heroics,

with forecasts often including subjective or judgmental inputs

Process Areas: There are no key process areas at the “Initial” level Except for Level 1, each

maturity level is decomposed into several key process areas that indicate the areas an

organization should focus on to improve its demand forecast and response processes

Horizon: undefined (i.e changes with each forecast)

Frequency: ad-hoc (i.e only run when management feels it’s necessary)

Tools: Heavy reliance on subjective forecasting; Excel spreadsheets

Metrics: No formal measurement of accuracy of forecasts or responsiveness of the enterprise

2) Repeatable Basic objective, statistically-based demand forecast management processes

are established to track history, accuracy, and actuals The necessary process discipline is in

place to repeat earlier successes with product lines/business units/divisions with similar data

Typical enterprise response is primarily through yearly departmental budgets, and also with

business development initiatives (sales/marketing/customer service plans) adjusted quarterly

Process Areas: The key process areas at Level 2 focus on the product line/business

unit/divisional concerns related to establishing basic, objective, statistically-based demand

forecasting controls They are Customer Information Capture, Departmental Forecast Creation, Forecast Review, Executive Alignment, Departmental Response, and Organization Process

Definition

Demand Information Capture: ability to systematically and objectively capture

customer demand information in each department/business unit/product line/channel

Departmental Forecast Creation: ability to create a statistically-based forecast for

the department/division/etc

Forecast Review: formal rules, tools, and processes are defined in each

department/division/etc for the interpretation of forecast results

Executive Alignment: full senior management commitment to forecasting and

response as an enterprise-wide commitment

Departmental Response: response process in place to adjust departmental/division

budgets and plans based on forecast

Organization Process Definition: a formal structure for the forecasting function is

defined and sufficient resources and budget are allocated to the forecasting function

Horizon: Medium to long-term forecasting; looking to determine demand for the next year

and, occasionally, the upcoming quarter

Frequency: Forecasts are produced on a yearly basis for use in departmental budget

adjustments and on a semi-annual (or quarterly) basis for adjustment of business

development plans

Tools: Customer Relationship Management System, Opportunity Management System, Supply

Chain Management System, Statistical Forecasting Program, Data Warehousing, Demand Planning System, Marketing Analytics System, and Order Management System

Metrics: departmental forecast accuracy, departmental response time, budget variance

3) Defined The demand planning process for forecast activities is documented, standardized,

and integrated into a standard enterprise demand forecast process for the organization All forecasts use an approved, tailored version of the organization's standard forecast process for developing and maintaining forecasts Typical enterprise response is primarily through

quarterly departmental budget adjustments, and also with business development initiatives (sales/marketing/customer service plans) periodically dynamically adjusted throughout the quarter

Process Areas: The key process areas at Level 3 address both product line/business

unit/divisional and organizational issues, as the organization establishes an infrastructure that institutionalizes effective demand forecast management processes across all product

lines/business units/divisions They are Aggregated Input Collection, Standard Output

Distribution, Enterprise Response, Governance Process Development, and Corporate

Communication Process

Aggregated Input Collection: a standard, objectified process is in place across all

product lines/business units/divisions for collecting forecast inputs to help with the creation of an enterprise-wide forecast

Standard Output Distribution: standard, objectified process for distribution of a

unified forecast to all product lines/business units/divisions

Enterprise Response: enterprise responsiveness goals are set; standardized

processes for adjusting budgets, inventory policies, resources, service levels, etc to the forecast exist across the organization

Governance Process Development: the development of standard forecasting

meeting schedules, agendas, participants, roles and responsibilities across the

organization

Corporate Communication: process for communicating expectations and gathering

feedback on corporate forecasting and response policies and goals

Horizon: Medium-term forecasting; goal is to determine demand on a quarterly basis and,

occasionally, adjust the forecast once or twice a quarter

Frequency: Forecasts are prepared on a quarterly basis for departmental budget adjustments

and are periodically, dynamically adjusted for use in business development plans throughout the quarter

Tools: Executive Dashboard, Output Distribution System, Decision Support System, Employee

Relationship Management System, Responsiveness Scorecard, and Inventory Optimization System

Metrics: enterprise forecast accuracy, enterprise response time, stock-out/capacity out

situations, campaign effectiveness, product introduction rate, warehousing costs,

obsolete/excess inventory cost, time-to-market

4) Managed Detailed measures of the forecast process accuracy and response are collected

Both the forecast process and responses are quantitatively understood and controlled Typical enterprise response is primarily through periodic scheduled dynamic departmental budget allocations throughout the quarter, requiring prioritization of functional initiatives based on contextually-relevant demand forecast information (units/headcount requirements/etc instead

of recognized revenue), with most business development activities (sales/marketing/customer service decisions) tied directly into demand forecast information

Process Areas: The key process areas at Level 4 focus on establishing a quantitative

understanding of both the forecast process and the enterprise response They are Forecast Performance Monitoring, Quantitative Process Management, Forecast Accuracy Assurance, and Collaborative Inter-Firm Forecasting

Forecast Performance Monitoring: review process that includes continuous review

through a high level of collaboration between users and forecasters during the forecast process as well as a formal monthly or quarterly review process with the forecast team and users to asses forecast performance and define improvement priorities

Quantitative Process Management: control the process performance and cost of

the forecast creation/distribution and response process quantitatively

Forecast Accuracy Assurance: reviewing and auditing of working procedures to see

that they comply with applicable standards and procedures Management is provided with the results of the reviews and audits

Collaborative Inter-Firm Forecasting: define process for collecting inputs and

sharing results with other value system partners

Contextually-relevant Forecasting: process for turning the enterprise-wide forecast

into the most relevant view for the department/division

Horizon: Short-term, operational forecasts; goal is to assess near-term demand (i.e

anywhere from several times a quarter to hourly/weekly for business development initiatives)

Frequency: Departmental forecasts are produced and adjusted several times a quarter while

business development activities utilize a continuous forecasting/response approach

Tools: Accuracy Scorecard, Functional/Departmental Application Integration to Executive

Dashboard, and Interconnectivity with External Partners

Metrics: forecast error variability, order processing lead time, fulfillment percentage per

customer, supplier lead time, resource allocation, call center response time, return-rate, customer service levels, service cost per customer, product mix effectiveness, inventory turns

5) Optimizing Continuous process improvement is enabled by quantitative feedback from

the process and from piloting innovative ideas and technologies All functional decisions enterprise-wide are continuously made and adjusted based on contextually-relevant demand forecast information

Process Areas: The key process areas at Level 5 cover the issues that the enterprise must

address to implement continual, measurable forecast/response process improvement They are Process Change Management and Technology Change Management

Process Change Management: Continually improve the forecasting processes with

the intent of improving forecasting quality and increasing demand responsiveness

Technology Change Management: Identify new demand forecasting technologies

and inject them into the organization in an orderly manor

Horizon: Ranges – however, any forecast produced (from a long-term, strategic forecast to

an hourly, operational forecast) is continuously updated, adjusted, expanded based on current demand information

Frequency: Continuous; all decisions are based on an ever-changing view of demand

Tools: Demand Signal Action Map

Metrics: depth of the Value Offering Point (VOP) – this measures at which point the firms

operations are integrated into the customer’s value chain; the goal is to move integration from the point of customer order to collaboration on activities further upstream, providing greater visibility

2.0 FORECASTING OVERVIEW

2.10 Why is Forecasting Important

Executives must consider some kind of forecast in most critical operating decisions they make

Strategic planning, budgeting, capital investments, marketing, manufacturing scheduling and research and product development all use “the future” as a primary informational input Without sound predictions of demand, critical decisions regarding the levels and alignment of assets, resources and capacities become highly risky Inaccurate demand projections lead to misalignment between cost and revenue and therefore to lower profitability

Good forecasting is essential and the more management teams know about applying forecast techniques and how they should organize and manage a successful forecasting function, the better off (i.e the more profitable) they will be

The business benefits from forecasting are exceptionally compelling These proven

advantages include1:

• Increased profits from operations

• Decrease in non-productive cash consumption

• Increase in factory or back-office processing/support utilization

• Decrease in excess and obsolete inventories

• Increased inventory turns

• Decrease in negative manufacturing variances

• Increased performance to customer request date

• Decrease in number of stock-out (or capacity-out) situations

• Decrease in cost of purchased items

• Decreased time to market for new products

• Higher yield on products

• More effective product mix

2.20 Forecasting Horizons

Companies use different types of forecasts to provide the projections they need over a variety

of time horizons in order to make appropriate management decisions The most popular of these various forecasts include:

• Long range forecasts - used for strategic planning and typically entail forecasts of

market size and opportunities, structural changes in industry, customer behavior, major technological innovations, etc

• Medium term forecasts – typically of economic conditions, competitive conditions,

specific known events (e.g Y2K, Euro currency introduction, deregulation, etc.) and other shifts in the demand environment

• Operational (short-term) forecasts - used for assessing near term demand This type

of forecast includes sales and order forecasts and is the principal input to operations planning and execution Therefore, it has the biggest impact on short-term operating profitability

• Lifecycle forecasts – forecasting demand for products/services based on their stage in

the product lifecycle These forecasts are typically components of operational and medium term forecasts, but are sometimes conducted separately to assess the impact

1 Crosby, J (1999) Cycles, Trends and Turning Points New York, NY: NTC Publishing Group

or feasibility of product/service launches and/or changes

• Initiative forecasts – assessing impact of a marketing campaign, a product bundle, a

joint alliance, entry into a new market, etc These forecasts are typically components

of operational and medium term forecasts, but are sometimes conducted separately

to assess the impact or feasibility of an initiative

How each company uses these types of forecasts is dependent upon their specific business and industry

2.30 Forecasting Models & Techniques

Forecasting techniques can be broken down into qualitative and quantitative methods The two most popular quantitative models are time series and explanatory (or causal)

Time series is based on historical data and is the most commonly used method for producing operational forecasts It aims to discover the pattern in the data and extrapolate it into the future For short-term sales forecasts this is, in most cases, a good method Multiple patterns and extrapolations are possible with this method Data can be extrapolated in a straight line, weighted to more recent data, averaged in different ways, adjusted for seasonality, etc Time series methods allow firms to forecast using essentially only their own historical data As it relies on patterns and pattern changes it dependent entirely on past data Time series should

be used when several years worth of data exist and trends are relatively stable Because this method relies on historical patterns, any significant future deviations from past patterns, turning points, will not be detected

Explanatory methods assume the data being forecast has an explanatory relationship with one

or more independent variables For example, the number of new loans generated could relate

to housing starts, GDP and interest rates The objective is to find the form of the relationship, create the formula and to use it to predict future values as inputs change Some models are estimated by econometric (regression) techniques, other models are related deterministically (e.g revenue – expenses = profits)

Explanatory models are typically more difficult to define than time series as both substantial historical data and knowledge of the relationships between variables must be defined A common goal of forecasters is to graduate from time series to explanatory models as they gain understanding, history and experience with a given situation being forecast

Qualitative forecasting is very commonly used, though unfortunately less accurate than

quantitative methods Many organizations use a judgmental form of forecasting, using the opinions of executives, managers and other experts to derive a forecast There are a number

of techniques in use to ensure bias is reduced and the process is systematic; however, there is significant evidence to show that judgmental methods are inferior to quantitative methods Opinions are an important part of forecasting, but managers and forecasters must be

exceptionally careful in when applying judgments The typical rule of thumb – less is usually better

3.0 ORGANIZATION & CULTURE CONSIDERATIONS

“Forecasting is a human activity usually carried out by many individuals in the organization

We should therefore address the organizational environment, culture and process and how it interacts with the use of quantitative and qualitative techniques for generating a forecast.”

Forecasting is significantly under-utilized within most organizations This is often because the forecast process and results are not respected Politics or management manipulation may impact the forecast’s accuracy and, therefore, the organization’s trust in it It many cases, the formal forecasting processes and structure are not adequate, resulting in poor accuracy and a low or negative impact on decision-making

Additionally, most organizations have not dedicated enough thought to the job and career needs of the forecasting function However, it is critical to do so as the morale and turnover within the forecasting function can significantly impact the quality of results and the credibility

of the forecasting function within the firm

This section addresses the organizational considerations that can very often prevent

forecasting from being successful It is primarily intended for senior management, although forecasting functional management and P&L executives will find it useful to review By

addressing the areas that follow, management can help to make forecasting a success and assist the organization in becoming more demand-driven

3.10 Level 2 – Repeatable

3.101 Remove politics from the forecasting process

• Set guidelines for senior management to ensure they are not manipulating sales, cost

or financial projections This sets a clear and problematic behavioral example that is likely to be followed throughout the organization

• Ensure that the definition and building of forecasting models are not influenced by corporate politics There is increasing evidence that the politics of model building may

be the single most important factor in determining the success or failure of a particular corporate modeling project

• Formally discuss the issue of politics in the forecasting process with the management team and issue guidelines such as the following:

ƒ Projections are not manipulated for budget purposes

ƒ Projections are not manipulated to dress up numbers and buy time

ƒ Projections are not manipulated for stock price or other reasons2

ƒ Managers are not overly conservative so as to always be seen to exceed targets

ƒ Managers are not underreporting in order to create financial buffers

ƒ Business units are not withholding information for budget advantages

ƒ Information is being shared freely between areas of business

• Implement review processes to ensure guidelines are being followed

• Define leadership guidelines to ensure all members of senior management

communicate the same values to the organization:

ƒ A culture of honesty must be fostered if forecasting is to be successful

ƒ Realistically face the facts in a demand environment

2 Ensure guidelines cover other ulterior motives such as ability to get bank loans, ability to get internal resources, ability to get venture capital, or value in an M&A/LBO, etc

ƒ React systematically and quickly

ƒ Trust the results of the forecasting process

ƒ Monitor for signals in the demand environment

ƒ Continuously improve forecasting accuracy and speed of response

3.102 Limit influence of opinion on quantitative results

• Ensure that forecasters are permitted to produce quantitative results without the influence of management opinion and that results are taken “as-is” If senior

management is seen to be manipulating results, a clear example is set indicating that the forecast results cannot be trusted and that manipulation is permissible

3.103 Formalize a structure for the forecasting function

• Define a plan for the organizational structure to support forecasting

• Ensure that sufficient resources and budget are allocated to the forecasting function

If necessary, benchmark against similar organizations with good forecasting practices

(see Inforte’s DCM Index for additional information)

• Define and implement formal management processes to ensure successful execution

of the forecasting process

3.104 Implement a career path for forecasters

• Ensure the organization perceives forecasting as a career and not a short-term

stepping-stone to something else

• Staff the forecasting function with full time forecasters wherever possible Unless the forecasters are focused on their role, the forecasting function will tend to be sub-optimized

• Develop career models and paths for forecasters in the same manor other roles in the firm are developed Failure to implement a career-oriented structure will lead to low morale and turnover

3.105 Ensure the forecasting team has a comprehensive skill mix

• Understand the range of roles within the forecasting team Typical roles include:

ƒ Aggregation and analysis

ƒ Business area forecasters

ƒ Long range planners

ƒ Inter-company task force for producing collaborative forecasts across a firm’s multi-tiered channels and distribution network

ƒ Economic monitoring team, essential for large companies, to define

explanatory models linking economic conditions to the firm’s business

• The types of skills necessary for an effective forecast team include:

ƒ Leadership

ƒ Quantitative analysis

ƒ Statistical modeling and forecasting experience

ƒ Graphical data representation abilities

ƒ Inventory control policy knowledge (e.g APICS qualification)

ƒ Risk management expertise (e.g for insurance and financial services)

ƒ Supply chain planning skills

ƒ Pricing and yield management understanding

3.106 Define the responsibilities of the forecasting team

• Effective forecasting teams have responsibility for the following:

ƒ Definition of the forecasting model - to be approved by the steering

committee The forecasting team should involve affected business areas to ensure buy-in and full information

ƒ Assessing and recruiting appropriate skill mix to the forecasting team

ƒ Ensuring the needs of business areas are fully defined and incorporated into the forecasting process

ƒ Creating and communicating a clear definition of what is expected from the business areas as input and what is expected of the users of the forecast

ƒ Tracking, monitoring and improving forecast accuracy

ƒ Ensuring new product/service launches and acquisitions are incorporated into the groups mission and that the necessary additional processes are defined and additional resources procured

ƒ Monitoring for techniques and approaches, including outside the company that can yield improvements in the process

ƒ Maintaining good relations with the forecasters and business areas in the company forecasting alliance

inter-ƒ Defining the method of aggregation of component forecasts and tracking forecast error at both the component and aggregation level

ƒ Defining the various contextual views of component and aggregate forecasts required by the various areas of the business and the alliance

3.20 Level 3 – Defined

3.201 Ensure full senior management commitment

• Recognize forecasting as an enterprise-wide commitment, requiring organizational, process and technology change

• Commit to the following types of activity to support successful forecasting:

ƒ Constantly communicate importance of forecasting to corporate results

ƒ Participate in reviews

ƒ Actively seek ideas for improvement

ƒ Reward demand-driven initiatives

ƒ Place a premium on forecasting accuracy within business units

ƒ Learn the basics and potential applications of forecasting

ƒ Ensure leaders within the firm share similar values regarding forecasting

3.202 Ensure strong leadership within the forecasting function

• Install strong leadership within the forecasting function One of the bigger challenges

in implementing a successful forecasting practice is ensuring forecasting results are appropriately and consistently communicated and interpreted throughout the

organization – a strong leadership team can ensure this takes place

• Hire a Director of Demand Planning

3.203 Implement a collaborative forecasting approach

• Ensure high levels of collaboration among business areas and the forecasting function This is especially important in areas that are difficult to forecast, or in areas where the forecast team is inexperienced The key is to work closely with the area of the

business concerned Their expectations must be set appropriately and a joint effort should ensue to drive up forecast accuracy

3.204 Centralize and objectify the forecasting function

• Define degree of centralization for forecasting function In most cases, a central organization with designated liaisons throughout the business works well

• Centrally review all forecasts produced by business areas, to ensure consistency and quality This also allows the central forecasting organization to provide coaching

• Utilize CRM systems as much as possible to systematically track opportunities

Similarly use ERP systems to track shipments and orders

• Prepare central organization to aggregate forecasts from the various areas of the firm

In some cases the function will run all forecasts at the most granular level and then aggregate them In other cases they will coach and QA the production of component forecasts from the various units and then aggregate the data themselves

• Give central function complete access to the raw data within the various business areas

• Ensure the following when component forecasts are generated in the business areas:

ƒ Rules are applied consistently across the firm based on the type of forecast

ƒ Data infrastructure is consistent and clean across the firm

ƒ Accuracy levels are consistent

ƒ Frequency and detail is consistent across the firm

ƒ Inter-business unit interactions are efficient

3.205 Ensure reporting relationships are independent

ƒ Keep forecasting area independent from P&L areas This limits the risk of influence by direct supervisors with vested interest in and intense desires for certain outcomes This is not intended to imply that all executives are biased, but in order to ensure accuracy that scales over a large and diverse organization, building objectivity into the structure of the firm is highly advisable

• Structure reporting relationship through an objective area In many firms, the central forecasting function reports through finance in order to leverage quantitative skills and independence from operating units

• Keep the forecasting function separate from the sales force Research shows that considerable bias often impacts the sales force In some organizations forecasting is structured through Sales Operations

3.206 Rethink the training approach

• Do not overly focus forecasting training on methods and techniques Instead, cover other key factors in forecasting training, such as:

ƒ Firm’s goals and expectations regarding forecasting

ƒ Forecasting processes and metrics

ƒ Identification of situations where systematic forecasting can improve

organizational decision making

ƒ Pros and cons of various methods

ƒ How to work in a forecasting team

ƒ How to choose an appropriate time horizon

ƒ Finding appropriate data and adjusting it for outliers

ƒ How judgment can be incorporated into statistical forecast (and when it should not)

ƒ How large changes in the macro environment can be monitored

ƒ Level of appropriate aggregation in the forecast

ƒ Effective methods for combining forecasts

ƒ How to avoid or minimize communication problems between preparers and users of forecast

3.207 Conduct training for management in forecasting

• Do not assume that MBA programs have adequately prepared managers for

participation in forecasting efforts

• Assess whether managers have had formal forecasting training within the last 10 years

• Train executives so that staff can present an array of forecast techniques and outputs

and management can assess forecasts based on merits

3.30 Level 4 – Managed

3.301 Measure and monitor forecasting performance

• Define and track metrics that provide a view of how well the forecasting operation is performing These may include:

ƒ Forecast accuracy (e.g measure of forecast versus actual results)

ƒ Revenue-to–Cost variability by business unit (see Inforte’s DCM Index for

additional information)

ƒ Inventory turns

ƒ Revenue per employee per business unit

• Ensure the forecasting process is reviewed regularly and that direction is given in terms of areas for improvement

• Ensure forecast process and organization does not lose credibility If reviews reveal that forecasts are not being used, investigate and resolve the causes

3.302 Implement demand-driven planning

• Commit to a demand-driven philosophy of operational management and ensure that this commitment is widely communicated

• Refine the firm’s operational planning methodology so that all plans, decisions and initiatives are based on a robust and relevant view of demand Insist that this

approach is used for all business plans

• Issue guidelines for demand driven plans that include the following:

ƒ Identification of projection method and assumptions

ƒ Identification of other demand factors that may impact the plan

ƒ Implementation of a monitoring process to assess the impact of ongoing changes on the plan

ƒ Monitoring of resource, budget and operations responses to fluctuations in projected demand

3.303 Define responsiveness of the enterprise

• Recognize that forecasts will be inaccurate to some degree and that the level of forecast error should be the driver for the level of responsiveness

• Specify the responsiveness factor for each business unit and department within the enterprise The responsiveness factor is the required agility to produce results that fall within the targeted variability between revenue and costs The following factors

influence the responsiveness factor:

ƒ Type of business, product and channels being used

ƒ Stage of product/services in the lifecycle

ƒ Current volatility of results versus benchmarks

ƒ Legacy (current) resources and assets on-hand

• Define responsiveness scorecard and drive improvements Scorecard may include the following3:

ƒ Speed of recognition of demand changes

ƒ Speed of definition of required action

ƒ Safety stock levels in the supply chain

ƒ Supplier contract flexibility

ƒ Ability to quickly reallocate human resources

ƒ Ability to cross-train workers

ƒ Level of staging in production

ƒ Understanding of demand impacts throughout function

ƒ Speed of budget adjustments

• Create a long-term plan for implementing these changes across the enterprise and ensure objectives for managers across all departments and functions include

responsiveness to forecast results

3.304 Implement an executive steering committee

• Implement a steering committee of senior management, P&L executives and senior forecasting leaders with the following mission:

ƒ Define corporate expectations for forecasting

ƒ Define the organizational structure and management processes

ƒ Review and recommend improvements to the forecasting structure and

processes

ƒ Define and issue guidelines regarding how, when and why management may interpret, manipulate or apply judgment to the published forecast results

ƒ Adjudicate issues

ƒ Ensure collaboration across all areas of the business

ƒ Ensure forecast is completed and communicated regularly

ƒ Ensure consistency of results and enterprise response

ƒ Monitor forecast error

ƒ Track corporate performance against forecast error

3 Postponement manufacturing and JIT are examples of management techniques that allow the

organization to respond quickly when, but not before, demand is known

ƒ Define budget and resource levels

ƒ Oversee inter-firm activities and process

3.305 Align compensation to the firm’s demand-driven goals

• Factor the following into bonus plans for forecasters:

ƒ Forecast accuracy

ƒ Forecast improvement metrics

ƒ Forecast timeliness

ƒ Forecast budget performance

ƒ User satisfaction survey

ƒ Inter-firm forecasting performance

3.306 Implement collaborative inter-firm forecasting

• Extend forecasting processes to include collaboration with the firm’s partners This may include distributors, retailers, suppliers and other members of the firm’s value system Greater accuracy and better understanding of constraints can be achieved when forecasting is carried out over the full value system

• Generate guidelines for leadership of inter-firm collaborative forecasting, such as the

following:

ƒ Team should have active participation from all firms involved

ƒ Team should define mission and joint goals

ƒ Team should agree on models to be used in forecasting

ƒ Budget and resources should be agreed upon and allocated between the firms

ƒ Full access to input data should be arranged by all in the process

ƒ The various contextual views of component and aggregate forecasts required

by the various participants should be clearly defined

ƒ Team should agree on an approach to interpretation so that both results and interpretation are consistent across all participants

ƒ Team should report to steering committees of all large participants

ƒ Leadership should rotate among the largest participants

3.40 Level 5 – Optimizing

3.401 Ensure forecasts drive decisions in all functions

• Give clear guidance to management across all parts of the organization as to the importance of forecast results and the need to respond rapidly and appropriately

• Ensure executives are committed to the process, reviewing results and basing

decisions on forecasts All functions can and should become more demand driven - not just customer facing ones, such as customer service, sales and marketing It is important that this happens across every function in the value chain including, but not limited to, manufacturing, procurement, product design, and risk management

• Provide a means for the management team to review the forecast results, the

enterprise wide action plans that result from the forecasts, as well as the status of the action plans Executive dashboards are often used for this purpose

4.0 IMPLEMENTATION CONSIDERATIONS

“The need today, is not for better forecasting methods, but for better applications of the techniques at hand.”

Specific management practices should be defined and implemented with regard to forecasting

in order to create an effective forecasting function This section contains guidelines and best practices for the implementation of processes and practices that tend to be most successful

It is primarily intended for managers of the forecasting function and their supervisors – those members of the organization that run the forecasting function It is also useful for P&L

executives and forecasters to review

4.10 Level 2 – Repeatable

4.101 Conduct a diagnostic of current capabilities

• Management should conduct an audit of current capabilities in the area of forecasting This should be an on-going process, where managers track a scorecard of metrics that indicate the effectiveness of the process

• Perform a diagnostic of current forecasting capabilities by assessing the firm’s

capabilities against the following factors4:

ƒ Forecasting methods

- Is the forecast independent of top management?

- Are objective methods used?

- Were structured techniques used to obtain judgments?

- Are the least expensive experts used?

- Is more than one method used to obtain forecasts?

- Do users understand the forecast methods?

- Are forecasts free of judgmental revisions?

- Are separate documents prepared for plans and forecasts?

ƒ Assumptions and data

- Is there ample budget for analysis and presentation of data?

- Does a central data bank exist?

- Are the least expensive macro economic forecasts used?

ƒ Uncertainty

- Are upper and lower bounds provided?

- Is quantitative analysis of previous accuracy provided?

- Are forecasts prepared for alternative futures?

- Are arguments listed against each?

ƒ Costs

- Is the amount spent on forecasting reasonable?

• Assess the quality of inputs, outputs, forecaster skills and tools in addition to these questions

4.102 Produce a gap analysis on current capabilities

4 Armstrong, J (1978) Forecasting with Econometric Methods: Folklore Versus Facts Journal of Business,

54

• Document and share results of capabilities assessment with the corporate steering committee

• Generate a roadmap for improvements and pursue it systematically

4.103 Define the problem and needs for each forecast

• Assess the needs of each forecast situation by asking the following questions5:

ƒ Why is the forecast needed?

ƒ Who will use the forecast and what are their specific requirements?

ƒ What level of detail or aggregation is required and what is the proper time horizon?

ƒ What data is available, and will the data be sufficient to generate the needed forecast?

ƒ What is the cost of the forecast?

ƒ What is the expected forecast accuracy?

ƒ Will the forecast be completed in time to help the decision-making process?

ƒ Does the forecaster clearly understand how the forecast will be used in the organization?

ƒ Is a feedback process available to evaluate the forecast after it is completed and adjust the forecasting process accordingly?

• Keep in mind, for planning purposes, that different forecast techniques, time horizons, frequencies, resources and teams are required for different types of forecasts (long-term, short-term, event-driven, etc)

• Be aware and account for the resources required from the various business areas to assist in the forecasting process Also be aware that many functions generate

projections of their own such as cost and budget projections, financial and EPS

planning, etc These should be coordinated with the demand forecasting process

4.104 Manage against bias

• Many research studies have shown that people inject significant bias into the

forecasting process during preparation and interpretation

• Create an environment and process that minimizes bias Following are typical biases, together with guidelines to counter them:

Bias: Inconsistency in application of the forecasting rules

Counter Activities:

- Formalize the forecasting process and decision rules

Bias: Optimism and wishful thinking, caused by people’s preferences for

future outcomes, affect their prediction of such outcomes

Counter Activities:

- Have forecasts compiled by a disinterested third party

- Have more than one person independently create the forecast

- Measure and reward forecast accuracy

Bias: Excessive conservatism and failing to change when new information is

available

5 Hanke, J & Reitsch, G (1997) Business Forecasting (3 rd Edition) New York, NY: Wiley

Counter Activities:

- Monitor for changes and design a process that triggers a reaction

to changing signals

- Measure and reward forecast accuracy

Bias: Recent events dominate those in the past

Counter Activities:

- Realize that cycles exist and that ups and downs are not permanent

Bias: Relying on events that can be specifically recalled from memory and

excluding other pertinent information

Counter Activities:

- Present complete information in a way that represents all angles

Bias: Forecasts and forecasters being unduly influenced by initial information

(anchoring)

Counter Activities:

- Start with full a set of objective information

- Allow multiple participants to state how they would change an initial statistical forecast

Bias: Believing patterns are evident and/or two variables are causally

related when they are not (illusory correlations) Related to this is the search for supportive evidence, the gathering of facts that lead toward certain conclusions and disregarding evidence that threatens

predetermined conclusions

Counter Activities:

- Induce nonconforming evidence

- Introduce the role of devil’s advocate

Bias: Believing persistent increases (or decreases) might be due to chance

rather than a genuine trend (regressions effects)

Counter Activities:

- Need to build understanding that if the errors are truly random then trend is unlikely to continue

Bias: Believing success is attributable to one’s skills whereas failure is due to

bad luck or someone else’s error This inhibits learning, as it does not allow for the recognition of one’s mistakes

Counter Activities:

- Do not punish mistakes

- Encourage people to accept mistakes and make them public so that they and others can learn from them and avoid similar mistakes in the future

Bias: Underestimating uncertainty through excessive optimism, illusory

correlation, or the need to reduce anxiety This results in underestimating future uncertainty

Counter Activities:

- Estimate uncertainty objectively

- Consider many possible future events by having different people propose unpredictable situations/events

Bias: Identifying problems too often in the area of ones own background and

experience

Counter Activities:

- Recruit people with different backgrounds and experience to independently suggest solutions

4.105 Produce a formal plan for the forecasting function

• Produce a formal forecast plan each year and update it each quarter It should include the following:

ƒ Scope and expectations for the function

ƒ Goals and objectives for the function

ƒ Vision for the function [e.g continuous forecasting goals]

ƒ Forecasts required

ƒ Frequency per forecast

ƒ Component and aggregation map

ƒ Model and techniques for each forecast

ƒ Data requirements for each forecast [including outside sources]

ƒ Budget and resources

ƒ Resource requirements from business areas

ƒ Communications plan

ƒ Format for forecast publishing

ƒ Signal monitoring methods and strategy for updates

ƒ Process improvement initiatives and goals

ƒ Metrics and tracking mechanism (e.g forecast error)

ƒ Function review process

ƒ Systems integration and other technology requirements

4.106 Define rules for management input

• The following common types of behavior by managers, throughout the enterprise, should be avoided:

ƒ Requests for staff revisions of forecasts Management should not request that staff adjust sales projection output, costs projections or pro forma profit/loss statements to a more favorable level

ƒ Management making its own revisions Management should not personally revise staff cost and revenue projections to reflect a more favorable level

ƒ Management requests for “backcasts” After senior management

predetermines an “appropriate” sales/revenue level, cost level or future financial position, management should not then request staff generate forecasts to support this level

ƒ Management ignores models/forecasts Staff forecasts should never be

discounted by senior management as unimportant and/or inaccurate

4.107 Plan adequate time, resources and access for data gathering and preparation

• Produce a data source and preparation plan This tends to be one of the most

important aspects of the forecasting process It is also very time consuming

• Consider the many different types of forecasting involved during data collection and generation of the preparation plan