FORECASTING AND THE ENTERPRISE
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FORECASTING AND THE ENTERPRISE
Best practices for operating an effective forecasting function
Copyright 2002 - Inforte Corporation
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:
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.
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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.
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Copyright 2002 - Inforte Corporation
TABLE OF CONTENTS
1.0
INTRODUCTION & OBJECTIVES ...................................................................... 6
2.0
FORECASTING OVERVIEW .............................................................................12
2.10
2.20
2.30
3.0
WHY IS FORECASTING IMPORTANT ......................................................................... 12
FORECASTING HORIZONS ................................................................................... 12
FORECASTING MODELS & TECHNIQUES ................................................................... 13
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
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4.403
5.0
Monitor and continuously improve enterprise responsiveness ..................... 29
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
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Copyright 2002 - Inforte Corporation
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 topdown planning from a central function. However, very few companies have adopted a
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 underdeveloped.
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
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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
Revenue Management
Inventory Management
Sales Incentives
Market Segmentation
Dynamic Budgeting
Procurement
Financial Planning
As well as these generic responsiveness procedures there are many industry specific practices
for responding to demand, such as risk management within financial services - these are
addressed in a series of Inforte papers on industry-specific demand chain management.
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.
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DEMAND FORECAST & RESPONSE CAPABILITY MATURITY MODEL
5 – Optimizing
All functional decisions enterprise-wide are continuously made and
adjusted based on contextually-relevant demand forecast
information.
4 – Managed
Detailed measures of the forecast process accuracy and response
are collected; enterprise response is primarily through scheduled
dynamic departmental budget allocations throughout the quarter
3 – Defined
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
2 – Repeatable
1 – Initial
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.
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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
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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.
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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
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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:
•
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2.20
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
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:
1
•
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
Crosby, J. (1999). Cycles, Trends and Turning Points. New York, NY: NTC Publishing Group.
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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.
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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:
Projections are not manipulated for budget purposes.
Projections are not manipulated to dress up numbers and buy time.
Projections are not manipulated for stock price or other reasons2.
Managers are not overly conservative so as to always be seen to exceed
targets.
Managers are not underreporting in order to create financial buffers.
Business units are not withholding information for budget advantages.
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:
A culture of honesty must be fostered if forecasting is to be successful.
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.
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3.102
•
3.103
React systematically and quickly.
Trust the results of the forecasting process.
Monitor for signals in the demand environment.
Continuously improve forecasting accuracy and speed of response.
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.
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 suboptimized.
•
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:
Aggregation and analysis
Business area forecasters
Long range planners
Inter-company task force for producing collaborative forecasts across a firm’s
multi-tiered channels and distribution network
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:
Leadership
Quantitative analysis
Statistical modeling and forecasting experience
Graphical data representation abilities
Inventory control policy knowledge (e.g. APICS qualification)
Risk management expertise (e.g. for insurance and financial services)
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3.106
•
Supply chain planning skills
Pricing and yield management understanding
Define the responsibilities of the forecasting team
Effective forecasting teams have responsibility for the following:
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.
Assessing and recruiting appropriate skill mix to the forecasting team.
Ensuring the needs of business areas are fully defined and incorporated into
the forecasting process
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.
Tracking, monitoring and improving forecast accuracy.
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.
Monitoring for techniques and approaches, including outside the company that
can yield improvements in the process.
Maintaining good relations with the forecasters and business areas in the intercompany forecasting alliance.
Defining the method of aggregation of component forecasts and tracking
forecast error at both the component and aggregation level.
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:
Constantly communicate importance of forecasting to corporate results.
Participate in reviews.
Actively seek ideas for improvement.
Reward demand-driven initiatives.
Place a premium on forecasting accuracy within business units.
Learn the basics and potential applications of forecasting.
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.
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3.203
•
3.204
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.
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
3.205
the following when component forecasts are generated in the business areas:
Rules are applied consistently across the firm based on the type of forecast.
Data infrastructure is consistent and clean across the firm.
Accuracy levels are consistent.
Frequency and detail is consistent across the firm.
Inter-business unit interactions are efficient.
Ensure reporting relationships are independent
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:
Firm’s goals and expectations regarding forecasting
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3.207
Forecasting processes and metrics
Identification of situations where systematic forecasting can improve
organizational decision making
Pros and cons of various methods
How to work in a forecasting team
How to choose an appropriate time horizon
Finding appropriate data and adjusting it for outliers
How judgment can be incorporated into statistical forecast (and when it should
not)
How large changes in the macro environment can be monitored
Level of appropriate aggregation in the forecast
Effective methods for combining forecasts
How to avoid or minimize communication problems between preparers and
users of forecast
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:
Forecast accuracy (e.g. measure of forecast versus actual results)
Revenue-to–Cost variability by business unit (see Inforte’s DCM Index for
additional information)
Inventory turns
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:
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3.303
Identification of projection method and assumptions.
Identification of other demand factors that may impact the plan.
Implementation of a monitoring process to assess the impact of ongoing
changes on the plan.
Monitoring of resource, budget and operations responses to fluctuations in
projected demand.
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:
Type of business, product and channels being used
Stage of product/services in the lifecycle
Current volatility of results versus benchmarks
Legacy (current) resources and assets on-hand
•
Define responsiveness scorecard and drive improvements. Scorecard may include the
following3:
Speed of recognition of demand changes
Speed of definition of required action
Safety stock levels in the supply chain
Supplier contract flexibility
Ability to quickly reallocate human resources
Ability to cross-train workers
Level of staging in production
Understanding of demand impacts throughout function
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:
Define corporate expectations for forecasting.
Define the organizational structure and management processes.
Review and recommend improvements to the forecasting structure and
processes.
Define and issue guidelines regarding how, when and why management may
interpret, manipulate or apply judgment to the published forecast results.
Adjudicate issues.
Ensure collaboration across all areas of the business.
Ensure forecast is completed and communicated regularly.
Ensure consistency of results and enterprise response.
Monitor forecast error.
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.
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3.305
•
3.306
Define budget and resource levels.
Oversee inter-firm activities and process.
Align compensation to the firm’s demand-driven goals
Factor
the following into bonus plans for forecasters:
Forecast accuracy
Forecast improvement metrics
Forecast timeliness
Forecast budget performance
User satisfaction survey
Inter-firm forecasting performance
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:
Team should have active participation from all firms involved.
Team should define mission and joint goals.
Team should agree on models to be used in forecasting.
Budget and resources should be agreed upon and allocated between the firms.
Full access to input data should be arranged by all in the process.
The various contextual views of component and aggregate forecasts required
by the various participants should be clearly defined.
Team should agree on an approach to interpretation so that both results and
interpretation are consistent across all participants.
Team should report to steering committees of all large participants.
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.
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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:
•
4.102
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?
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?
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?
Costs
Is the amount spent on forecasting reasonable?
Assess the quality of inputs, outputs, forecaster skills and tools in addition to these
questions.
Produce a gap analysis on current capabilities
4
Armstrong, J. (1978). Forecasting with Econometric Methods: Folklore Versus Facts. Journal of Business,
54.
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•
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:
Why is the forecast needed?
Who will use the forecast and what are their specific requirements?
What level of detail or aggregation is required and what is the proper time
horizon?
What data is available, and will the data be sufficient to generate the needed
forecast?
What is the cost of the forecast?
What is the expected forecast accuracy?
Will the forecast be completed in time to help the decision-making process?
Does the forecaster clearly understand how the forecast will be used in the
organization?
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 (longterm, 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:
5
Excessive conservatism and failing to change when new information is
available.
Hanke, J. & Reitsch, G. (1997). Business Forecasting (3rd Edition). New York, NY: Wiley.
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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.
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-
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
•
4.106
•
4.107
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:
Scope and expectations for the function
Goals and objectives for the function
Vision for the function [e.g. continuous forecasting goals]
Forecasts required
Frequency per forecast
Component and aggregation map
Model and techniques for each forecast
Data requirements for each forecast [including outside sources]
Budget and resources
Resource requirements from business areas
Communications plan
Format for forecast publishing
Signal monitoring methods and strategy for updates
Process improvement initiatives and goals
Metrics and tracking mechanism (e.g. forecast error)
Function review process
Systems integration and other technology requirements
Define rules for management input
The following common types of behavior by managers, throughout the enterprise,
should be avoided:
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.
Management making its own revisions. Management should not personally
revise staff cost and revenue projections to reflect a more favorable level.
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.
Management ignores models/forecasts. Staff forecasts should never be
discounted by senior management as unimportant and/or inaccurate.
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.
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•
Work with IT to map data sources throughout the multiple systems that will be
involved in the forecasting process. IT should help to provide automated extraction
routines.
•
Ensure that all assumptions and methodology are documented when relying on outside
parties or business areas to gather data. It is often in the assumptions of what data to
include and what to leave out that the most inaccuracies are introduced.
•
Do not gather excessive amounts of data. Empirical evidence suggests that more
information does not necessarily improve accuracy. Instead assess each data set for
relevancy to the forecasting task at hand.
•
Prepare a template and checklist for data gathering activities so that data is
presented, as much as possible, in a similar form from all areas of the business.
4.108
Objectify the data gathering process
•
Keep data gathering and preparation as objective as possible. In general, forecasts
derived from human predictions have been shown to be inferior to statistical
forecasts.6
•
Familiarize the forecasting team with the many biases that are typically introduced in
the data and model preparation process.
•
Minimize data gathered directly from the sales force as salesperson forecasts have
been shown to be prone to significant bias. Sales person forecasts were once very
popular – salespeople are typically closer to the customer and, therefore, closer to
potential changes in the marketplace. However, empirical evidence shows salesperson
forecasts are notoriously inaccurate. 7
•
If using data from the sales force, keep in mind that salesperson forecasts
have been show to fluctuate considerably depending upon the mood of the
moment and whether the last few sales calls were successful or not.
Consider that sales people are often rewarded for beating targets and will
often set targets low deliberately. At the same time, sales managers want to
set targets high to motivate employees – so they adjust estimates upwards –
thereby confusing objective forecasts with desired targets.8
It is important to note that data gathered from management will tend to be overoptimistic. For example, they rarely predict decreasing sales or disappointing product
launches. This is not to say that managers do not have important, valuable
information for the forecasting process. But they do tend to be overly optimistic and
do not often separate personal or political interests from the best way to achieve
accurate predictions.
6
Dawes, R., Faust, D. & Meehl, P. (1988). Clinical Versus Actuarial Judgment. Science, 243, 1668-1674.
Hogarth, R. & Makridakis, S. (1981). Forecasting and Planning: An evaluation. Management Science, 2/81,
115.
Kahneman, D., Slovic, P. & Tversky, A. (1982). Judgment Under Uncertainty. New York, NY: Cambridge
University Press.
7
Walker, K & McClelland, L. (1991). Management Forecasts & Statistical Prediction Model Forecasts in
Corporate Budgeting. Journal of Accounting Research, 29, 373-382.
Winklhofer, H., Diamantopolous, A. & Witt, S. (1996). Forecasting Practice: A Review of the Empirical
Literature and an Agenda for Future Research. International Journal of Forecasting, 12, 193-221.
8
Makridakis, S. & Wheelwright, C. & Rob, J. (1997). Forecasting: Methods & Applications. New York, NY:
Wiley.
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