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Improving Forecasting for the Supply Chain. Robert Fildes, Centre for Forecasting, Lancaster University Management School President, International Institute of Forecasters. Hierarchical Forecasting in the Supply Chain. Macro variables. e.g. growth. Consumer demand. e.g POTS. Business .

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improving forecasting for the supply chain

Improving Forecasting for the Supply Chain

Robert Fildes, Centre for Forecasting, Lancaster University Management School

President, International Institute of Forecasters

slide2

Hierarchical Forecasting in the Supply Chain

Macro variables

e.g. growth

Consumer demand

e.g POTS

Business

demand

Aggregate

Competitors'

Company demand

demand

Distribution Centres

+ Retailers

Product

Product

Product

Class A

Class B

Class C

1

2

3

slide3

Improving data flows

Forecasting for Production & Inventory

Market factors

Product

Classes

Class A

Class B

Total

AA

AB

AC

AX

BJ

. . . . .

. . . .

Products

Labour

Machines

Parts

Raw Materials

the results of poor forecasting
THE RESULTS of POOR FORECASTING
  • SHORT TERM
    • Stock/ service level
  • MARKET PLANNING
    • Capacity problems
    • Pricing; sales force management
    • inefficient financial management
  • LONG TERM TECHNOLIGICAL/SOCIAL CHANGE
    • Bankruptcy
slide5

EVALUATING THE FORECASTING ACTIVITY

  • Decision Effectiveness
  • Accuracy
    • bias
    • variance
  • Cost
  • Speed
  • Motivational Implications
  • Sales Force Remuneration
  • Feedback/ Self-fulfilling Prophecy
  • Goal Signalling

Forecasts are Frequently Politically Modified

forecasting requirements down the supply chain

Additional Information from the Supply Chain

Forecasting Requirements down the Supply Chain

Customers

  • What must be forecast
  • Customer demand
  • Retailer orders
  • Manufacturer Orders

£

Sales

Retailer

Shipments

Orders

Manufacturer

Parts & Materials Suppliers

information affecting the supply chain

Promotion:

2 for 1

Economic

factors

Customers

£

Sales

EPOS info

Retailer

Orders

Shipments

Manufacturer

Information affecting the supply chain

The basic model:

Orders=f(past orders) + judgemental estimates of promotions

The full model:

Orders=f(past orders, Sales, forecast sales, promotions, Events)

linking with the retailer
Linking with the Retailer
  • Sharing data
  • Sharing plans
  • Sharing forecasts

The benefits?

slide9

The Benefits of

Additional Information?

  • Sources of Additional Information:
    • from cross-correlations (steal)
    • from common factors, i.e seasonality
          • (Bunn &Vassilopoulis, IJF, 1993)
    • from orders
    • from external factors
    • from the marketing mix
    • from managerial information
the value of forecasting
The Value of Forecasting
  • Mixed evidence
    • value depends on absolute error level
    • depends on production system
    • depends on service level, cost trade-off
  • Achievable accuracy improvements
    • 30% possible

Service - inventory investment tradeoff curves

Service

Inventory Investment

typical approach to forecasting for the supply chain
Typical Approach to Forecasting for the Supply Chain
  • Data is often unstable
  • Statistical forecast obtained
    • usually exponential smoothing type approach
    • ‘rolling forecasts’ are used
  • Managerial judgement then used to adjust the forecast
benchmark forecast errors mape
BENCHMARK FORECAST ERRORS (MAPE%)

But these figures are too low

 50 - 100% for low frequency,

15-30% 1 month for product demand, retail

The Laws of Forecasting

· Forecast as short a period ahead as possible

· Forecast at the highest level of aggregation possible

improving the organization of forecasting
Developing consistent data

Increased software support

Improved techniques

Improved data bases

Improved communication with users

83%

70%

66%

61%

35%

IMPROVING the ORGANIZATION of FORECASTING

Activity

% Respondents

Scoring Important

slide14

The Supply Chain

Forecasting Process

System & Variables

Data

Previous

Forecast

& Error

Method based

forecast

Judgemental adjustment

Compare

Judgemental

forecast

Final Forecast

Additional market

information

& forecasts

- by category & total

How do organisations integrate different information sources?

how to design and manage the forecasting process to deal with market complexity

Issue:

How to design and manage the forecasting processto deal with market complexity
  • Staff
    • Motivation
    • Training
  • Information
    • Data base: Key variables collected regularly
  • Systems
    • The design and use of FSS
  • Organisational aspects
    • Value of good forecasting recognised
    • Information flows facilitated through integration
    • Responsibility of accurate and unbiased forecasts transparent
  • Moon & Mentzer (IJF, 03)
  • functional integration
  • Approach
  • systems
  • Performance measurement
issue staff
Issue: Staff
  • Technical staff
    • There aren’t any!
  • Forecasters
    • No training
    • Limited aspect of job for most
    • Bias (Sanders & Manrodt, Stewart)
    • Not appraised
  • Users
    • Ambivalent about the possibility of achievable improvements
    • Political nature of forecasting

Certification and training?

issue information organisational aspects
Issue: Information & Organisational Aspects
  • No responsibility for collection of forecast oriented information
  • ‘Pools of analysis’.
    • Information collected in different parts of the organisation is not transferred.
  • No clear organisational responsibility for the forecasting function
    • Location?
    • no learning, no improvement
motivations affecting forecast accuracy the agency problem
Motivations Affecting Forecast Accuracy- the ‘Agency Problem’
  • Use of high forecasts to support funding requests
  • Use of low forecasts to increase performance related pay
  • Desire (by operations or development) to hide or ignore product limitations corrupts data
  • Use of extreme forecast to achieve greater recognition
    • Benefits from being ‘extreme’
  • Financial prudence
  • Ideology
forecasting support systems fss

Issue: how to design and manage the forecasting process

to deal with market complexity

Forecasting Support Systems (FSS)
  • Systems designed to support forecasting by providing:
  • statistical methods,
  • facilities for formulating informed management judgments,
  • facilities for the integration of statistical forecasts with management judgment.And
  • Possibly an extended information set, e.g. prices

A Type of Decision Support System

complementary nature of statistical forecasts management judgment

Dealing with the complexity

Organisationally based Forecasting combines

statistical analysis with managerial judgement

Complementary nature of statistical forecasts & management judgment
  • humans are adaptable and can take into account one-off events, but they are inconsistent and suffer from cognitive biases
  • statistical methods are rigid, but consistent, and can take into account large volumes of information
combining statistical customer managerial forecasts

Forecasting Support System

Combining Statistical, Customer & Managerial Forecasts
  • Customer forecast used for first two periods
  • Customer forecast compared with actual for accuracy
  • Statistical and MI are compared with actual for accuracy
  • Separate accuracies are compared and used to improve process
  • Communication process is very important

The Effective System relies on:

Combining Different Information Sources

the stories

Issue: Forecasting systems

The Stories
  • No statistical basis of models
  • No ability to explore alternative models
    • Limited tailoring to user requirements
    • System used in default mode
  • No corporate technical knowledge
  • Poor measures of performance
    • no benchmarks
  • User interventions unstructured
  • No monitoring of effectiveness of user interventions
  • No history of interventions
the consequences of poor forecasts
The Consequences of Poor Forecasts
  • Too much stockor
  • Unnecessarily Poor Service

Service - inventory investment tradeoff curves

The wrong product in the wrong place at the wrong time

slide24

Examining Sales Forecasting Practiceto Improve Supply Chain Forecasting - Research Programme

  • Product hierarchy + data base
  • Organisational Requirements – for operations
  • Information flows + availability
  • Forecasting Methods
  • Current Accuracy Levels
  • Users
    • User interventions & their value
    • Motivation
    • Design involvement
  • System issues

Can we design better processes and systems to Improve Accuracy and Effectiveness?

ideal use of support system
Ideal Use of Support System

“ delegating to the system routine computations and resolutions of interaction s too complex for the manager to perform”

While

“leaving the judgements that the algorithm could neither make, nor recognize were needed, to the human”

Keen & Scott Morton, 1978

The FSS’s role is to effectively integrate the statistical methods with managerial judgement

consequences of non ideal use of fss
Consequences of non-ideal use of FSS
  • Judges read noise as systematic
  • Statistical forecasts distorted by transitory special events
  • Double counting of some effects
  • Basis of forecast is unclear and cannot be easily communicated
  • Wasted managerial effort
    • Managers may have an ‘effort budget’
objectives of this research programme
Objectives of this research programme
  • To understand the existing and potential design features which are conducive to the adoption, acceptance and effective use of FSS by forecasters.
  • To investigate the use of forecasting support systems (FSS) in companies to establish the role they play in forecasting processes and the extent to which their role can be improved;
  • to improve the effectiveness (and accuracy) when FSS are used to combine the strengths of statistical methods with managerial judgment

In addition, a methodological contribution is:

  • ·        To compare FSS usage in an organisational setting with experimentally based evidence.
supply chain forecasting the key issues
Supply Chain Forecasting – the Key Issues
  • Inclusive data base
    • Collaboration
  • Recruitment and Training of Staff
    • A Certificate in Forecasting Practice?
  • Organisational Issues
    • Motivation; information sharing
    • Organisational improvement through learning
  • The Forecasting Support System
conclusions
Conclusions
  • Value of improved forecasting in supply chain
    • substantial in situations with high noise
  • Major Improvements possible through design of FSS
    • Managerial judgment problematic
    • But increases user acceptability
    • accuracy?
    • special effects?
  • Organisational Responsibilities
    • User motivation
    • Performance measurement
    • Training (selection)

Despite the advances in statistical forecasting techniques and software, performance has improved little if at all (Moon et al, 2003)

slide30

What goes wrong?

  • In a phrase
  • the organisation does not care enough!
  • - its culture does not support learning
lancaster centre for forecasting
Lancaster Centre for Forecasting

Reference:

Armstrong, J.S. (ed.) ‘The Principles of Forecasting’, Kluwer, 2001.

  • Research
    • sponsorship of projects
  • Contract Research
    • market analysis and forecasting
    • price elasticity estimation
    • software appraisal/ development
      • call centre
      • pricing
    • company based Organisational Forecasting Audit
  • MSc Projects
    • 4mths, agreed brief, expenses
  • Seminar Programme & Forecasting Practitioner Network
  • Research Programme in Using Software Effectively

Director: Professor Robert Fildes, Lancaster Centre for Forecasting,

Lancaster University, LA1 4YX: Tel: (44) (0) 1524 - 593879

email: R.Fildes@Lancaster.ac.uk

slide33

System & Variables

Data

Previous

Forecast

& Error

Method based

forecast

Judgemental adjustment

Additional forecasts

- by category &

total

Compare

Judgemental

forecast

Final Forecast

Figure 1 The Forecasting Support System