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Improving Forecasting for the Supply Chain

Improving Forecasting for the Supply Chain

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Improving Forecasting for the Supply Chain

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  1. Improving Forecasting for the Supply Chain Robert Fildes, Centre for Forecasting, Lancaster University Management School President, International Institute of Forecasters

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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)

  8. Linking with the Retailer • Sharing data • Sharing plans • Sharing forecasts The benefits?

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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?

  15. 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

  16. 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?

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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?

  25. 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

  26. 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’

  27. 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.

  28. 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

  29. 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)

  30. What goes wrong? • In a phrase • the organisation does not care enough! • - its culture does not support learning

  31. 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

  32. 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