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Improving Forecasting with Imperfect Advance Demand Information. Tarkan Tan Technische Universiteit Eindhoven October 23, 2007 Forecasting and Inventory Management: Bridging the Gap EPSRC project Meeting - London. Outline. Introduction Related Literature

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improving forecasting with imperfect advance demand information

Improving Forecasting with Imperfect Advance Demand Information

Tarkan Tan

Technische Universiteit Eindhoven

October 23, 2007

Forecasting and Inventory Management: Bridging the Gap

EPSRC project Meeting - London

outline
Outline
  • Introduction
  • Related Literature
  • Advance Demand Information (ADI)
  • Analysis and Proposed Methodology
  • Incorporating ADI
  • Conclusions and Future Research
introduction
Introduction
  • B2B Production Environments
    • High demand volatility
      • Seasonality
      • Changing trends
      • Affected by individual clients
    • Some clients provide information on their future orders
      • subject to changes in time (imperfect Advance Demand Information - ADI)
    • Demand forecasting in such a make-to-stock production environment
introduction1
Introduction
  • Motivation:
    • Dairy products company
    • 3 business lines: Food, Nutrition, and Pharma
    • Orders: single or call-off of a contract
    • Demand forecast is used in
      • Packaging- and raw material acquisition
      • Production planning
      • Financial forecasting and budget allocation
      • Milk allocation planning
      • Reserving inventory space
introduction2
Introduction
  • ADI collection:
    • Customers have their own production plans
    • Some customers place their orders in advance:
      • minimize the risk of unmet orders
      • parts of contracts
      • time allowance for arranging transportation
    • 30% of the orders are known by the end of the previous month (57% for Pharma)
introduction3
Introduction
  • If the order is known not to change => Perfect ADI
  • Impurity and uncertainty => Imperfect ADI
  • In our application, advance orders are never postponed or cancelled
  • The changes are in forms of increased orders
  • We made use of this observation, but similar methods can be devised for different forms of ADI
introduction4
Introduction
  • In many B2B environments, judgmental forecasts are preferred to statistical forecasts
    • specific customer information (customers ceasing operations for a period, capacity extensions, etc.)
  • By personnel with in-depth customer information (Area Sales Managers - ASMs), for each Product-Customer Combination (PCC)
  • Labor-intensive and repeats itself
  • Little time to get available data
preliminary analysis1
Preliminary Analysis
  • Forecasting System:
    • 12 months, rolling horizon, monthly updates
  • Group the forecasts according to the requirements
  • Define aggregation levels
  • Statistical forecast as an input to ASMs
  • Cap the number of product/customer combinations (PCC) for judgmental update
literature review
Literature Review
  • ADI
    • Review: Karaesmen, Liberopoulos, and Dallery (2003)
  • Imperfect ADI
    • DeCroix and Mookerjee (1997)
    • Van Donselaar, Kopczak, and Wouters (2001)
    • Treharne and Sox (2002)
    • Thonemann (2002)
    • Zhu and Thonemann (2004)
    • Tan, Güllü, and Erkip (2005, 2007)
  • Forecasting with ADI
    • Thomopoulos (1980)
    • Abuizam and Thomopoulos (2005)
slide13
ADI
  • some customers never change their orders
  • some others update (increase) in time
  • some others never provide any information
  • How can the placed order be classified?
    • "Perfect" ADI
      • Guaranteed by contracts
      • Analyze order history of PCC and build PCC profile
        • Those who never change their orders (reliable information)
        • Those who reach their historical maximum # of updates (Mij )
    • Imperfect ADI
      • Those who have not reached Mij
    • No ADI
slide14
ADI
  • Production/inventory models with ADI:
  • Dividing the demand into two groups (observed and unobserved) =>
    • independence violated (overlapping populations)
    • not making the best use of information
      • special patterns of ordering
      • timing or number of orders
bayesian updates
Bayesian Updates
  • Dependence on distributional assumptions
    • Normal => (e.g.: 75 observed, demand ~ Normal with st dev = 25, prior forecast = 100, posterior forecast = 102)
    • Poisson (# orders)=> (e.g.: 91 and 100 observed, average # orders = 5.25, prior forecast = 467, posterior forecast = 564)
  • Updates are one-sided
  • Only the information as to the total observed demand (or total number of observed orders) is utilized
    • Information on the individual order patterns of the customers not taken into account
analysis
Analysis
  • How to make use of individual order patterns of the customers?
proposed methodology
Proposed Methodology
  • Forecast for each PCC
  • Information from placed orders:
    • No Advance Demand Information (ADI)
    • "Perfect" ADI
      • Those who reached their historical maximum (Mi)
    • Imperfect ADI
      • Those who have not reached Mi
imperfect adi
Imperfect ADI
  • Some Possible Methods:
    • Basic
    • Binomial
    • Number of orders
    • Right tail estimation
    • Non-stationary right tail estimation
slide19
Basic: Ft= max{FAt, Ot}

Number of orders:

Right tail estimation:

Non-stationary right tail estimation:

proposed method

Monthly cycle

Real-time

Forecast agreement

Statistical forecast

ADI

ASM input

Evaluate accuracy

Final Forecast

Proposed Method
model
Model

CaseFinal Forecast

  • Perfect ADI: ADI
  • No ADI: Forecast Agreement
  • Imperfect ADI: Forecast Agreement + ADI
results example
Results (Example)
  • For a product which 5 customers order
    • PCC1, 2, and 3:
      • No ADI: F(PCC1-3) = 287
    • PCC4: 91 observed, M=1
      • Perfect ADI: F(PCC4) = 91
    • PCC5: 100 observed (single order), M=3, F w/o info = 90,

History: F1(PCC5)=180, D1=275; F2=90, D2=0; ...

      • Imperfect ADI: (NSRTE)

F(PCC5) = 100 + Av(95-10, (-90-10)+, ...) = 120

    • Ftotal = 287 + 91 + 120 = 498 (compare with 467 vs 564)
  • If PCC4 had ordered 33, Ftotal = 287+33+120 = 440
slide24

Results

  • 192 data points:
    • 78 x No ADI
    • 65 x Perfect ADI
    • 49 x Imperfect ADI
conclusions
Conclusions
  • A methodology to improve forecasting by making use of information
  • A number of methods for utilizing imperfect ADI
  • Takes individual ordering pattern histories and the current build-up of orders into account
  • Safety Stock Reduction:
    • Statistical forecast + ADI: 25%
    • Statistical forecast + ADI + ASM Update: 37%
future research
Future Research
  • Different methods for utilizing imperfect ADI
  • Incorporating this kind of ADI directly in production/inventory planning
  • Lot sizing
  • Inventory rationing based on ADI
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