Improving forecasting with imperfect advance demand information
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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)


Preliminary analysis

Preliminary Analysis


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


Improving forecasting with imperfect advance demand information

Forecast Accuracy per Area Sales Manager (ASM):


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)


Improving forecasting with imperfect advance demand information

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


Improving forecasting with imperfect advance demand information

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


Improving forecasting with imperfect advance demand information

Basic: Ft= max{FAt, Ot}

Number of orders:

Right tail estimation:

Non-stationary right tail estimation:


Comparison of methods mean absolute error

Comparison of Methods (% Mean Absolute Error)


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


Improving forecasting with imperfect advance demand information

Results

  • 192 data points:

    • 78 x No ADI

    • 65 x Perfect ADI

    • 49 x Imperfect ADI


Improving forecasting with imperfect advance demand information

Results


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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