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Bullwhip Effect & Demand Information Sharing

Bullwhip Effect & Demand Information Sharing. John Boylan & Mohammad Ali Buckinghamshire New University EPSRC Launch Meeting, 24 October 2007. Outline. Approaches to the Bullwhip Effect Demand Information Sharing (DIS) and standard assumptions Scenarios presented in current literature

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Bullwhip Effect & Demand Information Sharing

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  1. Bullwhip Effect & Demand Information Sharing John Boylan & Mohammad AliBuckinghamshire New UniversityEPSRC Launch Meeting, 24 October 2007

  2. Outline • Approaches to the Bullwhip Effect • Demand Information Sharing (DIS) and standard assumptions • Scenarios presented in current literature • Uncertainty Principles • New scenarios introduced

  3. Bullwhip Effect Amplification of ‘noise’ as demand moves upstream Amplification of upstream inventory requirements

  4. Approaches to the Bullwhip • Control Theory • System Dynamics • OR / Statistical approach : Share downstream demand information with upstream links • Lee et al (2000) • Chen et al (2000) • Raghunathan (2003)

  5. Demand Information Sharing Papers share the following assumptions: • Demand follows ARIMA process • Residual noise is Gaussian • Linear hierarchy, one node at each echelon • Inventory rule is ‘Order Up To’ (OUT)

  6. Advantages Convenient mathematically Can be insightful Disadvantages Even if process is ARIMA, forecasting may not be ARIMA Alternatives Assume ARIMA process but use a non-optimal method (eg SMA, SES) Use state-space approach 1. ARIMA process

  7. Advantages Leads to tractable results Disadvantages May lead to low safety stocks if data is skewed NB: depends on inventory rule Alternatives Use non-standard ARIMA model with skewed noise distribution For slow-moving items, use Integer ARMA models (with Poisson noise) 2. Gaussian Residual Noise

  8. 3. Linear Hierarchy • Unrealistic to have single node at each echelon • Upstream propagation based on sum of demands: • MA(q1) + MA(q2) = MA(max{q1,q2}) • AR(p1) + AR(p2) = ARMA(p1+p2,max{p1,p2}) • Even if backward inference allows for identification of the process for total demand, it does not allow identification at each node

  9. 4. ‘OUT’ Inventory Rule • OUT leads to Yt = Dt + (St – St-1) • If optimal (MMSE) forecasting method used: St = mt + -1(p/(p+h))  √v Yt = Dt + (mt – mt-1) Immediately apparent that Bullwhip or Anti-Bullwhip may occur

  10. Upstream Translation of Demand (MMSE) ARIMA (p, d, qM) where qM = max {p+d, qR-L} Manufacturer (Upstream Link) MMSE Forecasting Method ARIMA (p, d, qR) Retailer (Downstream Link) Alwan et al (2003), Zhang (2004), Gilbert (2005)

  11. Upstream Translation of Demand (SMA) ARIMA (p, d, qR +n) Manufacturer (Upstream Link) SMA Forecasting Method ARIMA (p, d, qR) Retailer (Downstream Link) Where n is the number of historical terms used in forecasting

  12. Upstream Translation of Demand (SES) ARIMA (p, d, t - 1) + term Manufacturer (Upstream Link) SES Forecasting Method ARIMA (p, d, qR) Retailer (Downstream Link) Where t is the number of historical terms used in forecasting

  13. Current No information sharing Demand information sharing Downstream Demand Inference New No information sharing (estimation of noise term) Centralised demand information sharing Scenarios

  14. Lead Time Forecast by Manufacturer AR(1)

  15. No Information Sharing Take

  16. Demand Information Sharing

  17. Downstream Demand Inference

  18. Uncertainty Principle I If the upstream member can identify the demand model at the downstream link, the demand value at the downstream link cannot be exactly calculated.

  19. Principle I(applies when p+d<qM) ARIMA (p, d, qM) ARIMA (1, 0, 2) L=1 qM = max {p+d, qR-L} qM = qR-L = qR-1 ARIMA (p, d, qR) ARIMA (1, 0, 3)

  20. Uncertainty Principles Principle II: “If the upstream member cannot identify the demand model at the downstream link, then the demand value at the downstream link can be exactly calculated, if a certain model is assumed from a restrictive subset of the possible models.”

  21. Principle II (applies when p+d=qM) ARIMA (p, d, qM) ARIMA (p, d, 0) ARIMA (p, d, 1) ARIMA (p, d, qM) ARIMA (p, d, qM+1) ARIMA (p, d, qM+L) … …

  22. New Scenario : No Information Sharing – estimation of noise • There are two estimation methods for the above • Recursive Estimation Method • Forecast Error Method

  23. New Scenario: Centralised Demand Information Sharing

  24. Scenarios in our Research Current Literature New Scenarios Introduced

  25. Summary of Research • Downstream Demand Inference shown to be infeasible • No Information Scenario improved to include estimation of ‘noise’ term • Demand Information Sharing scenario enhanced by basing estimation on demand at retailer

  26. Further Research • Issue of batching • Evaluation of multi-node supply chains • Inventory rules other than OUT • Challenging the nature of the rules

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