Transfer function analysis of forecasting induced bullwhip in supply chains

Dejonckheere, Jeroen; Disney, Stephen Michael; Lambrecht, Marc; Towill, Denis Royston

doi:10.1016/s0925-5273(01)00084-6

Cited by 127 publications

(70 citation statements)

References 13 publications

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“…The results are plotted in the figures 9a and 9b. Dejonckheere et al (2001) for more details on tracking abilities of common forecasting algorithms). From figure 9 we can see that in those cases, the buIlwhip effect wiII certainly be present.…”

Section: Insight 4: Exponential Smoothing Based Order-up-policies Appmentioning

confidence: 99%

“…The control engineering approach goes back to the work of Simon (1952), Vassian (1955), Adelson (1966), Forrester (1961) and Towill (1970Towill ( , 1982. We also refer to Deziel andEilon (1967), Bertrand (1986), Towill (1999), Dejonckheere, Disney, Lambrecht and Towill (2001) and Disney (2000) for other contributions on replenishment rules and inventory fluctuations. Finally we refer to Sterman (2000) for a good overview of the systems thinking approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring and avoiding the bullwhip effect: A control theoretic approach

Dejonckheere¹,

Disney

Lambrecht

et al. 2003

European Journal of Operational Research

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573

388

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Abstract(2) Logistics Systems Dynamics Group, Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff -UK, CFIO 3ED. Wales, UK DisneySM@Cardiff.ac.uk An important contributory factor to the bullwhip effect (i.e. the variance amplification of order quantities observed in supply chains) is the replenishment rule implemented by supply chain members. We analyse the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies. We not only quantifY the variance amplification, but we prove that the bullwhip effect is guaranteed irrespective of the forecasting method used. Avoiding the bullwhip effect consequently means avoiding the order-up-to policies. In a second part of the paper we introduce a general decision rule that avoids variance amplification and succeeds in generating smooth ordering patterns, even when demand has to be forecasted. The methodology is based on control systems engineering and allows us to gain important insights in the dynamic behaviour of replenishment rules.

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Section: Insight 4: Exponential Smoothing Based Order-up-policies Appmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring and avoiding the bullwhip effect: A control theoretic approach

Dejonckheere¹,

Disney

Lambrecht

et al. 2003

European Journal of Operational Research

Self Cite

573

388

View full text Add to dashboard Cite

show abstract

“…This enables problems to be tackled via such analysis techniques which could not possibly have been contemplated a few decades ago. Dejonckheere et al (2002), and Disney et al (2008) are good examples of this approach.…”

Section: Exploring the Step Responsementioning

confidence: 99%

“…Quality of forecasting is seen as an important factor in its control, as identified by Lambrecht and Dejonckheere (1999) and Chen et al (2000). Subsequently in a paper by Dejonckheere et al (2002) an analytical assessment of the impact of forecasting was established via transfer function analysis. The present contribution continues that approach.…”

Section: Introductionmentioning

confidence: 99%

A pragmatic approach to the design of bullwhip controllers

Zhou¹,

Disney

Towill

2010

International Journal of Production Economics

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It is well known that forecasting mechanisms can greatly increase "bullwhip" -demand variance amplification of orders as processed by both human and algorithmic decision makers. This paper is concerned with the application of the well established APIOBPCS Decision Support System (a variant of the Order-Up-To Rule) in such circumstances. It has two feedback controls (based on the inventory and the ordersin-pipeline respectively) with gains set equal according to the Deziel-Eilon Rule.There is one feed-forward control based on exponential forecasting, although this is not a restriction on the application of this system.We consider the pragmatic role of APIOBPCS in the situation where the echelon decision maker may be handling a wide range of SKU's in a non-altruistic environment where upmarket information may either be withheld or simply unavailable. Under such circumstances it has been established via site-based studies that the decision makers output (the orders) reflect a wide range of strategies (or maybe ignorance). Three strategies may be regarded as "appropriate", i.e. Passorders-Along; Demand Smoothing; and Level Scheduling depending, on context. APIOBPCS can be adapted to each of these modus operandi. In the first case with the added capability of smoothing the "sharp edges" with a modicum of inventory variation, and in the last case with the advantage of built-in trend detection."Players" in non-altruistic supply chains must be able to cope with added uncertainties due to lead-time variations. We show that APIOBPCS may be well matched to such situations and is hence "copable" as well as "capable". The paper includes recommended parameter settings according to desired decision-making policies.

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“…Based on the pilot experiments by Venkateswaran and Son (2004), it was seen that exponential smoothing exhibits the least oscillations and has the desirable property of low mean absolute deviation when compared to the moving average method. Also, Dejonckheere et al (2002) showed that simple exponential smoothing results in the lowest ordering costs.…”

Section: Demand Forecastingmentioning

confidence: 99%