Measuring and avoiding the bullwhip effect: A control theoretic approach

By Jeroen Dejonckheere, Stephen M. Disney, M. Lambrecht, Denis R. Towill. European Journal of Operational Research, 2003

710 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/242063feb29e7750068d64d17b4e128c98763a8c

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In Industrial and Manufacturing Engineering

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Dynamic modeling and control of supply chain systems: A review (2008, background): “studied the AR of the ORATE/CONS transfer function in single stage discrete-time systems [41].”
Application of machine learning techniques for supply chain demand forecasting (2008, background): “As a result, the firm’s demand (e.g., the manufacturer’s demand) appears to fluctuate in a random fashion even if the final customer’s demand has a predictable pattern.”
Supply chain design and cost analysis through simulation (2010, background): “2003 ), and thus order crossover phenomena (Riezebos 2006 ) are not considered in this study.”
Literature review on disruption recovery in the supply chain* (2017, background): “…risks are related to operational risks (Kleindorfer and Saad 2005; Chopra et al. 2007; Acar et al. 2010; Georgiadis et al. 2011; Hora and Klassen 2013; Meisel and Bierwirth 2014) and are frequently considered in the framework of bullwhip-effect (Dejonckheere et al. 2003, Ouyang and Li 2010).”
Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy (2014, methodology): “This focus allows us to sharpen the results of Dejonckheere et al. (2003) for the Naïve and the Simple Exponential Smoothing forecasting techniques and to introduce new results for the Holts and Damped Trend forecasting methods.”
Impact of Information Exchange on Supplier Forecasting Performance (2012, background): “Ideally, volume of shipments received by the retailer should be a delayed version of the retailer orders and such an assumption is usually made in simulation models [23].”
On the bullwhip and inventory variance produced by an ordering policy (2003, background): “[11], for a detailed investigation of the Order-Up-To strategy.”
Exploring the oscillatory dynamics of a forbidden returns inventory system (2014, neutral): “Many factors affecting the Bullwhip Effect have been investigated including: the impact of forecasting methods (Chen et al. , 2000; Dejonckheere et al. , 2003); statistical modelling of demand processes (Aviv, 2003, Gaalman, 2006); cooperative mechanisms such as information sharing (Lee et al. ,…”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, positive): “Forecast errors also contribute to the bullwhip effect, the tendency of orders to increase in variability as one moves up a supply chain (Chandra & Grabis, 2005; Dejonckheere, Disney, Lambrecht, & Towill, 2003, 2004; Zhang, 2004).”
The Bullwhip Effect—Impact of Stochastic Lead Time, Information Quality, and Information Sharing: A Simulation Study (2004, methodology): “Dejonckheere et al. (2003) apply a con-trol theoretic approach to BWE quantiﬁcation and come to similar conclusions.”