‘Bullwhip’ and ‘backlash’ in supply pipelines

Shukla, Vinaya; Naim, Mohamed Mohamed; Yaseen, Ehab A.

doi:10.1080/00207540802270096

Cited by 34 publications

(35 citation statements)

References 45 publications

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“…Simulation, specifically control engineering based system dynamics simulation is therefore considered, with a well known four echelon system used in several previous supply chain dynamics studies incl. Sterman (1989) and Shukla et al (2009) being simulated. Although some work on capacity constrained systems using system dynamics modelling has been done previously, the nature of investigation in those studies is different to what is done here.…”

Section: Problem Statement and Capacity Constraintsmentioning

confidence: 99%

Detecting disturbances in supply chains: the case of capacity constraints

Shukla

Naim

2017

IJLM

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Purpose -The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. This paper is aimed at demonstrating the feasibility of automatically, and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.Design/Methodology/approach -Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature based technique.Findings -The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra.Originality/value-Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution.

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Section: Problem Statement and Capacity Constraintsmentioning

confidence: 99%

Detecting disturbances in supply chains: the case of capacity constraints

Shukla

Naim

2017

IJLM

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“…• bullwhip effect in service supply chains Akkermans and Voss (2013) • shipment planning (Shukla et al, 2009;Mula et al, 2013)a n d…”

Section: Nonlinear System Dynamicsmentioning

confidence: 99%

“…(Jeong et al, 2000) Recommends but does not apply for supply chain design (Saleh et al, 2010) Testing supply chain re-engineering strategies (Wikner et al, 1992) Identifying analogies between seemingly different decision rules (Naim et al, 2012) (Sterman, 1989;Syntetos et al, 2011;Bruccoleri et al, 2014). Shaping stability regions of nonlinear systems (Larsen et al, 1999;Laugesen and Mosekilde, 2006) Robust control of supply chain decision rules (Jeong et al, 2000) Bullwhip and backlash - (Shukla et al, 2009;Akkermans and Voss, 2013;Mula et al, 2013) Effect of capacity and batching (Paik and Bagchi, 2007;Cannella et al, 2008;Hamdouch, 2011), inaccuracies of inventory (Cannella et al, 2015), reverse logistics (Turrisi et al, 2013) Improve nonlinear systems performance (Spiegler and Naim, 2014) Application for crosschecking results obtained via analytical methods…”

Section: Nonlinear System Dynamicsmentioning

confidence: 99%

“…Hence, much of the research on supply chain dynamics either takes a 'trial and error', experimental, simulation approach to redesign (Forrester, 1961;Sterman, 1989;Shukla et al, 2009;Poles, 2013;Spiegler and Naim, 2014) or develops exact solutions of models that are already linearised approximations to the realworld situation (Towill, 1982;John et al, 1994;Disney and Towill, 2005;Gaalman and Disney, 2009;Zhou et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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A technique to develop simplified and linearised models of complex dynamic supply chain systems

Spiegler

Naim

Towill

et al. 2016

European Journal of Operational Research

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Please note: Changes made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher's version if you wish to cite this paper.This version is being made available in accordance with publisher policies. See http://orca.cf.ac.uk/policies.html for usage policies. Copyright and moral rights for publications made available in ORCA are retained by the copyright holders.At e c h n i q u et od e v e l o ps i m p l i fi e da n dl i n e a r i s e dm od e l so fc o m p l e x dynamic supply chain systems AbstractThere is a need to identify and categorise different types of nonlinearities that commonly appear in supply chain dynamics models, as well as establishing suitable methods for linearising and analysing each type of nonlinearity. In this paper simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested. Hence, an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models.We use the highly cited Forrester production-distribution model as a benchmark supply chain system to study nonlinear control structures and apply appropriate analytical control theory methods. We then compare performances of the linearised model with numerical solutions of the original nonlinear model and with other previous research on the same model.Findings suggest that more accurate linear approximations can be found. These simplified and linearised models enhance the understanding of the system dynamics and transient responses, especially for inventory and shipment responses.A systematic method is provided for the rigorous analysis and design of nonlinear supply chain dynamics models, especially when overly simplistic linear relationship assumptions are not possible or appropriate. This is a precursor to robust control system optimisation.

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“…The main goal of this business game is to show the existence and the characteristics of the "bullwhip effect" 4,5 . This phenomenon represents the propagation and amplification of orders as they pass upstream in a supply chain pipeline.…”

Section: Introductionmentioning

confidence: 99%

Decision Making in Supply Chains and Value Networks: The Beer Game Evolution

Spagnoletti

D'Atri²,

D’Atri

2013

ijecs

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New trends in inter-organization configurations are challenging the traditional concept of supply chain. Concepts such as Virtual Enterprise were introduced to describe scenarios in which manufacturers operate as nodes within a network of suppliers, customers, engineers, and other specialized service functions. Our aim is to build a simulation tool based on the rules of the Beer Game that includes the variables of a virtual enterprise, VirtuE in particular, and risk management in order to understand the strategies underlying the subject's behavior in the face of risk within a means-end chain. The simulation tool will contribute to understanding the complexity of managing decision making in supply chains and networks. This study presents the tests carried out on the original game, the new variables introduced, and the simulation results b . Keywords

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‘Bullwhip’ and ‘backlash’ in supply pipelines

Cited by 34 publications

References 45 publications

Detecting disturbances in supply chains: the case of capacity constraints

Detecting disturbances in supply chains: the case of capacity constraints

A technique to develop simplified and linearised models of complex dynamic supply chain systems

Decision Making in Supply Chains and Value Networks: The Beer Game Evolution

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