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Y. Son, J. Venkateswaran (2007)
Hierarchical supply chain planning architecture for integrated analysis of stability and performanceInt. J. Simul. Process. Model., 3
P. Senge, J. Sterman (1992)
Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the futureEuropean Journal of Operational Research, 59
J. Sterman (1989)
Misperceptions of Feedback in a Dynamic Decision Making Experiment
Stephen Williams (1978)
Computer simulation
Hau Lee, V. Padmanabhan, S. Whang (1997)
Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect"Manag. Sci., 50
Johannes Harl, L. Ritzman (1985)
A heuristic algorithm for capacity sensitive requirements planningJournal of Operations Management, 5
J. Sterman (2002)
System Dynamics: Systems Thinking and Modeling for a Complex World
L. Rabelo, M. Helal, C. Lertpattarapong, R. Moraga, A. Sarmiento (2008)
Using system dynamics, neural nets, and eigenvalues to analyse supply chain behaviour. A case studyInternational Journal of Production Research, 46
S. Minegishi, D. Thiel (2000)
System dynamics modeling and simulation of a particular food supply chainSimul. Pract. Theory, 8
C. Riddalls, S. Bennett, N. Tipi (2000)
Modelling the dynamics of supply chainsInternational Journal of Systems Science, 31
H. Hwarng, N. Xie (2008)
Understanding supply chain dynamics: A chaos perspectiveEur. J. Oper. Res., 184
Narasimha Kamath, R. Roy (2007)
Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics frameworkEur. J. Oper. Res., 179
W. Berry, Thomas Cpim, T. Vollmann (1982)
Capacity planning techniques for manufacturing control systems: Information requirements and operational featuresJournal of Operations Management, 3
M. Erkoc, E. Iakovou, Andre Spaulding (2005)
Multi-stage onboard inventory management policies for food and beverage items in cruise liner operationsJournal of Food Engineering, 70
J. Vorst, A. Beulens, P. Beek (2000)
Modelling and simulating multi-echelon food systemsEur. J. Oper. Res., 122
T. Higuchi, M. Troutt (2004)
Dynamic simulation of the supply chain for a short life cycle product - Lessons from the Tamagotchi caseComput. Oper. Res., 31
Fangruo Chen, Jing-Sheng Song (2001)
Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated DemandOper. Res., 49
D. Towill (1996)
Time compression and supply chain management ‐ a guided tourLogistics Information Management
A. Clark, H. Scarf (1960)
Optimal Policies for a Multi-Echelon Inventory ProblemManag. Sci., 50
P. Georgiadis, D. Vlachos, E. Iakovou (2005)
A system dynamics modeling framework for the strategic supply chain management of food chainsJournal of Food Engineering, 70
J. Forrester (2012)
Industrial Dynamics: A Major Breakthrough for Decision Makers
Purpose – The purpose of this paper is to study the behaviour of a food supply chain possessing two originalities, i.e. a singular structure (40‐day upstream push and 24‐hour downstream pull) and one that suffers from simultaneous fluctuations in raw material supply capacities (due to epizooty) and customer demand (due to customer anxieties and fears) caused by a sanitary crisis. Design/methodology/approach – A simulation model based on the system dynamics principles of Forrester is developed and applied to the French chicken meat supply chain suffering an Avian Influenza crisis. Findings – This model first enables one us to study the regulation mechanisms of the chain that will improve understanding of the supply chain behaviour under environmental perturbations. A what‐if analysis is then implemented to examine the supply chain stability and the influence of flexibility adjustment times, inventory coverage time, slaughtered chicken buffer size and smoothing policies on the supply chain performance in different crisis fluctuation rate scenarios in order to propose necessary logistic policy enhancements. Research limitations/implications – This work will improve one's knowledge about the buffer inventory problem and the global stability of this multi‐echelon push‐pull supply chain. Practical implications – The model can be used as a decision system support which aims to minimise the additional costs due to stock level increases as demand decreases as well as exceptional external purchasing sparked by the lack of available products when there is a sudden hike in demand. The research can help decision‐makers of fresh food push‐pull supply chains when they are facing such crises by using both cybernetic representation and computer simulation. Originality/value – This study deals with a specific food supply chain within the context of a sanitary crisis. A system dynamics model is presented for studying the behaviour of the entire food supply chain threatened by high uncertainties in the supply capacity as well as in customer demand.
British Food Journal – Emerald Publishing
Published: Aug 9, 2011
Keywords: Food supply chain management; Sanitary crisis; Avian influenza; Economic performance; System dynamics; Simulation
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