Information Sharing as a Coordination Mechanism for Reducing the Bullwhip Effect in a Supply Chain

In this study we examine the relationship between supply network's topology and its robustness in the presence of random failures and targeted attacks. The agent based model developed in this paper uses the basic framework and parameters in the experimental game presented in Sterman (1989) for modeling adaptive managerial decision making in an inventory management context. The study extends the linear supply chain context to a complex supply network and undertakes a rigorous examination of robustness of these supply networks that are characterized by distinct network characteristics. We theorize that network characteristics such as average path length, clustering coefficient, size of the largest connected component in the network and the maximum distance between nodes in the largest connected component are related to the robustness of supply networks, and test the research hypotheses using data from several simulation runs. Simulations were carried out using twenty distinct network topologies where ten of these topologies were generated using preferential attachment approach (based on the theory of scalefree networks) and the remaining ten topologies were generated using random attachment approach (using random graph theory as a foundation). These twenty supply networks were subjected to random demand and their performances were evaluated by considering varying probabilities of random failures of nodes and targeted attacks on nodes. We also consider the severity of these disruptions by considering the downtime of the affected nodes. Using the data collected from a series of simulation experiments, we test the research hypotheses by means of binomial logistic regression analysis. The results point towards a significant association between network characteristics and supply network robustness assessed using multiple performance measures. We discuss the implications of the study and present directions for future research.

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“…Moyaux, et al, 2007). In this study we develop multi-agent model using the NetLogo modeling platform (Wilensky, 1999).…”

Section: Methodsmentioning

confidence: 99%

Supply network topology and robustness against disruptions – an investigation using multi-agent model

Nair

Vidal

2011

International Journal of Production Research

214

146

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“…In the general context of SC optimisation, it important to always remember the role of the bullwhip effect (Moyaux et al, 2007) that is influenced by several variables, such as the inventory management, lotsizing, market supply, operations uncertainty and information sharing process.…”

Section: Investigating Scs Trends In Literature Referencesmentioning

confidence: 99%

The role of uncertainty in supply chains under dynamic modeling

Fera¹,

Fruggiero²,

Lambiase³

et al. 2017

10.5267/j.ijiec

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The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client's order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.

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“…Since the 1990s, a large body of literature has documented the roles of information sharing and supply chain coordination to reduce the bullwhip effect (Moyaux et al, 2007;Fiala, 2005;Paik and Bagchi, 2007;Sahin and Robinson, 2002;Kok et al, 2005). More recently, many researchers attempt various approaches to identify causes, measures and remedies of the bullwhip effect (Wang and Disney, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Order Cycle Time on the Bullwhip Effect: A Numerical Study

Li¹,

Choi²

2017

JMPP

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The bullwhip effect phenomenon refers to the amplification of demand variability as movingaway from the end customers to the suppliers in a supply chain. This research is concerned with the impacts of order cycle time on the bullwhip effect by considering its four major causes, i.e., demand forecast, price fluctuation, anticipation of shortages, andorder batching. Considering a two-echelon supply chain, fournumerical examples are developed where a multi-period inventory system with a periodic review policy is used.In each order cycle, a replenishment is initiated to raise the inventory to the order-up-to level, and the variance ratio of the manufacturer order to the market demand is calculated. Our results show that order cycle time reduction could counterattack four major causes of the bullwhip effect at the same time.

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Information Sharing as a Coordination Mechanism for Reducing the Bullwhip Effect in a Supply Chain

Cited by 119 publications

References 20 publications

Supply network topology and robustness against disruptions – an investigation using multi-agent model

Supply network topology and robustness against disruptions – an investigation using multi-agent model

The role of uncertainty in supply chains under dynamic modeling

Impacts of Order Cycle Time on the Bullwhip Effect: A Numerical Study

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