Supply chain simulation: generic models and application examples

Umeda, Shigeki; Zhang, F.

doi:10.1080/09537280500224028

Cited by 45 publications

(19 citation statements)

References 19 publications

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“…By modelling a supply chain network with agent-based approach, Gjerdrum et al (2001) simulated how the agent system could determine the inventory control policy in order to reduce the operating cost of the system while a high level of customer order fulfilment could be maintained. In another study, Umeda and Zhang (2006) developed an agent-based simulation model to study three different operation models (reorder-point, centralised, and pull). A negotiation (or coordination) protocol was developed in finding next order volumes between operational managers and parts suppliers of the distributed supply chain.…”

Section: Multi-agent Simulationmentioning

confidence: 99%

A review of coordination studies in the context of supply chain dynamics

Chan

2009

International Journal of Production Research

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Section: Multi-agent Simulationmentioning

confidence: 99%

A review of coordination studies in the context of supply chain dynamics

Chan

2009

International Journal of Production Research

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“…In general, it is possible to affirm that the coordination that leads to a centralized ordering system and vendor re-order point system in pull operational systems has a positive impact on supply performance (Umeda & Zhang, 2006). Coordination provides the possibility of managing and reducing the stochasticity of the process, which influences storage capacity between the echelons in the SC.…”

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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“…In this case, the analyst chooses proper models in library, and customizes them as the need arises [3] [4].…”

Section: Introductionmentioning

confidence: 99%