A flexible agent system for change adaptation in supply chains

Ahn, Hyung Jun; Lee, Habin; Park, Sung Joo

doi:10.1016/s0957-4174(03)00100-3

Cited by 48 publications

(21 citation statements)

References 21 publications

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“…Chang and Harrington (2000) modelled a retail chain as a multi-agent adaptive system to study the effects of centralisation versus decentralisation on innovations. Ahn et al (2003) proposed a flexible agent system, which is adaptable to the dynamic changes of transactions in the supply chains.…”

Section: Methodsmentioning

confidence: 99%

Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

Datta

Christopher

2011

International Journal of Production Research

142

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Section: Methodsmentioning

confidence: 99%

Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

Datta

Christopher

2011

International Journal of Production Research

142

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“…From an operational point of view, this pipeline works like a process of activities that are distributed. Therefore, supply chain is not only a linear structure but actually a network [1,2]. In recent years, SC network management has become an important management approach to create closer enterprise collaborations [3,4,5,6], and many companies have invested significant resources in developing information systems to obtain successful SC network management implementation.…”

Section: Research Motivationmentioning

confidence: 99%

A Fuzzy Decision Making Trial and Evaluation Laboratory Analysis of SCM System Implementation

Lu¹,

Rau²,

Liou³

et al. 2014

Appl. Math. Inf. Sci.

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This study has two phases. The first phase constructs a comprehensive model of key success factors (KSFs) for supply chain network management implementation in the semiconductor industry based on both a literature review and an expert review. The model has four dimensions: strategy, process, organization, and technology (SPOT), and 15 KSFs. The SPOT model can be a checklist for future projects. The second phase analyzed the complicated interrelationship among KSFs with Fuzzy Decision Making Trial and Evaluation Laboratory and discussed its managerial implications. Lastly, we provide two case studies to confirm the importance of the two most important factors.

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“…Each agent within the chain has the responsibility of a specific task, but the organization as a whole accomplishes the objective of an optimal integrated supply chain management (Ahn et al 2003).…”

Section: Agent-based Mass Customizationmentioning

confidence: 99%

Supporting mass customization with agent-based coordination

Sugumaran

Dietrich

Kirn

2005

ISeB

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Mass customization (MC) often implies a value chain with several spatially distributed actors that require extensive coordination. Agent technology is beginning to be utilized in supply chain management because of its ability to facilitate collaboration and coordination. Thus, agents are suitable for constructing MC environments and interfacing the various participants working together to archive the common goal of producing customized goods. This paper presents a multi-agent system architecture for MC, which takes into account the coordination problems that exist in MC. Because of asymmetric and incomplete knowledge bases, agents may be limited in global planning and scheduling. Therefore, we suggest a three-tier model of coordination and utilize partial global plans for addressing the coordination problem. Finally, we provide an example application and a prototype implementation for the MC coordination.

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A flexible agent system for change adaptation in supply chains

Cited by 48 publications

References 21 publications

Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

A Fuzzy Decision Making Trial and Evaluation Laboratory Analysis of SCM System Implementation

Supporting mass customization with agent-based coordination

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