A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management
A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management
Lean supply chain management is a relatively new concept resulting from the integration of lean philosophy into supply chain management. Decision making in a lean supply chain context is challenging because of the complexity, dynamics, and uncertainty inherent to both supply networks and the types of waste (defined as any processes, including use of resources, which do not add value to customers). Efficient knowledge management has been identified as one of the key requirements to achieve integrated support for lean supply chain decisions. This paper proposes a decision-focused knowledge framework including a multi-layer knowledge model (to capture the know-why and know-with together with the know-what and know-how), a knowledge matrix for knowledge elicitation, and a decision tree for the design of the knowledge base. A knowledge system for lean supply chain management (KSLSCM) has been developed using artificial intelligence system shells VisiRule and Flex. The KSLSCM has five core components: a supply chain decision network manager, a waste elimination knowledge base, a knowledge refinement module, an inference engine, and a decision justifier. The knowledge framework and the KSLSCM have been evaluated through an industrial decision case. It has been demonstrated through the KSLSCM that the decision-focused knowledge framework can provide efficient and effective support for collaborative decision making in supply chain waste elimination.
2123-2137
Liu, Shaofeng
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Leat, Mike
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Moizer, Jonathan
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Megicks, Phil
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Kasturiratne, Dulekha
ead76ff3-9226-49e0-beb5-65d1834d3387
1 April 2013
Liu, Shaofeng
9e435733-c5c6-49f8-b2b8-d0b44c24fc96
Leat, Mike
d6f877e6-6ad5-4884-ac9a-da70a985b876
Moizer, Jonathan
b35b09ee-5237-461e-98cb-eecfe1ebc4f7
Megicks, Phil
5330ca01-abb8-4e41-b079-07c1e7656336
Kasturiratne, Dulekha
ead76ff3-9226-49e0-beb5-65d1834d3387
Liu, Shaofeng, Leat, Mike, Moizer, Jonathan, Megicks, Phil and Kasturiratne, Dulekha
(2013)
A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management.
International Journal of Production Research, 51 (7), .
(doi:10.1080/00207543.2012.709646).
Abstract
Lean supply chain management is a relatively new concept resulting from the integration of lean philosophy into supply chain management. Decision making in a lean supply chain context is challenging because of the complexity, dynamics, and uncertainty inherent to both supply networks and the types of waste (defined as any processes, including use of resources, which do not add value to customers). Efficient knowledge management has been identified as one of the key requirements to achieve integrated support for lean supply chain decisions. This paper proposes a decision-focused knowledge framework including a multi-layer knowledge model (to capture the know-why and know-with together with the know-what and know-how), a knowledge matrix for knowledge elicitation, and a decision tree for the design of the knowledge base. A knowledge system for lean supply chain management (KSLSCM) has been developed using artificial intelligence system shells VisiRule and Flex. The KSLSCM has five core components: a supply chain decision network manager, a waste elimination knowledge base, a knowledge refinement module, an inference engine, and a decision justifier. The knowledge framework and the KSLSCM have been evaluated through an industrial decision case. It has been demonstrated through the KSLSCM that the decision-focused knowledge framework can provide efficient and effective support for collaborative decision making in supply chain waste elimination.
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More information
Accepted/In Press date: 3 July 2012
e-pub ahead of print date: 23 August 2012
Published date: 1 April 2013
Identifiers
Local EPrints ID: 453858
URI: http://eprints.soton.ac.uk/id/eprint/453858
ISSN: 0020-7543
PURE UUID: d1663471-81b7-4ece-bb2e-560b29903698
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Date deposited: 25 Jan 2022 17:40
Last modified: 17 Mar 2024 03:58
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Author:
Shaofeng Liu
Author:
Mike Leat
Author:
Jonathan Moizer
Author:
Dulekha Kasturiratne
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