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Concept Analysis to Enrich Manufacturing Service Capability Models



Jun H. Shin, Boonserm Kulvatunyou, Yunsu Lee, Nenad Ivezic


When an Original Equipment Manufacturer (OEM), which makes a final product for the consumer marketplace by purchasing components from its suppliers, faces unexpected supply network failures and market events, models of suppliers’ manufacturing service capabilities can provide information required for efficient recovery of these supply network. Models of manufacturing service capabilities include descriptions of both material processing and manufacturing information processing capabilities of a supplier. Presently, manufacturers and suppliers are challenged in making and streamlining sourcing decisions due to limited, imprecise, or ambiguous semantics associated with these models. This paper identifies issues with existing manufacturing service capability (MSC) models by analyzing several practical use cases found in existing web portals containing supplier capability descriptions. We identify the use of an ontology-based manufacturing service capability model can address the imprecision and ambiguity issues. This paper also proposes an approach based on concept analyses on archetypal data sets as a way to enrich semantics and address the limited semantic issues. Several model extension methods for semantic enrichments are formalized within the approach. We demonstrate the approach on an ontology-based manufacturing service model called manufacturing service description language (MSDL) using data sets including product and service categories, detailed capability descriptions of specific processes, and product-term definitions.
Proceedings Title
Conference on Systems Engineering Research
Conference Dates
March 19-22, 2013
Conference Location
Atlanta, GA


Supply network, Use case analysis, Manufacturing service capability, Ontology, Concept analysis proecdures


Shin, J. , Kulvatunyou, B. , Lee, Y. and Ivezic, N. (2013), Concept Analysis to Enrich Manufacturing Service Capability Models, Conference on Systems Engineering Research, Atlanta, GA, [online], (Accessed April 14, 2024)
Created March 22, 2013, Updated February 19, 2017