The impact and pervasiveness of diminishing manufacturing sources and material shortages (DMSMS) obsolescence are increasing due to rapidly advancing technologies which shorten the procurement lives of high-tech parts. For long field-life systems, this has led to an increasing disparity in the life cycle of parts as compared to the life cycle of the overall system. This disparity is challenging since obsolescence dates of parts are important to product life cycle planning. While proposed obsolescence forecasting methods have demonstrated some effectiveness, obsolescence management is a continuing challenge since current methods are very difficult to integrate with other tools and lack clear, complete, and consistent information representation. This paper presents an ontology framework to support the needs of knowledge representation for obsolescence forecasting. The formalized obsolescence forecasting method is suitable for products with a life cycle that can be represented with a Gaussian distribution. Classical product life cycle models can be represented using the logic of ontological constructs. The forecasted life cycle curve and zone of obsolescence are obtained by fitting sales data with the Gaussian distribution. Obsolescence is forecasted by executing semantic queries. The knowledge representation for obsolescence forecasting is realized using web ontology language (OWL) and semantic web rule language (SWRL) in the ontology editor Protégé-OWL. A flash memory example is included to demonstrate the obsolescence forecasting procedure. Discussion of future work is included with a focus on extending the ontology beyond the initial representation for obsolescence forecasting to a comprehensive knowledge representation scheme and management system that can facilitate information sharing and collaboration for obsolescence management.
Ontology-Based Knowledge Representation for Obsolescence Forecasting
Contributed by the Application Track Committee of ASME for publication in the Journal of Computing and Information Science in Engineering. Manuscript received February 18, 2011; final manuscript received October 29, 2012; published online December 19, 2012. Assoc. Editor: Bahram Ravani.
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Zheng, L., Nelson, R., Terpenny, J., and Sandborn, P. (December 19, 2012). "Ontology-Based Knowledge Representation for Obsolescence Forecasting." ASME. J. Comput. Inf. Sci. Eng. March 2013; 13(1): 014501. https://doi.org/10.1115/1.4023003
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