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Technical Briefs

Ontology-Based Knowledge Representation for Obsolescence Forecasting

[+] Author and Article Information
Liyu Zheng

Department of Industrial and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24061

Raymond Nelson, III, Peter Sandborn

Department of Mechanical Engineering,
University of Maryland,
College Park, MD 20742

Janis Terpenny

Department of Industrial and
Manufacturing Systems Engineering,
Iowa State University,
Ames, IA 50011
e-mail: terpenny@iastate.edu

Sustainment refers to all activities necessary to keep an existing system operational, continue to manufacture and field versions of the system that satisfy the original requirements, or manufacture and field revised versions of the system that satisfy evolving requirements [3].

The sales data is mainly in the form of number of units shipped. If it is not available, sales in market dollars or percentage market share may be used, as long as the total market does not increase appreciably over time [6].

For some products, within the same type of the product, life cycle curves characterized by parameters k, μ, and σ can vary with some primary attributes of the product. Examples are memory chips whose life cycle curves vary with different memory sizes. Memory size is the primary attribute describing the memory chip that evolves over time [6-8]. For these products, if the primary attributes of the product are not considered, the parameters k, μ, and σ obtained from the sales data of the product are only average values for that product.

The time range of the zone of obsolescence can be determined using data mining of historical data (e.g., last-order or last-ship dates) to achieve more accurate obsolescence forecasting [8].

1Corresponding author.

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.

J. Comput. Inf. Sci. Eng 13(1), 014501 (Dec 19, 2012) (8 pages) Paper No: JCISE-11-1309; doi: 10.1115/1.4023003 History: Received February 18, 2011; Revised October 29, 2012

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.

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Figures

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Fig. 5

Product life cycle ontology

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Fig. 4

Description logic for a system class example

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Fig. 3

Framework of obsolescence ontologies

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Fig. 2

Gaussian distribution life cycle curve [1]

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Fig. 1

Product life cycle pattern [18]

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Fig. 6

Description logic for the product life cycle class

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Fig. 7

Flow diagram of obsolescence forecasting for product with Gaussian distribution life cycle

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Fig. 8

SWRL rule for obsolescence forecasting

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Fig. 9

Life cycle curve of the 64 Mbit monolithic flash memory

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Fig. 10

Screenshots of the 64 Mbit monolithic flash memory ontology in the Protégé-OWL

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Fig. 11

Screenshot of the SWRL rule and the result after executing query in the Protégé-OWL

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