Ontology-Based Knowledge Representation for Obsolescence Forecasting

Zheng, Liyu
Terpenny, Janis
Nelson, Raymond
Sandborn, Peter
Terpenny, Janis
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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].

<p>This article is from <em>Journal of Computing and Information Science in Engineering</em> 13 (2012): 014501, doi:<a href="http://dx.doi.org/10.1115/1.4023003" target="_blank">10.1115/1.4023003</a>. Posted with permission.</p>
Center for e-Design, ontology, DMSMS, obsolescence, life cycle, forecast