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][7][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., lastorder or last-ship dates) to achieve more accurate obsolescence forecasting [8]. KeywordsCenter for e-Design, ontology, DMSMS, obsolescence, life cycle, forecast (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 knowledg...
When an original equipment manufacturer no longer supplies and/or supports a product then the product is considered to be obsolete. Obsolescence is a significant problem for systems whose operational and support life is much longer than the procurement lifetimes of their constituent components. Unlike high-volume, commercial products, which are quickly evolved, long field life, low-volume systems, such as aircraft may require updates of their components and technology called design refreshes to simply remain manufacturable and supportable. However these systems can’t perform design refreshes all the time due to the high nonrecurring and re-qualification costs. One approach to optimally managing this problem is to use DRP (Design Refresh Planning), which is a strategic method for scheduling design refreshes such that the life cycle cost impact of obsolescence is minimized. The planning of these design refreshes is restricted by various constraints, which need to be implemented into the DRP process. These constraints can reflect technology roadmap requirements, obsolescence management realities, logistical restrictions, budget ceilings and management policy. In this paper, constraints imposed on the DRP process are explored, classified within a taxonomy, and implemented in the planning process. A communications system design example is included.
Technology obsolescence also known as diminishing manufacturing sources and material shortages (DMSMS) is a significant problem for systems whose operational life is much longer than the procurement lifetimes of their constitute components. The most severely affected systems are sustainment-dominated, which means their long-term sustainment (lifecycle) costs significantly exceed the procurement cost of the system. Unlike high-volume commercial products, these sustainment-dominated systems may require design refreshes to simply remain manufacturable and supportable. Design refresh planning (DRP) is a strategic method for reducing the lifecycle cost impact of DMSMS and increasing system availability. The objective of DRP is to determine when a design refresh should occur (or what the frequency of refreshes should be) and how to manage the system components that are obsolete or soon to be obsolete at the design refreshes. This paper describes the formulation and implementation of constraints in the DRP process for systems impacted by DMSMS type obsolescence and proposes a method of transforming an implicit system limitation into an explicit DRP constraint. These constraints can reflect technology roadmap requirements, obsolescence management realities, logistics limitations, budget limitations and management policy.
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