Scenario analysis of demand in a technology market using leading indicators

Meixell, Mary J.; Wu, S. David

doi:10.1109/66.909656

Cited by 47 publications

(29 citation statements)

References 14 publications

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“…Compared to previous methods such as ordinal scale based approaches [4,5] that rely on unquantifiable technological attributes, the primary feature of the method is that it can capture market trends for making obsolescence forecasting. In addition, the method includes follow on steps to realize more accurate obsolescence forecasting, including a data mining based approach [8] that determines the time range of the zone of obsolescence using data mining of historical data and a leading indicator method [9] to further identify a leading indicator product among particular types of product with a certain life cycle pattern. These methods have some increase in accuracy of forecasting, but at the expense of increased complexity.…”

Section: Discussionmentioning

confidence: 99%

“…The obsolescence dates of parts can be obtained by forecasting. There have been several types of methods proposed including traditional methods such as ordinal scale based approaches, in which the life cycle stage of the product is determined from a combination of technological attributes [4,5], methods based on forecasting the product sales curve [6][7][8], and leading indicator methods, in which a leading indicator product can be further identified in each life cycle pattern of products that provides advanced indication of changes in demand trends of products [9]. Results have been mixed in the practical application of these obsolescence forecasting methods, where under certain situations, some of these methods can produce good forecasts and others not.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-Based Knowledge Representation for Obsolescence Forecasting

Zheng

Nelson

Terpenny

et al. 2012

Journal of Computing and Information Science in Engineering

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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][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...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ontology-Based Knowledge Representation for Obsolescence Forecasting

Zheng

Nelson

Terpenny

et al. 2012

Journal of Computing and Information Science in Engineering

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“…Other researchers have considered the expansion of flexible capacity, while the use of different kinds of flexibility and the reconfiguration of dedicated capacity have been studied using stochastic dynamic programming approaches [12,13]. Besides decisions with respect to the volumes and types of capacity, the timings of investment in flexibility, as part of a scenario of capacity expansion, have also been considered in some studies [14,15].…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Loss-Averse Behaviour on Capacity Portfolio Planning for Power Systems

Bao¹,

Jiang²

2015

Int. j. simul. model.

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A decision maker seldom behaves completely rationally when deciding on the investment in capacity portfolio for a power system. Thus his decision will certainly show some bias when compared with a truly risk-neutral one. In order to provide an in-depth analysis of such bias, this paper studies the issues influencing capacity portfolio planning for a power system when the decision maker is lossaverse. The investment decision is the result of the decision maker aiming to maximize utility, and the effect of this is investigated using a modified newsvendor model, in which the probability of abnormal electricity demand is considered. Theoretical proofs and numerical simulations are also presented to provide explanations of some counterintuitive findings. We find that bias in the capacity portfolio occurs whenever underage losses are greater than overage losses. Even where significant flexibility is installed, the loss-averse decision maker values dedicated capacity to a greater extent when building redundancy into the system capacity, and all of his decision bias will go towards building-in dedicated capacity rather than flexibility. The most interesting finding is that the probability and variance of abnormal demand are likely to be overestimated, and this will result in investing more dedicated capacity, not only for providing higher service levels in abnormal situations, but also for delivering greater operational profit.

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“…The uncertain demand is parameterized (Meixell and Wu, 2001) by its deviation from the mean and the correlation between the demands of the two product families. Demands for various scenarios are generated by choosing different levels of deviation from the mean and the correlation between demands of the two product families.…”

Section: Ltmentioning

confidence: 99%

“…We consider positive correlation of demand among the product families which can be caused by the industrial life cycle of the leading indicator product, e.g. a chip set, affecting the demand for the other products (Meixell and Wu, 2001). We also consider negative correlation case on the demand of product families.…”

Section: Introductionmentioning

confidence: 99%

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

Rastogi

Fowler

Carlyle

et al. 2011

International Journal of Production Economics

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a b s t r a c tA semiconductor supply network involves many expensive steps, which have to be executed to serve global markets. The complexity of global capacity planning combined with the large capital expenditures to increase factory capacity makes it important to incorporate optimization methodologies for cost reduction and long-term planning. The typical view of a semiconductor supply network consists of layers for wafer fab, sort, assembly, test and demand centers. We present a two-stage stochastic integer-programming formulation to model a semiconductor supply network. The model makes strategic capacity decisions, (i.e., build factories or outsource) while accounting for the uncertainties in demand for multiple products. We use the model not only to analyze how variability in demand affects the make/buy decisions but also to investigate how the correlation between demands of different products affects these strategic decisions. Finally, we demonstrate the value of incorporating demand uncertainty into a decision-making scheme.

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Scenario analysis of demand in a technology market using leading indicators

Cited by 47 publications

References 14 publications

Ontology-Based Knowledge Representation for Obsolescence Forecasting

Ontology-Based Knowledge Representation for Obsolescence Forecasting

The Effect of Loss-Averse Behaviour on Capacity Portfolio Planning for Power Systems

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

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