In this article I use the lens of Bombay/Mumbai's taxi trade from the early twentieth century to the present to examine how obsolescence becomes ingrained in the political and public imagination, and how this can illuminate the contradictory experiences of time in changing cities. Based on a labor‐centric approach to transport infrastructure, I ask what happens when particular technologies and linear understandings of progress rooted in obsolescence pass over or attempt to erase certain urban subjects. How are these temporal contradictions manifested, and what kinds of political and value claims emerge as a result? Further, how do people actively produce other time‐spaces using alternative temporal claims to challenge progress‐oriented development narratives? How, in other words, does obsolescence resist becoming obsolete? Finally, I examine the role that materiality and physical objects such as cars play in mediating these temporal relationships.
With rapid innovation in the electronics industry, product obsolescence forecasting has become increasingly important. More accurate obsolescence forecasting would have cost reduction effects in product design and part procurement over a product’s lifetime. Currently many obsolescence forecasting methods require manual input or perform market analysis on a part by part basis; practices that are not feasible for large bill of materials. In response, this paper introduces an obsolescence forecasting framework that is capable of being scaled to meet industry needs while remaining highly accurate. The framework utilizes machine learning to classify parts as active, in production, or obsolete and discontinued. This classification and labeling of parts can be useful in the design stage in part selection and during inventory management with evaluating the chance that suppliers might stop production. A case study utilizing the proposed framework is presented to demonstrate and validate the improved accuracy of obsolescence risk forecasting. As shown, the framework correctly identified active and obsolete products with an accuracy as high as 98.3%.
more » « less- Award ID(s):
- 1650527
- NSF-PAR ID:
- 10137108
- Date Published:
- Journal Name:
- ASME 2016 International Manufacturing Science and Engineering Conference MSEC2016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life‐cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can be achieved in predicting time to failure, thus yielding more accurate field‐failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures. Copyright © 2016 John Wiley & Sons, Ltd.
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Tolerancing began with the notion of limits imposed on the dimensions of realized parts both to maintain functional geometric dimensionality and to enable cost-effective part fabrication and inspection. Increasingly however, component fabrication depends on more than part geometry as many parts are fabricated as a result of a "recipe" rather than dimensional instructions for material addition or removal. Referred to as process tolerancing, this is the case, for example, with IC chips. In the case of tolerance optimization, a typical objective is cost minimization while achieving required functionality or "quality." This paper takes a different look at tolerances, suggesting that rather than ensuring merely that parts achieve a desired functionality at minimum cost, the underlying goal of product design is to make money, more is better and tolerances comprise additional design variables amenable to optimization in a decision theoretic framework. We further recognize that tolerances introduce additional product attributes that relate to product characteristics such as consistency, quality, reliability and durability. These important attributes complicate the computation of the expected utility of candidate designs, requiring additional computational steps for their determination. The resulting theory of tolerancing illuminates the assumptions and limitations inherent to Taguchi's loss function. We illustrate the theory using the example of tolerancing for an apple pie, which conveniently demands consideration of tolerances on both quantities and processes, and the interaction among these tolerances.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2016-8625
