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Sensor Based Cost Modelling for a Knowledge Support System Development

Tokucoglu, H, Chen, X, El Rhalibi, A and Opoz, TT Sensor Based Cost Modelling for a Knowledge Support System Development. In: Xplore, IEEE International conference on automation and computing . (IEEE International conference on automation and computing, 5-7th September 2019, Lancaster, UK). (Accepted)

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Abstract

Nowadays, many small or medium size manufacturing companies face significant challenges of quality, cost and cycle time in their production life cycle. In order to deal with these challenges, the utilization of knowledge management system in their facilities becomes an appealing solution. However, most their current knowledge management system is not flexible enough and adequate for handling high amount of production data or calculating manufacturing cost of a product adaptively. Therefore, a novel knowledge support system framework for calculating unit product manufacturing cost through a generic cost model becomes necessary for small or medium size companies (SMC) to effectively optimise a manufacturing system in order to produce, repair or remanufacture products with the highest efficiency, best quality performance and lowest cost. This paper presents a generic cost model that considers production time based on sensors in a manufacturing system. This means the basic elements of model would adapt cycle time variation, which is one of the most important data of the generic cost models that will be obtained from the sensors on the machines.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: T Technology > T Technology (General)
Divisions: Engineering & Technology Research Institute
Date Deposited: 11 Sep 2019 10:01
Last Modified: 03 Oct 2019 11:26
URI: http://researchonline.ljmu.ac.uk/id/eprint/11320

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