Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25083
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dc.contributor.authorPsarommatis, F-
dc.contributor.authorDanishvar, M-
dc.contributor.authorMousavi, A-
dc.contributor.authorKiritsis, D-
dc.date.accessioned2022-08-15T15:46:43Z-
dc.date.available2022-08-15T15:46:43Z-
dc.date.issued2022-01-07-
dc.identifierORCID iD: Foivos Psarommatis https://orcid.org/0000-0002-2731-8727-
dc.identifierORCID iD: Morad Danishvar https://orcid.org/0000-0002-7939-9098-
dc.identifierORCID iD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712-
dc.identifierORCID iD: Dimitris Kiritsis https://orcid.org/0000-0003-3660-9187-
dc.identifier.citationPsarommatis, F. et al. (2024) 'Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing', IEEE Transactions on Engineering Management, 71, pp. 702 - 714. doi: 10.1109/TEM.2021.3133619.en_US
dc.identifier.issn0018-9391-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/25083-
dc.description.abstract© Copyright The Authors 2022. An attempt is made to translate five generic key performance indicators (KPIs) into a continuous real-time cost function in a batch order-based manufacturing environment. The challenge of controlling and optimizing resource utilization, production efficiency, product-process quality, environmental impact, and inventory was specified by microelectronics and hard metal composite manufacturers. The motivation is to facilitate decision-making by converting operations management data into dynamic financial cost models. The process of interpreting engineering data of the physical level and operations management level into financial metrics creates a common language between engineers, managers, and financial departments of the company whose common objective is the profitability of the company, each with their own priorities. The proposed method provides a realistic representation of the performance of the system in monetary value. The integration may become an instrument of effective and efficient tactical and strategic collective decision-making. The main outcome is a near real-time formulation and prediction of manufacturing cost with respect to the five KPIs. The resultant cost function is verified according to several production scenarios. The case study demonstrating the proposed cost modeling methodology utilizes real-time and historical information from two different industrial partners in Tungsten metallurgy and electronic circuit manufacturing industries.en_US
dc.description.sponsorshipZ-Fact0r and Eur3ka; European Unions Horizon 2020 Research and Innovation Programme (Grant Number: 723906 101016175).en_US
dc.format.extent702 - 714-
dc.format.extentPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.rightsCopyright © 2022 The Author(s). Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectcost functionsen_US
dc.subjecthard metalen_US
dc.subjectmicroelectronicsen_US
dc.subjectoptimizationen_US
dc.subjectprocess planningen_US
dc.subjectsustainable manufacturingen_US
dc.subjectzero defect manufacturing (ZDM)en_US
dc.titleCost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TEM.2021.3133619-
dc.relation.isPartOfIEEE Transactions on Engineering Management-
pubs.publication-statusPublished-
pubs.volume71-
dc.identifier.eissn1558-0040-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers
Dept of Civil and Environmental Engineering Research Papers

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