The Complexity of Data-Driven in Engineer-To-Order Enterprise Supply-Chains

Conference paper


Addo-Tenkorang, R., Helo, P., Sivula, A. and Gwangwava, N. 2022. The Complexity of Data-Driven in Engineer-To-Order Enterprise Supply-Chains. GCMM 2021: Global Congress on Manufacturing and Management. Liverpool, UK 07 - 09 Jun 2021 Springer. https://doi.org/10.1007/978-3-030-90532-3_39
AuthorsAddo-Tenkorang, R., Helo, P., Sivula, A. and Gwangwava, N.
TypeConference paper
Abstract

The complexity of data-driven engineer-to-order manufacturing enterprise supply-chains for effective and efficient decision making has received a lot of attention both within the original equipment manufacturing industrial research and development circle and supply-chains operations research and management circles. However, despite these complexities, most of the published supply-chains research in operations research and management have neglected the ‘engineer-to-order perspective within the original equipment manufacturing supply-chains sector. This research employs a comprehensive study of complex supply-chains management activities to attempt to propose feasible and measurable essential propositions and/or framework for “best practices” in data-driven engineer-to-order supply-chains. There seems to be no specific comprehensive study on the complexity of data-driven engineer-to-order supply-chains within the original equipment manufacturing sectors for complex products such as the aerospace, marine, and/or power plant industries, etc. However, because this area of complexity of data-driven engineer-to-order within enterprise supply-chains have not been much researched or explored; there is an expected challenge of finding enough available literature to draw-on or makes an inference to. Hence, this study will take solace from mostly real-life industrial case(s) and/or activities, etc. Therefore, this paper presents a comprehensive study of the complexity of data-driven engineer-to-order enterprise supply-chains as well as outlining essential propositions and/or framework to enhance effective and efficient resilient complex engineer-to-order supply-chains. This paper will thus, contribute to the development of a more robust and resilient framework when dealing with the complexity of data-driven engineer-to-order enterprise supply-chains.

Year2022
ConferenceGCMM 2021: Global Congress on Manufacturing and Management
PublisherSpringer
Publication dates
Online20 Apr 2022
Publication process dates
Deposited02 Dec 2022
Journal citationp. 517–532
ISSN2367-3370
Book titleAdvances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering
Book editorBatako, A.
Burduk, A.
Karyono, K.
Chen, X.
Wyczółkowski, R.
ISBN978-3-030-90532-3
978-3-030-90531-6
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-90532-3_39
Copyright holder© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
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