TY - GEN
T1 - The Complexity of Data-Driven in Engineer-To-Order Enterprise Supply-Chains
AU - Addo-Tenkorang, Richard
AU - Helo, Petri
AU - Sivula, Ari
AU - Gwangwava, Norman
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Bigdata
KW - Complexity
KW - Engineer-to-Order
KW - Original-equipment-manufacturer
KW - Supply-chains
UR - http://www.scopus.com/inward/record.url?scp=85128891079&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90532-3_39
DO - 10.1007/978-3-030-90532-3_39
M3 - Conference contribution
AN - SCOPUS:85128891079
SN - 9783030905316
T3 - Lecture Notes in Networks and Systems
SP - 517
EP - 532
BT - Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering - Progress in Application of Intelligent Methods and Systems in Production Engineering
A2 - Batako, Andre
A2 - Burduk, Anna
A2 - Karyono, Kanisius
A2 - Chen, Xun
A2 - Wyczólkowski, Ryszard
PB - Springer Nature Link
T2 - Global Congress on Manufacturing and Management, GCMM 2021
Y2 - 7 June 2021 through 9 June 2021
ER -