TY - CHAP
T1 - Complexity Management in Engineer-To-Order Industry: A Design-Time Estimation Model for Engineering Processes
AU - Brabrand, Christian Victor
AU - Shafiee, Sara
AU - Hvam, Lars
PY - 2022
Y1 - 2022
N2 - The engineer-to-order (ETO) industry’s business environment constantly changes, which results in challenges related to project management, on-time delivery, quality, and market competition. Companies face pressure to optimize production while demand for personalized products, and accordingly the complexity level increases. To address these challenges, companies require to identify the most important complexity drivers to improve planning, get a better overview of the resource allocation, and improve internal processes. This study proposes a design-time estimation model based on the most important complexity drivers: 1) Functional requirement, 2) Number of technologies, 3) Level of connectivity, 4) Regulation and standards. This study presents key complexity drivers for assessing the expected hours to design a product in an ETO industry. Complexity drivers are explored qualitatively and quantitatively from (i) literature review; (ii) internal regular meetings and; (iii) data analysis. The gathered complexity drivers are weighted and combined in order to develop the mathematical design-time model. Finally, an IT-tool is prototyped to test the mathematical model at the case company. The application of the developed IT-tool is also tested at the case company to prove the usability
AB - The engineer-to-order (ETO) industry’s business environment constantly changes, which results in challenges related to project management, on-time delivery, quality, and market competition. Companies face pressure to optimize production while demand for personalized products, and accordingly the complexity level increases. To address these challenges, companies require to identify the most important complexity drivers to improve planning, get a better overview of the resource allocation, and improve internal processes. This study proposes a design-time estimation model based on the most important complexity drivers: 1) Functional requirement, 2) Number of technologies, 3) Level of connectivity, 4) Regulation and standards. This study presents key complexity drivers for assessing the expected hours to design a product in an ETO industry. Complexity drivers are explored qualitatively and quantitatively from (i) literature review; (ii) internal regular meetings and; (iii) data analysis. The gathered complexity drivers are weighted and combined in order to develop the mathematical design-time model. Finally, an IT-tool is prototyped to test the mathematical model at the case company. The application of the developed IT-tool is also tested at the case company to prove the usability
KW - Healthcare
KW - Lean
KW - Patient satisfaction
KW - Value stream mapping
KW - Customized health care
U2 - 10.1007/978-3-030-90700-6_72
DO - 10.1007/978-3-030-90700-6_72
M3 - Book chapter
SN - 978-3-030-90699-3
T3 - Lecture Notes in Mechanical Engineering
SP - 636
EP - 644
BT - Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. CARV 2021, MCPC 2021
A2 - Andersen, A. -L.
PB - Springer
CY - Cham
T2 - 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and 10th World Mass Customization & Personalization Conference (MCPC2021)
Y2 - 1 November 2021 through 2 November 2021
ER -