TY - JOUR
T1 - Requirement-driven evolution and strategy-enabled service design for new customized quick-response product order fulfillment process
AU - Lee, Ching Hung
AU - Li, Li
AU - Li, Fan
AU - Chen, Chun Hsien
N1 - Funding Information:
This research was partially supported by the Xi'an Jiaotong University [grant number: 7121192301 ] and National Natural Science Foundation of China [grant number 72174168 ]. We appreciate the support from the Human factor and design lab of MAE at Nanyang Technological University. With NTU's help, the system demo video could be watched to understand our purpose about this. (https://www.youtube.com/watch?v = H9S25m64IDM).
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2022/3
Y1 - 2022/3
N2 - Under the digital transformation era, technologies such as Cyber-physical systems, the Internet of things, and Artificial Intelligence are increasingly mature, making it possible to transform from traditional factories to smart factories. During the transformation, building a communication channel between customer requirements and production capacity to realize customized order services with low volume and high-mix production is critical. This study proposes a novel requirement-driven and strategy-based model to achieve the quick response order placement and production configuration services through three phases, that is, (1) requirement-based service diagnosis, (2) design strategy generation, and (3) service system conceptualization and evaluation. Firstly, a statistical kano analysis method was proposed to mining customer requirements considering industry contexts. Then, TRIZ evolution trends were modified to design concepts for digital transformation based on key enterprise processes. Finally, a novel service development maturity model was constructed to evaluate the new digital system design. A comprehensive empirical case study of designing “Customized Product Order Fulfillment System” for the laptop production process is conducted to demonstrate this approach. The proposed novel requirement-driven and strategy-based model is expected to provide valuable insights for suggestions on technological trends and forecasting, future diverse and innovative applications in customized order fulfillment scenarios.
AB - Under the digital transformation era, technologies such as Cyber-physical systems, the Internet of things, and Artificial Intelligence are increasingly mature, making it possible to transform from traditional factories to smart factories. During the transformation, building a communication channel between customer requirements and production capacity to realize customized order services with low volume and high-mix production is critical. This study proposes a novel requirement-driven and strategy-based model to achieve the quick response order placement and production configuration services through three phases, that is, (1) requirement-based service diagnosis, (2) design strategy generation, and (3) service system conceptualization and evaluation. Firstly, a statistical kano analysis method was proposed to mining customer requirements considering industry contexts. Then, TRIZ evolution trends were modified to design concepts for digital transformation based on key enterprise processes. Finally, a novel service development maturity model was constructed to evaluate the new digital system design. A comprehensive empirical case study of designing “Customized Product Order Fulfillment System” for the laptop production process is conducted to demonstrate this approach. The proposed novel requirement-driven and strategy-based model is expected to provide valuable insights for suggestions on technological trends and forecasting, future diverse and innovative applications in customized order fulfillment scenarios.
KW - Customer requirements
KW - Customized order fulfillment
KW - Kano model
KW - Require-driven and strategy- enabled design
KW - TRIZ evolution trend
UR - http://www.scopus.com/inward/record.url?scp=85122575678&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2021.121464
DO - 10.1016/j.techfore.2021.121464
M3 - Journal article
AN - SCOPUS:85122575678
SN - 0040-1625
VL - 176
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 121464
ER -