Requirement-driven evolution and strategy-enabled service design for new customized quick-response product order fulfillment process

Ching-Hung Lee, Li Li, Fan Li and Chun-Hsien Chen

Technological Forecasting and Social Change, 2022, vol. 176, issue C

Abstract: 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.

Keywords: Customized order fulfillment; Customer requirements; Require-driven and strategy- enabled design; Kano model; TRIZ evolution trend (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008994

DOI: 10.1016/j.techfore.2021.121464

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