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Knowledge-Based Open Performance Measurement System (KBO-PMS) for a Garment Product Development Process in Big Data Environment

Article dans une revue avec comité de lecture
Auteur
HONG, Yan
473499 Soochow University
WU, Tianyu
473499 Soochow University
ZENG, Xianyi
221176 Génie et Matériaux Textiles [GEMTEX]
ccWANG, Yuyang
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
YANG, Wen
473499 Soochow University
PAN, Zhijuan
473499 Soochow University

URI
http://hdl.handle.net/10985/17242
DOI
10.1109/access.2019.2936294
Date
2019
Journal
IEEE Access

Résumé

Globally, customers are getting increasingly demanding in terms of personalization of products and are asking for shorter product development periods with more predictable product performance, especially in fashion industry. Current market pressures drive firms to adapt new design process in product development (PD) processes. Nevertheless, choosing the effective PD process is a challenging, complex decision. There is a critical need to develop a performance measurements system (PMS) for choosing appropriate product development (PD) processes in garment design to support product mangers to effectively respond to market. This paper presents a knowledge-based open performance measurement system (KBO-PMS) in big data environment, in order to support complex industrial decision-making for new product development. Its dynamic and flexible structure enables the whole system to be more adapted to knowledge sharing of product managers and processing of various time-varying data. The proposed KBO-PMS is composed of an interactive structure, capable of both integrating new KPIs from the open resource and tracking the evolution of the KBO-PMS components with time. The proposed KBO-PMS has been validated by realizing the performance evaluation of product development (PD) in fashion industry. It can be regarded as an application of open-resource based dynamic group decision-making in fashion big data environment.

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