Data-driven diagnosis framework for platform product supply chains under disruptions

Li, Mingxing1,2,3; Cai, Yiji4; Guo, Daqiang5; Qu, Ting1,2,3; Huang, George Q.6

2024-10-01

10.1080/00207543.2024.2407915

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH   影响因子和分区

0020-7543

1366-588X

Global supply chains face disruptions from geopolitical conflicts, pandemics, and wars. These disruptions exert a long-lasting effect across the supply chain, affecting supply, logistics, and markets. Platform product supply chains, characterised by their diversity of choices within interconnected nodes encompassing product configuration, supply, manufacturing, and delivery, are particularly vulnerable to these disruptions, incurring significant costs and diminished customer satisfaction. Therefore, the ability to diagnose these issues is vital for improving its overall performance. This study introduces a novel three-phase framework for supply chain diagnosis that leverages a data-driven methodology. Initially, the framework employs Generic Bills-of-Materials (GBOM) for qualitative structural mapping of platform products and their supply chains. Subsequently, a network model is constructed to encapsulate intra-nodal and inter-nodal dynamics of the supply chain. The third phase integrates Failure Mode and Effects Analysis (FMEA) with historical data to formalise supply chain domain knowledge, enabling a comprehensive analysis of the supply chain operational state. Finally, a real industrial case is presented, showing the effectiveness of the proposed framework in diagnosing short-, medium-, and long-term decisions. Findings reveal (i) inventory placement yield divergent impacts on the supply chain order fulfilment cycle time (OFCT) and (ii) reducing product variants improves planning accuracy and reduces OFCT.

SDG8: Decent work and economic growth supply chain diagnosis disruptions supply chain resilience platform product failure mode and effects analysis (FMEA)

SCI

英语

通讯

National Key Research and Development Program of China["2021YFB3301701","2021YFB3301702"] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515110622] ; Science and Technology Projects in Guangzhou[202201010329] ; The 2019 Guangdong Special Support Talent Program-Innovation and Entrepreneurship Leading Team (China)[2019BT02S593] ; RGC Theme-based Research Scheme[T32-707/22-N]

Engineering ; Operations Research & Management Science

Engineering, Industrial ; Engineering, Manufacturing ; Operations Research & Management Science

WOS:001326940800001

TAYLOR & FRANCIS LTD

Web of Science

1.Jinan Univ, Sch Intelligent Syst Sci & Engn, Zhuhai, Peoples R China
2.Jinan Univ, Guangdong Int Cooperat Base Sci & Technol GBA Smar, Zhuhai, Peoples R China
3.Jinan Univ, Inst Phys Internet, Zhuhai, Peoples R China
4.Southern Univ Sci & Technol, Coll Business, Shenzhen, Guangdong, Peoples R China
5.Hong Kong Univ Sci & Technol Guangzhou, Smart Mfg Thrust, Syst Hub, Guangzhou, Peoples R China
6.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong, Peoples R China

Li, Mingxing,Cai, Yiji,Guo, Daqiang,et al. Data-driven diagnosis framework for platform product supply chains under disruptions[J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH,2024.

Li, Mingxing,Cai, Yiji,Guo, Daqiang,Qu, Ting,&Huang, George Q..(2024).Data-driven diagnosis framework for platform product supply chains under disruptions.INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH.

Li, Mingxing,et al."Data-driven diagnosis framework for platform product supply chains under disruptions".INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2024).