Exploring Data-Driven Decision-Making for Enhanced Sustainability

被引:1
|
作者
Chavez, Zuhara [1 ]
Gopalakrishnan, Maheshwaran [1 ]
Nilsson, Viktor [2 ]
Westbroek, Arvid [2 ]
机构
[1] KTH Royal Inst Technol, Dept Sustainable Prod Dev, Sodertalje, Sweden
[2] KTH Royal Inst Technol, Dept Ind Econ & Management, Stockholm, Sweden
来源
SPS 2022 | 2022年 / 21卷
关键词
Data-driven; decision-making; sustainable manufacturing; digitalization; sustainability; BIG DATA; OPPORTUNITIES; MANAGEMENT; CHAIN;
D O I
10.3233/ATDE220158
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The industry transition towards digital transformation opens the possibilities to utilize data for enhancing sustainability in industrial operations and build capabilities towards resilient and circular operations, i.e., shift towards industry 5.0. This paper explores how data-driven decision-making (DDDM) can enable sustainable and resilient supply chain operations within the manufacturing industry. A series of in-depth interviews were conducted with experts, researchers, and company representatives across the manufacturing industry and universities in Sweden. The findings show a consensus among companies, researchers, and literature about the potential of data utilization for sustainability purposes; however, in most cases, the complete transformation towards data-driven has not happened yet. Companies have uncertainty about what data is needed rather than its lack. Reliability & validity of data become essential to exploit the potential of the data organizations already possess. Based on the literature and interview data, a conceptual model is proposed, including three identified parameters connected to DDDM, 1) data and IT infrastructure, 2) current operations, and 3) an improved triple bottom line performance. The model captures the interconnections between such parameters, depicting the benefits and challenges of DDDM and its relation to more sustainable and resilient supply chain operations within the manufacturing industry. In a data-driven approach, real-time analysis of complex & extensive amounts of data gives unlimited possibilities to improve manufacturing operations through decision-making.
引用
收藏
页码:392 / 403
页数:12
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