Improving supply chain collaboration through operational excellence approaches: an IoT perspective

Abstract

Purpose

Collaboration is an important emerging dimension of sustainable supply chain management. How to improve supply chain collaboration (SCC) by means of operational excellence approaches has become an important research topic. The Internet of things (IoT), an important means of operational excellence, has also received increased attention. For better collaboration by the IoT, this study proposes a novel methodology to evaluate the measures of IoT adoption in SCC.

Design/methodology/approach

Based on the six-domain model and the common classification of collaboration, the measures of the IoT and the criteria of SCC are developed, respectively. A hybrid multi-step methodology that combines neutrosophic set theory, analytic hierarchy process (AHP) and technology for order preference by similarity to an ideal solution (TOPSIS) is proposed to complete the evaluation.

Findings

The results show that improving information transparency, strengthening the integration of management information systems and improving large data processing abilities are the most important measures of the IoT in improving SCC. Measures such as introducing sensing technology and laser scanning technology rank at the bottom and are relatively unimportant.

Practical implications

The research results provide insights and references for firms to improve SCC by adopting appropriate IoT measures.

Originality/value

Most of existing studies indicate the significance of technology in SCC. But this study shows a different conclusion that technologies rank the bottom, while information transparency is more important. And a suitable explanation is given. It further enriches the theoretical studies in SCC field.

Citation

Cui, L., Gao, M., Dai, J. and Mou, J. (2022), "Improving supply chain collaboration through operational excellence approaches: an IoT perspective", Industrial Management & Data Systems, Vol. 122 No. 3, pp. 565-591. https://doi.org/10.1108/IMDS-01-2020-0016