4.7 Article

Selecting industrial IoT Platform for digital servitisation: a framework integrating platform leverage practices and cloud HBWM-TOPSIS approach

Journal

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 61, Issue 12, Pages 4022-4044

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.2002458

Keywords

Digital servitisation; industrial IoT platform; platform leverage practice; cloud hierarchical BWM; cloud TOPSIS

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Digital servitisation is an important strategy for enhancing industrial companies' competitiveness. However, selecting the appropriate IIoT platform for deploying industrial service is challenging due to a lack of understanding of the required capabilities. To address this issue, a multi-criteria decision making framework is proposed, which combines cloud hierarchical BWM and cloud TOPSIS methods. This framework helps companies accurately determine the criteria weights and ranking of potential platforms, allowing managers to make informed decisions and leverage the IIoT platform for digital servitisation.
Digital servitisation has emerged as an important strategy to enhance industrial companies' competitiveness. Leveraging the IIoT (industrial internet of thing) platform is considered an essential way to facilitate digital servitisation. Selecting an appropriate IIoT platform from numerous alternatives in the market is a difficult task for the firms due to lack of deep understanding of the required IIoT platform capabilities for deploying industrial service. To help firms make wise decision, we propose a feasible multi-criteria decision making framework for IIoT platform selection. Firstly, a practice-oriented technical-managerial-service criteria system is derived from typical platform leverage logics for digital servitisation. Next, an integrative approach combining cloud hierarchical BWM (best-worst method) and cloud TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed for selecting the best IIoT platform. Using this approach, the criteria weights and the ranking of potential platforms can be accurately determined by considering the fuzziness and randomness of linguistic decision information. Finally, a case study of a Chinese crane manufacturer illustrates the feasibility and reliability of the proposed framework. The analysis results can help the managers find the best IIoT platform and provide them with deep insight and direction for leveraging the IIoT platform towards digital servitisation.

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