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

Abstract

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.

KEYWORDS:

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71971139]; National Science and Technology Major Project of the Ministry of Science and Technology of China [grant numbers 2017-I-0007-0008, 2017-I-0011-0012].

Notes on contributors

Tongtong Zhou

Tongtong Zhou is a Ph.D candidate in Shanghai Jiao Tong University, majoring in the Industrial Engineering. Her interests include platform-based product service system and decision support techniques. Her works have been published in Applied Soft Computing Journal and Journal of Cleaner Production.

Xinguo Ming

Xinguo Ming is a professor in the Industrial Engineering at the Shanghai Jiao Tong University. His research focus on the product development, product service system, industrial artificial intelligence and smart manufacturing system. His works have been published in Journal of Cleaner Production, Applied Soft Computing Journal, Computers in Industry, Advanced Engineering Informatics, International Journal of Production Research, etc.

Zhihua Chen

Zhihua Chen is a Ph.D candidate in Shanghai Jiao Tong University, majoring in the Industrial Engineering. His interests include smart product service system, decision support, industrial internet, etc. His works have been published in Journal of Cleaner Production, Applied Soft Computing Journal, Computers in Industry, International Journal of Advanced Manufacturing Technology, etc.

Rui Miao

Rui Miao is an associate professor in School of Naval Architecture, Ocean & Civil Engineering at the Shanghai Jiao Tong University. His research focus on the product lifecycle management and system engineering. His works have been published in International Journal of Production Research, Expert Systems with Applications, etc.