Multi-group decision-making approach for evaluating brand collaboration requirements of large-scale manufacturing industrial value chain

Abstract

Brand collaboration requirement (BCR) evaluation is a key premise for core manufacturing enterprises to lead the collaborative brand construction of a large-scale manufacturing industry value chain (LSMIVC). Because brand collaboration (BC) in LSMIVC requires interaction with other businesses, BCRs are interactional and decision-makers come from multiple groups. However, existing requirement evaluation approaches rarely consider the interactional nature of requirements and the multiple groups nature of decision makers, producing unstable, easy-to-fail, and inaccurate results. Therefore, this study presents a multi-group decision-making approach based on a decision-making trial and evaluation laboratory and analytic network process (DEMATEL-ANP). This approach integrates the assessment information of BCRs from group decision-makers and prevents unstable and easily invalid assessment results owing to an incomplete decision-maker team. Additionally, DEMATEL-ANP was used to rank the interaction BCRs, which improved the accuracy and effectiveness of the requirement evaluation. Finally, the developed approach and model were tested against the air-conditioning industry value chain. The case study shows that the proposed method can determine requirement ranking. Comparative tests show that the proposed approach is effective and superior, and is more consistent with real-world BC situations. Therefore, the proposed approach provides a direction and theoretical basis for core manufacturing enterprises to implement BC.

Keywords:

• requirements evaluation
• brand collaboration
• large-scale manufacturing
• industrial value chain
• group decision-making
• DEMATEL–ANP

Disclosure statement

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

Additional information

Funding

This work was supported by National Key Research and Development Program of China;National Natural Science Foundation of China;National Natural Science Foundation of China-Guangdong Joint Fund; 2018 Guangzhou Leading Innovation Team Program (China).