Currently, many new challenges, such as multiplication of data and information, mass customization, global cooperation, and scarcity of resources, shape the relationships among different actors. Therefore, when managers have several projects to schedule, human resources allocation becomes much more complex to grasp. The work in this paper is to propose a human resources allocation methodology in design process to cope with the adaption of the Product‐Process‐Organization (P‐P‐O) model for the factory of the future. According to the new concept in the Industry 4.0, future human resource organization structure will be the horizontal and point‐to‐point structures. Therefore, this paper discusses the related concepts regarding human resource horizontal ability and project urgency level.

The concurrent engineering design depends on the efficiency of communication between the actors in the design process, such as how effective communication between engineers and teams will have a direct effect on the design efficiency. Currently, the relationships among different actors in the project are shaped by many new challenges such as multiplication of data and information, mass customization, global collaboration, ageing societies, increasing urbanization, scarcity of resources, dynamic technology and innovation. When managers consider these factors, human resource evaluation becomes much more complex to grasp. In order to cope with adaption of product-process-organization model for industry of the future, it is necessary to have a methodology to approach the problem of human resource evaluation in the future organization structure.

Multi-Criteria Decision Making (MCDM) methods have rapidly developed and have been applied to many areas for decision making in engineering. Apart from that, the process to select fault-diagnosis sensor for Fuel Cell Stack system in various options is a multi-criteria decision-making (MCDM) issue. However, in light of the choosing of fault diagnosis sensors, there is no MCDM analysis, and Fuel Cell Stack companies also urgently need a solution. Therefore, in this paper, we will use MCDM methods to analysis the fault-diagnosis sensor selection problem for the first time. The main contribution of this paper is to proposed a fault-diagnosis sensor selection methodology, which combines the rank reversal resisted AHP and TOPSIS and supports Fuel Cell Stack companies to select the optimal fault-diagnosis sensors. Apart from that, through the analysis, among all sensor alternatives, the acquisition of the optimal solution can be regarded as solving the symmetric or asymmetric problem of the optimal solution, which just maps to the TOPSIS method. Therefore, after apply the proposed fault-diagnosis sensor selection methodology, the Fuel Cell Stack system fault-diagnosis process will be more efficient, economical, and safe.

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