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A methodology for the modular structure planning of product-service systems

1 Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
2 Institute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge, CB3 0FS, United Kingdom

Special Issues: Optimization methods in Intelligent Manufacturing

Product-service system (PSS) is an important way of the transformation and upgrading of modern manufacturing industry, it is also one of core development trends of intelligent manufacturing. A PSS can be configured quickly and cheaply to meet the customer’s personalized product and service requirements via a PPS design platform, and a modular master structure is the core of PSS design platform. When a PSS instance is configured, it needs to determine the module types and make decisions on the types of PSS firstly, so as to build a master structure for PSS. Therefore, the decision-making on module types and customization degree is a key step to establish the PSS modular master structure. This article proposes a five-step planning method for the modular structure planning of PSS. Firstly, the PSS module types are classified based on the Kano model. Then, bi-level decision-making on modules and its properties are finished by using conjoint analysis method, includes the customer’s decision-making on modules and their properties, and the manufacturer’s modules and their properties, which provides support for PSS modular optimization configuration design. Finally, the proposed methodology is validated through the case of power transformer. The proposed module planning method for the PSS modular structure helps to determine the module types for PSS services solution layer and generic part layer.
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Keywords product-service system; modular master structure; modular design; conjoint analysis

Citation: Hao Li, Xiaoyu Wen, Haoqi Wang, Guofu Luo, Steve Evans. A methodology for the modular structure planning of product-service systems. Mathematical Biosciences and Engineering, 2019, 16(3): 1489-1524. doi: 10.3934/mbe.2019072


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