Customization-Oriented Design of Product-Service System

In order to support PSS (Product-Service System) customization in early design phase, Song and Sakao (2017) develop a modular design framework that involves design activities and methods. The framework includes five successive stages, i.e. requirements analysis, requirements specification, modularization, modular configuration and concept selection. The proposed design framework is module-based and can be adjusted flexibly according to the user needs. In addition, it takes advantage of some existing methods. An elevator PSS design case study shows the feasibility and potentials of the design framework and its associated design process to its broad usage in industry.

Since the success of the PSS largely depends on the understanding and satisfying of different requirements, requirements analysis has become a critical factor in PSS development.

In this Chapter, the elicited PSS requirements are converted into technical attributes based on a rough QFD (Quality function deployment) approach developed by Song et al. (J Prod Res 52(18):5528–5545, 2014). The method includes two stages: determining importance of customer requirement and the prioritizing the technical attributes based on rough set theory and grey relational analysis (GRA).

PSS is high-value service solution (e.g. repair, maintenance, and energy management) based on product to help manufacturers to achieve sustainable growth and profitability. Modularization can help to realize customization, increase in flexibility, reusability of service modules, and simplification of complex PSS systems, etc.

Configuration is an efficient method for rapid PSS customization. However, the previous service configuration methods may produce a large number of feasible solutions, especially when there are more module instances or fewer configuration constraints. This will increase the burden of service solution screening and reduce the efficiency of service delivery. To solve this problem, Song and Chan (2015) develop a multi-objective optimization model for configuration of the PSS. The optimization model considers service performance, service cost and response time at the same time, and it is solved with non-dominated sorting genetic algorithm II (NSGA II) to obtain a set of optimal configuration solutions. In this way, the manufacturer can flexibly satisfy customer needs with a module-based PSS at lower cost. The PSS configuration optimization model is expected to enhance the customization ability of the service provider, because it can respond to customer requirements timely by providing the customized PSS. The rough TOPSIS approach developed by Song et al. (2013b) can then be used to evaluate and select the proper PSS concept from the configured PSS set.

Many manufacturers today are trying hard to provide a large number of value-added PSSs. Potential buyers are impeded by the increased number of PSS to effectively find the most suitable PSS to meet their personalized needs. In order to accurately discover the needed or wanted PSS with lower search costs, it is effective to recommend suitable PSS solutions to the appropriate buyers.