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Modular production brings new opportunities for service provisions. We examine a supply chain consisting of two competing module suppliers and a common manufacturer, in which the manufacturer sources two differentiated modules from two suppliers and then produces two competing products for consumers. A delegation relationship is applied to explore the possibility of the module services offered by the suppliers. When the service commission fees are fixed, there exist optimal commission fees to maximize the manufacturer's profit. Service commission fees, service efficiencies, service sensitivity, and market types greatly affect the service levels, prices, and profitability of each member in the supply chain. The manufacturer should strategically adopt differentiated pricing strategies and service provisions from two suppliers rather than the best service abilities of the suppliers. In the service‐level sensitivity market, the improved service levels are more attractive to consumers so that the manufacturer will charge high prices by investing more in services. Product services with "high quality, high price" are more suitable for high‐end consumers. We also consider the variable service commission fees and find that there are still some robust results except that the effects of the service incentives and the commission fees will be enlarged. [ABSTRACT FROM AUTHOR]

Modular production requires new service provision under the trend of servitization. This paper is to explore the issue of module-based service associated with three different power structurers in a two-echelon supply chain that consists of a module supplier and a manufacturer. The manufacturer considers to delegate its upstream module supplier to provide services for downstream consumers. We divide the market into two types: the price-sensitive market and the service-level sensitive market. Different power structures and market types have a great impact on the service levels, prices and the profitability of each member in the supply chain. When the manufacturer (the supplier) acts as the leader in the supply chain, the module supplier should provide the highest (lowest)-level service and the manufacturer will charge the highest (lowest) price for the bundled product and service. The module supplier can benefit from the service-delegation relationship while the manufacturer will be hurt. There is an opportunity for the module supplier to offer the service, especially when the module supplier is in a dominated position. [ABSTRACT FROM AUTHOR]

With behavior-based pricing (BBP), firms use consumers' purchase history data to price discriminate between new and past consumers. In this paper, we investigate BBP in a competitive setting, which consists of a standard product firm and a custom product firm. We examine how firms' adoption of BBP affects product variety, lead time, and price decisions. We formulate this problem as a two-period game model. Through the analysis of equilibrium outcomes, we find that BBP by the standard product firm alone decreases product variety, whereas BBP by the custom product firm alone increases lead time. In addition, BBP increases one firm's market share when only this firm uses BBP. Furthermore, the impact of BBP on profits is influenced by the consumer sensitivity to lead time and the cost incurred by reducing lead time. [ABSTRACT FROM AUTHOR]

Project risk management, as a subjective and dynamic decision method of avoiding project risk, plays an important role in selecting appropriate control measures (i.e. measure set) to handle multiple emerging risks (i.e. risk set). This paper focuses on the multi-risk control problem under the condition of finite risk management resources. Based on the description of a Multi-HUB network and a collaboration mechanism within project risk management system, a multi-risk control system is formulated as a non-cooperative complete information game model between the project risk set and the measure set. In the proposed model, each risk within the risk set is described as a player in the game, who is competing with other players for the allocation of risk control resources that are available in limited quantities within a given measure set. Then an efficient algorithm is proposed to solve the allocation solution based on Nash equilibrium, and an experiment is presented to illustrate the effectiveness of the proposed game model. The proposed modeling and solution methods can be used to support decision-making in project risk management. [ABSTRACT FROM AUTHOR]