With the emergence and development of servitization, more and more enterprises are turning from product focus to service focus. Service is customer-oriented, and driven by customer requirements. Value is the goal pursued by all actors in the service. In order to analyze the mechanism of multi-actor collaborative value creation in the service process, this paper proposes a method for building a service process value model, based on process mining. Driven by the raw data and an event log of service activities and processes in the real world, stored in the service system, the method uses process mining techniques and combines domain knowledge to describe the construction steps of the service process value model at the conceptual level. We focus on the specific processes and activities in the service, and mainly consider the value creation of the activity. The model proposed in this paper aims, to reflect how service actors co-create value in the actual execution of service processes, and to help service actors achieve their value goals. We use a case study inspired by an industrial case to validate our idea. Moreover, we develop a new plug-in, based on the α-algorithm for ProM, to realize the model construction in the case study.

The current sales model combining online and offline channels meets the diverse requirements of consumers. However, consumers’ inter-channel switching behavior and strategic behavior also pose significant challenges to pricing decisions in the hybrid channel. Using game theory and consumer utility theory, a retailer-driven pricing model is developed to study the optimal pricing problem for each channel in a mixed-channel supply chain considering the characteristics of channel competition and the waiting behavior of strategic consumers. Study results show there is a negative correlation between the proportion of strategic consumers and the optimal pricing and profit of each channel, and as the proportion of strategic consumers rises, the optimal pricing and profit of manufacturers and retailers all trend downward. Incorporating strategic consumers into the pricing model will assist the supply chain in elucidating the behavior of consumer heterogeneity during various decision-making periods and in making reasonable pricing decisions. Effective guiding strategies, such as pre-discount and purchase restrictions, can reduce the profit loss caused by strategic consumer behavior. The optimal combination of pre-announcement discount and strategic consumer ratio can generate the greatest profit for retailers and the supply chain.

With the evolution of product service systems, as well as the proposal and wide application of cloud computing and big services, more and more manufacturing enterprises are turning from being product oriented to service oriented. A difficult aspect is that with the growth of customer scale and the personalization of customer service needs, service providers cannot rely on their own resources to fulfill services. Moreover, meeting the needs of customers often requires a complete and complex service. Therefore, the service provider has to provide cross-enterprise collaborative services and coordinate the resources of all participants in the product service system to complete the services together. This research proposes a novel resource allocation method for product service systems that adopt the bilateral resource integration service mode and considers the service process life cycle. Based on the process mining techniques, this method extracts knowledge from the execution event log of the service process stored in the enterprise information system, constructs the resource allocation problem model, and gives the process mining-based resource allocation algorithm (PMRA). We use an air conditioner repair service as a case to verify the method proposed in this study. The contribution of this study is to propose a new method of resource allocation for cross-enterprise product−service processes based on process mining techniques, which takes into account empirical knowledge from historical data and can provide a new idea for service optimization of product service systems.

High-end manufacturing supply chains are increasingly exposed to disruption risks and environmental uncertainty, yet how Industrial Internet (II) empowerment builds supply chain resilience (SCR) and when such benefits are most pronounced remain unclear. Grounded in the resource-based view and ambidextrous innovation logic, this study investigates whether II empowerment—captured by connectivity capability (CC), integration capability (IC), and analytics capability (AC)—enhances SCR through supply chain collaborative innovation (SCCI), including supply chain breakthrough innovation (SCBI) and supply chain incremental innovation (SCII), and whether environmental uncertainty (EU) conditions these relationships. Survey data from 293 Chinese high-end manufacturing firms were analyzed using structural equation modeling and bootstrapped mediation tests, supplemented by moderated regression analysis. The results indicate that CC, IC, and AC all directly and positively affect SCR. CC and AC significantly promote SCBI, whereas the effect of IC on SCBI is not significant; meanwhile, CC, IC, and AC all significantly foster SCII. Both SCBI and SCII are positively associated with SCR. SCBI mediates the effects of CC and AC (but not IC) on SCR, while SCII mediates the effects of all three II dimensions. Furthermore, EU strengthens the impacts of CC, AC, SCBI, and SCII on SCR, whereas the IC × EU interaction is not significant. These findings clarify the innovation-based mechanisms and boundary conditions of II-enabled resilience and offer actionable implications for high-end manufacturers seeking resilient supply chains under uncertainty.