In recent decades, manufacturing practices have undergone a significant transformation, with the integration of computers and automation playing a central role. Concurrently, there has been a growing interest in utilizing intelligent techniques to effectively manage manufacturing processes. These processes entail the seamless integration of various activities across the supply chain. Given the diverse range of actors in a supply chain, each one with distinct characteristics such as cost, quality, and probability of failure, task assignment becomes a crucial challenge. In such a complex scenario, manual decision-making becomes impractical, necessitating the adoption of automated techniques to effectively address these challenges in a resilient and adaptive manner. This article proposes a service-oriented approach to model each manufacturing actor within the supply chain. Furthermore, it categorizes automated synthesis approaches for smart manufacturing on the basis ofi)the characteristics of each actor, which are retrieved by their Industrial API, andii)the goal(s) of the manufacturing process. Finally, the article evaluates three distinct approaches that implement automated synthesis techniques for composing services and generating operational plans.

Recent years witnessed a growing interest in the employment of intelligent techniques for the management of manufacturing processes in smart manufacturing. These processes may include tens of resources distributed among several different companies composing the supply chain. The status of the different resources evolves over time in terms of cost, quality, and probability of breakdunavailability, thus requiring the process to be adaptive and resilient to disruptions. Due to the high number of involved resources, making decisions manually soon become unfeasible, thus requiring automated techniques to solve the problem. In this paper, we discuss the potential and limitations of automated reasoning techniques for making modern supply chains adaptive and resilient by relying on the information provided by resources through their APIs.