An important prerequisite for determining whether a certain product is producible in any given production facility is an accurate assessment of which production lines and/or the machines are able to execute the necessary production steps. Not only the static information about the capabilities of the machines, but also the conditions of machines and tools are significant for this analysis. Because of the deviation of machine capabilities with increasing deterioration and weary of the equipment, it is also necessary to continuously monitor the status of the machine and analyze the machine conditions. In this paper, we present an approach for generating production plans across multiple factories, considering both static information and dynamic data analysis. Edge devices constantly monitor high frequency machine data and report condensed machine states to an Industrial IoT platform (IIoT). A marketplace within the cloud-application MindSphere enables us to integrate the requirements of the products and the capabilities of the production sites. Customers are be able to evaluate these production plans based on duration, energy consumption, CO2 footprint etc.

It is getting increasingly difficult for factories to provide the required flexibility to support lot-size-one production orders. Therefore, collaboration is especially important for manufacturing companies to ensure that they do not lose manufacturing contracts because they can only partially fulfill the requirements of their customers. This paper presents an approach for generating production plans across multiple factories, so that the production requirements for a given product can be met through collaboration with other factories. We describe two variants of an algorithm for computing multi-factory production plans based on constraint satisfaction. The approach makes inter-company logistics transparent to the product designer/seller - by managing the division of tasks, transportation between the factories, and resolving dependencies among the participating manufacturers in the network. We have designed an ecosystem management platform that will consider the product, its required quality, quantities, delivery dates, certification, logistics requirements etc. to automatically generate diverse production offers.

Traditionally, product configuration and production configuration processes are executed separately by different stakeholders in different phases of the product life cycle. With increasing demand for individualized products, the need for flexible production processes, modular factories and intelligent production infrastructures is also increasing. Therefore, the production environment must be continuously (re-)configured to meet production requirements of individualized products ("lot size one"). In order to simplify the value chain from product configuration to manufacturing of the individualized product, we propose to integrate product and production configuration - giving rise to a new methodology for variability management in smart production ecosystems. Such an ecosystem brings factory vendors, product designers, sellers and end-customers together. In this paper we propose novel concepts and algorithms for a holistic configuration approach required to support product designers, factory operators and end-users in a common marketplace. We present initial results from a pilot study.

Smart production aims to increase the flexibility of the production processes and be more efficient in the use of resources. Two important pillars of this initiative are "smart products" and "smart factories". From the perspective of product line engineering, these can be seen as two product lines (product line of factories and product line of goods) that need to be integrated for a common systems engineering approach. In this paper, we look at this problem from the perspective of configuration technologies, outline the research challenges in this area and illustrate our vision using an industrial example. The factory product line goes hand-in-hand with the product line of the products to be manufactured. Future research in product line engineering needs to consider an ecosystem of a multitude of stakeholders - e.g., factory component vendors, product designers, factory owners/operators and end-consumers.

Software solutions in complex environments, such as railway control systems or power plants, are assemblies of heterogeneous components, which are very large and complex systems themselves. Interplay of these systems requires a thorough design of a system-of-systems (SoS) encompassing the required interactions between the involved systems. One of the challenges lies in reconciliation of the domain data structures and runtime constraints to ensure consistency of the SoS behavior. In this paper, we present a generative approach that enables reconciliation of a common platform based on reusable domain models of the involved systems. This is comparable to a product line configuration problem where we generate a common platform model for all involved systems. We discuss the specific requirements for model composition in a SoS context and address them in our approach. In particular, our approach addresses the operational and managerial independence of the individual systems and offers appropriate modeling constructs. We report on our experiences of applying the approach in several real world projects and share the lessons learned.