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.

Rule-based specifications of systems have again become common in the context of product line variability modeling and configuration systems. In this paper, we define a logical foundation for rule-based specifications that has enough expressivity and operational behavior to be practically useful and at the same time enables decidability of important overall properties such as consistency or cycle-freeness. Our logic supports rule-based interactive user transitions as well as the definition of a domain theory via rule transitions. As a running example, we model DOPLER, a rule-based configuration system currently in use at Siemens.

One of the greatest barriers on the way to the efficient creation, handling, and evolution of product lines is the complexity and scale of the underlying artifacts. In this context, the MAPLE/SCALE workshop focuses on the investigation of scalability issues and the application of model-driven concepts and techniques in software product line engineering (SPLE). The workshop explores how to handle product lines of realistic complexity and how to facilitate systematic and efficient product derivation.

The MAPLE workshop focuses on the application of concepts and techniques from Model-driven Software Engineering (MDSE) in Software Product Line Engineering (SPLE). A particular focus is on techniques that allow to derive products from a product line more efficiently in order to maximize the return on investment and realize the expected benefits of SPLE techniques. The workshop covers both automated and interactive model-based techniques. As submissions, we particularly encourage research papers based on industrial experience and empirical studies as well as contributions, which identify and structure open challenges and research questions.

Many of the benefits expected from software product lines (SPL) [1-2] are based on the assumption that the additional investment required for domain engineering, pays off during application engineering when products are derived from the product line [3]. However, to fully exploit this we need to optimize application engineering processes and handle the reusable artifacts of an SPL in a systematic and efficient manner. In this context, the joint MAPLE/SCALE workshop focuses on two closely related aspects: how model-driven approaches can help to achieve systematic and efficient derivation of products and how scalability challenges can be addressed that arise from the application of SPL techniques to SPLs of realistic size and complexity. The workshop aims to explore and explicate the current status and ongoing work in model-driven approaches and/or scalability of SPLs and the transfer of knowledge between different disciplines and application domains.

In industrial settings, products are rarely developed by one organization alone. Software vendors and suppliers typically maintain their own product lines, which can contribute to a larger (multi) product line. The teams involved often use different approaches and tools to manage the variability of their systems. It is unrealistic to assume that all participating units can use a standardized and prescribed variability modeling technique. The configuration of products based on several models in different notations and with different semantics is not well supported by existing approaches. In this paper we present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototypic implementation based on Web Services. We show the feasibility of the approach and its implementation by using it with two different variability modeling approaches (one feature-based and one decision-oriented approach) on an example derived from industrial experience.

Developers of software engineering tools are facing high expectations regarding capabilities and usability. Users expect tools tailored to their specific needs and integrated in their working environment. This increases tools' complexity and complicates their customization and deployment despite available mechanisms for adaptability and extensibility. A main challenge lies in understanding and managing the dependencies between different technical mechanisms for realizing tool variability. We report on industrial experiences of applying a model-based and tool-supported product line approach for the customization and deployment of two Eclipse-based tools. We illustrate challenges of customizing these tools to different development contexts: In the first case study we developed variability models of a product line tool suite used by an industry partner and utilized these models for tool customization and deployment. In the second case study we applied the same approach to a maintenance and setup tool of our industry partner. Our experiences suggest to design software tools as product lines; to formally describe the tools' variability in models; and to provide end-user capabilities for customizing and deploying the tools.

Tool developers are facing high expectations regarding the capabilities and usability of software engineering tools. Users expect tools which are tailored to their specific needs and integrated in their environment. This increases the complexity of tools and makes their customization more difficult, although numerous mechanisms supporting adaptability and extensibility are available. In this experience paper we report on the lessons we have learned when developing a tool suite for product line engineering. Our experiences suggest that software engineering tools should be designed as product lines.

Software maintenance and evolution are among the most challenging and cost-intensive activities in software engineering. This is not different for software product lines due to their complexity and long life-span. New customer requirements, technology changes and internal enhancements lead to the continuous evolution of a product line's reusable assets. Due to the size of product lines, single stakeholders or teams can only maintain a small part of a system which poses additional challenges for evolution. This paper presents an approach supporting product line evolution by organizing variability models of large-scale product lines as a set of interrelated model fragments defining the variability of particular parts of the system. The approach allows semi-automatic merging of fragments into complete variability models. We also provide tool support to automatically detect changes that would make models and the architecture inconsistent. Furthermore, our approach supports the co-evolution of variability models and their respective meta-models. We illustrate the approach with examples from an ongoing industry collaboration.

Evolution is a permanent challenge in product line engineering. Reusable assets such as software components or documents evolve continuously due to new customer requirements or technology changes. This leads to modifications or extensions of the product line's variability models describing the reference architecture. Due to the large size of product lines, single stakeholders or teams can only maintain a small part of a system which poses additional challenges for evolution. This paper presents a tool-supported approach for building and maintaining variability models of large-scale product lines. We structure variability models into multiple model fragments of manageable size that can be created and maintained by individual teams. Model fragments can be merged semi- automatically into a variability model. We illustrate the approach with examples from ongoing industry collaboration.

Product line engineering comprises many heterogeneous activities such as capturing the variability of reusable assets, supporting the derivation of products from the product line, evolving the product line, or tailoring the approach to the specifics of a domain. The inherent complexity of product lines implicates that tool support is inevitable to facilitate smooth performance and to avoid costly errors. Product line engineering tools have to support heterogeneous stakeholders involved in diverse activities. Tool integration therefore is of particular importance to foster their seamless cooperation. However, the integration is difficult to achieve due to the diversity of models and work products. This paper describes the DOPLER tool suite which has been developed to provide such integrated support. The tool suite is flexible and extensible to support domain-specific needs

Deriving a product from a product line requires the involvement and cooperation of heterogeneous stakeholders such as customers, sales people, or engineers. Taking their different roles and needs into account is essential to exploit the possible benefits of product lines. In this paper we present the tool-supported product line engineering approach DOPLER. We demonstrate how the approach supports both non- technicians and engineers in product derivation and requirements engineering through a set of integrated tools.

Product derivation is the process of constructing products from the core assets in a product line. Guidance and support are needed to increase efficiency and to deal with the complexity of product derivation. Research has, however, devoted comparatively little attention to this process. In this paper we describe an approach for supporting product derivation. We show that variability models need to be prepared for concrete projects before they can be effectively utilized in the derivation process. Project-specific information and sales knowledge should be added and irrelevant variability should be pruned. We also present tool support and illustrate the approach using examples from ongoing research collaboration.

Understanding and modeling architectural variability is fundamental in product line engineering. Various extensions have been proposed to architecture description languages (ADLs) to deal with variability. Although these extensions are useful, we argue in this paper that decisions need to be treated as first-class citizens for modeling architectural variability. Decisions that have to be taken by different stakeholders during product derivation are an essential source to understand the variability at different levels (e.g., features, architecture, and implementation). We outline a decision-oriented approach to variability modeling and illustrate it with an example from our ongoing research collaboration with Siemens VAI.

Existing methods and tools supporting product line variability management typically emphasize either the feature or the architecture level. There have been attempts to combine these aspects, but no widely accepted method is available so far. This paper reports ongoing research in designing and implementing product line variability models, where the focus lies in treating features and architectural elements as parts of an integrated model. The research is carried together with our industry partner Siemens VAI