In the research area of capabilities and skills, the standardization of terms and models is progressing in working groups of Plattform Industrie 4.0 and Industrial Digital Twin Association. At the same time, there is an increasing amount of publications that use capability models for purposes such as automated selection of capabilities or planning of sequences. However, there is currently no concept and formal description of effects generated by resources when acting on products. Having such a formal effect definition is needed to express both required effects as well as effects achieved by capabilities in order to compare requirements with possible solutions to a desired production step in an automated manner. This article introduces a formalism that can be used to define effects in the context of capabilities. An effect is seen as the change between an initial and a target state, which can both be specified using properties. The formalism is used to express effects in three different manufacturing application examples. Furthermore, an implementation of the formalism using the Web Ontology Language is provided. This ontology is used to model effects in a machine-interpretable way and to automatically compare effects for compatibility.

The holistic application of data-driven value-adding services (DDSs) to shop-floor assets (SFAs; e.g. equipment, machines, components) is a major obstacle, particularly for small and medium-sized enterprises (SMEs): Due to the high complexity and required expertise in various disciplines. It is necessary to i) select suitable DDSs for a relevant component in production, ii) provide data for their training, iii) deploy the DDSs in the given IT/OT infrastructure, and iv) setup data-streams between SFAs, DDSs, and dashboards. Currently, many companies are implementing flagship projects that go through the necessary steps for individual components, but these isolated solutions are not scalable due to the need for manual intervention. This paper describes how to completely automatize the four steps and their consolidation in a scalable Industry 4.0 one-stop-shop solution. To achieve this goal, each asset is equipped with an Asset Administration Shell (AAS) that specifies, in particular, the represented asset's capabilities, the data required to apply such a capability and the data generated by a capability. Based on the AASs of SFAs, DDSs, and computing resources (CRs), a matching service suggests automatically which DDSs would add value to which SFAs and, for each SFA-DDS match, in which CR to deploy the DDS. A trade fair demonstration provides strong support for the statements made: The one-stop-shop solution including the automatic matching greatly simplifies the application of data-driven services and, in some cases, even makes it possible at all.

The contribution addresses the challenge of transforming a general recipe, which lacks knowledge about specific production resources, into a plant-specific master recipe in the process industry. The authors propose an approach using the concept of capabilities in manufacturing, enabled by an ontology and the Asset Administration Shell. The approach aligns required and offered capabilities at different description levels to allocate equipment to process elements and generate a master recipe in BatchML. The validation of the approach is accompanied by a procedure logic for a modular plant and discusses open challenges. In conclusion, the provided approach offers a methodical and less experience-dependent derivation of a master recipe for a manufacturing process.

Modular automation is a recent concept for industrial automation offering a more flexible production with less engineering effort. Historically, incompatible standards for modular automation exist in different industrial segments, e.g., in process automation (Module Type Package) and discrete automation (PackML). The incompatibility of these standards has a particular negative impact on so called hybrid or mixed-equipment production facilities where process and discrete manufacturing modules co-exist in a physical installation. In this contribution, we propose an integration approach of both standards which is based on Industry 4.0 technologies (e.g., Asset Administration Shell). Our approach is validated on a simulated process plant involving commercial soft-PLCs of multiple vendors for MTP modules and a BPMN-based process orchestrator.

The digitization of value chains is an ongoing challenge in production. The Asset Administration Shell (AAS) aims to address this issue. Besides standardization activities, there is however little architectural guidance on how to bridge the gap between potential AAS use-cases and realization.In this paper, we describe four AAS related use-cases that we derived from 15 application projects which adopted the AAS into industrial contexts. For each of the four use-cases, we devise an architecture blueprint that documents our experiences when applying the AAS. By utilizing these blueprints, practitioners can benefit from our experiences when implementing the AAS and bridge the gap between use-cases and implementation more easily.