Train-the-Trainer Concept for the “Industrie 4.0-CheckUp”

The digitalization of society is causing companies’ environmental conditions to change. New customer demands, a change in employee thinking and a market situation altered by new competitors are making the digital transformation of companies a necessity. Identifying capabilities in a company, recommending actions and then implementing actions necessitates ascertaining the company’s level of development in terms of digital transformation. A multitude of capability maturity models and different approaches to use exist to meet the needs of SMEs and large companies. Since the dimensions of Industrie 4.0 are understood slightly differently all over the world, this paper formulates a train-the-trainer approach that ensures a global baseline understanding based on a dedicated capability maturity model. The paper concludes with a discussion of future applications for this method

APERTO: A Framework for Selection, Introduction, and Optimization of Corporate Social Software

The deployment of social software in enterprises to support collaborative work has become increasingly important in the past few years. At the same time, the characteristics of social software--most importantly the so-called Nutzungsoffenheit--require a change of mindset. Corporate social software differs strikingly from traditional business software, which has clearly defined common usage scenarios for its functions. Classic approaches concerning the requirements analysis, change management and success measurement of business software can be applied only partly or not at all. In this report, the APERTO framework, consisting of the APERTO five-level model, the CUP-Matrix, as well as the tools developed therefrom, is introduced. It enables a complete and consistent categorization and classification of the usage potentials of corporate social software, and thus supports its selection, implementation, and optimization. The approach described in this report was applied successfully multiple times in the past few months in projects to select and implement solutions in various German enterprises

Adaptive Multi-Priority Rule Approach To Control Agile Disassembly Systems In Remanufacturing

End-of-Life (EOL) products in remanufacturing are prone to a high degree of uncertainty in terms of product quantity and quality. Therefore, the industrial shift towards a circular economy emphasizes the need for agile and hybrid disassembly systems. These systems feature a dynamic material flow. Besides that, they combine the endurance of robots with the dexterity of human operators for an effective and economically reasonable EOL-product treatment. Moreover, being reconfigurable, agile disassembly systems allow an alignment of their functional and quantitative capacity to volatile production programs. However, changes in both the system configuration and the production program to be processed call for adaptive approaches to production control. This paper proposes a multi-priority rule heuristic combined with an optimization tool for adaptive re-parameterization. First, domain-specific priority rules are introduced and incorporated into a weighted priority function for disassembly task allocation. Besides that, a novel metaheuristic parameter optimizer is devised to facilitate the adaption of weights in response to evolving requirements in a reasonable timeframe. Different metaheuristics such as simulated annealing or particle swarm optimization are incorporated as black-box optimizers. Subsequently, the performance of these metaheuristics is meticulously evaluated across six distinct test cases, employing discrete event simulation for evaluation, with a primary focus on measuring both speed and solution quality. To gauge the efficacy of the approach, a robust set of weights is employed as a benchmark. Encouragingly, the results of the experimentation reveal that the metaheuristics exhibit a notable proficiency in rapidly identifying high-quality solutions. The results are promising in that the metaheuristics can quickly find reasonable solutions, thus illustrating the compelling potential in enhancing the efficiency of agile disassembly systems

Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

The Impact of Formal Hierarchies on Enterprise Social Networking Behavior

With more and more companies using enterprise social networks (ESN) for employee communication and collaboration, the influence of ESN on organizational hierarchies has been subject of countless discussions in practice-oriented media and first academic studies. Conversely, the question whether and how formal organizational hierarchies influence ESN usage behavior has not yet been addressed. Drawing on a rich data set comprising 2.5 years of relationship building via direct messages, confirmed contact requests, and group messages, we are able to show that formal hierarchies have an important impact on social networking behavior. By applying means of social network analysis and supported by statements from interviews, we illustrate how deeply formal hierarchy impacts the three examined types of relationships. Our results motivate academics to further study the interrelation between hierarchy und ESN and hierarchy’s effects regarding the sociotechnical design and implementation of related systems

Adaptive Multi-Priority Rule Approach To Control Agile Disassembly Systems In Remanufacturing

End-of-Life (EOL) products in remanufacturing are prone to a high degree of uncertainty in terms of product quantity and quality. Therefore, the industrial shift towards a circular economy emphasizes the need for agile and hybrid disassembly systems. These systems feature a dynamic material flow. Besides that, they combine the endurance of robots with the dexterity of human operators for an effective and economically reasonable EOL-product treatment. Moreover, being reconfigurable, agile disassembly systems allow an alignment of their functional and quantitative capacity to volatile production programs. However, changes in both the system configuration and the production program to be processed call for adaptive approaches to production control. This paper proposes a multi-priority rule heuristic combined with an optimization tool for adaptive re-parameterization. First, domain-specific priority rules are introduced and incorporated into a weighted priority function for disassembly task allocation. Besides that, a novel metaheuristic parameter optimizer is devised to facilitate the adaption of weights in response to evolving requirements in a reasonable timeframe. Different metaheuristics such as simulated annealing or particle swarm optimization are incorporated as black-box optimizers. Subsequently, the performance of these metaheuristics is meticulously evaluated across six distinct test cases, employing discrete event simulation for evaluation, with a primary focus on measuring both speed and solution quality. To gauge the efficacy of the approach, a robust set of weights is employed as a benchmark. Encouragingly, the results of the experimentation reveal that the metaheuristics exhibit a notable proficiency in rapidly identifying high-quality solutions. The results are promising in that the metaheuristics can quickly find reasonable solutions, thus illustrating the compelling potential in enhancing the efficiency of agile disassembly systems

Towards a Service-Oriented Architecture for Production Planning and Control: A Comprehensive Review and Novel Approach

The trends of shorter product lifecycles, customized products, and volatile market environments require manufacturers to reconfigure their production increasingly frequent to maintain competitiveness and customer satisfaction. More frequent reconfigurations, however, are linked to increased efforts in production planning and control (PPC). This poses a challenge for manufacturers, especially in regard of demographic change and shortage of qualified labour, since many tasks in PPC are performed manually by domain experts. Following the paradigm of software-defined manufacturing, this paper targets to enable a higher degree of automation and interoperability in PPC by applying the concepts of service-oriented architecture. As a result, production planners are empowered to orchestrate tasks in PPC without consideration of underlying implementation details. At first, it is investigated how tasks in PPC can be represented as services with the aim of encapsulation and reusability. Secondly, a software architecture based on asset administration shells is presented that allows connection to production data sources and enables integration and usage of such PPC services. In this sense, an approach for mapping asset administrations shells to OpenAPI Specifications is proposed for interoperable and semantic integration of existing services and legacy systems. Lastly, challenges and potential solutions for data integration are discussed considering the present heterogeneity of data sources in manufacturing

Extended Production Planning of Reconfigurable Manufacturing Systems by Means of Simulation-based Optimization

Reconfigurable manufacturing systems (RMS) are capable of adjusting their operating point to the requirements of current customer demand with high degrees of freedom. In light of recent events, such as the covid crisis or the chip crisis, this reconfigurability proves to be crucial for efficient manufacturing of goods. Reconfigurability aims thereby not only at adjust production capacities but also for fast integration of new product variants or technologies. However, the operation of such systems is linked to high efforts concerning manual work in production planning and control. Simulation-based optimization provides the possibility to automate processes in production planning and control with the advantage of relying on mostly existing models such as material flow simulations. This paper studies the capabilities of the meta heuristics evolutionary algorithm, linear annealing and tabu search to automate the search for optimal production reconfiguration strategies. Two distinct use cases are regarded: an increase of customer demand and the introduction of a previously unknown product variant. A parametrized material flow simulation is used as function approximator for the optimizers, whereby the production system's structure as well as logic are target variables of the optimizers. The analysis shows that meta-heuristics find good solutions in a short time with only little manual configuration needed. Thus, metaheuristics illustrate the potential to automate the production planning of RMS. However, the results indicate that the performance of the three meta-heuristics considering optimization quality and speed differs strongly

A Survey on Self-Supervised Representation Learning

Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image representations without supervision. These representations can then be used in downstream tasks like classification or object detection. The quality of these representations is close to supervised learning, while no labeled images are needed. This survey paper provides a comprehensive review of these methods in a unified notation, points out similarities and differences of these methods, and proposes a taxonomy which sets these methods in relation to each other. Furthermore, our survey summarizes the most-recent experimental results reported in the literature in form of a meta-study. Our survey is intended as a starting point for researchers and practitioners who want to dive into the field of representation learning

Interoperable Architecture For Logical Reconfigurations Of Modular Production Systems

Individualisation of products and ever-shorter product lifecycles require manufacturing companies to quickly reconfigure their production and adapt to changing requirements. While most of the existing literature focuses on organisational structures or hardware requirements for reconfigurability, requirements and best practices for logical reconfigurations of automated production systems are only sparsely covered. In practice, logical system reconfigurations require adjustments to the software, which is often done manually by experts. With the ongoing automation and digitisation of manufacturing systems in the context of Industry4.0, the need for automated software reconfigurations is increasing. However, heterogeneous and proprietary technologies in the field of industrial automation pose a hurdle to overcome for generally applicable approaches for logical reconfigurations in the industrial domain. Therefore, this paper reviews available technologies that can be used to solve the problem of automated software reconfigurations. For this purpose, an architecture and a procedure are proposed on how to use these technologies for automatic adaptation and virtual commissioning of control software in industrial automation. To demonstrate the interoperability of the approach, collective cloud manufacturing is used as a composing platform. The presented approach further includes a domain-specific capability model for the specification of software artefacts to be generated, allowing jobs to be described and matched on the platform. The core element is a code generator for generating and orchestrating the control code for process execution using the reconfigurable digital twin as a validator on the platform. The approach is evaluated and demonstrated in a real-world use case of a modular disassembly station