Contribution to the industry-specific identification and selection of a business model in machinery and equipment industry

Industry 4.0 introduces a paradigm shift that will lead to changes of business models in many e. Today, industrial companies are gradually transforming their traditional and transaction-based business models into new business models made possible by cyber-physical systems. New business models such as as-a-service or platform-based business models emerge. This change brings enormous opportunities, but also many risks for the manufacturing industry. Many companies are faced with the problem of choosing from the multitude of new business models. The business model development to be found in literature primarily follows the needs of the customer. Machinery and equipment industry is a particularly interesting sector since more than 50% of the customers in machinery and equipment industry come from the same sector. This paper develops a process model for the industry-specific selection of business models. The model includes the following questions: Which business models can be selected in the field of machinery and equipment industry? Which possible goals can be pursued with the respective business models? Which criteria are useful for deciding on the respective business model

Information-based Preprocessing of PLC Data for Automatic Behavior Modeling

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing, analysis and modeling often cannot be entirely automated. Even machine- or deep learning approaches often depend on a priori expert knowledge and labelling. In this paper, an information-based data preprocessing approach is proposed. By applying statistical methods including variance and correlation analysis, an approximation of the sampling rate in event-based systems and the utilization of spectral analysis, knowledge about the underlying manufacturing processes can be gained prior to modeling. The paper presents, how statistical analysis enables the pruning of a dataset's least important features and how the sampling rate approximation approach sets the base for further data analysis and modeling. The data's underlying periodicity, originating from the cyclic nature of an automated manufacturing process, will be detected by utilizing the fast Fourier transform. This information-based preprocessing method will then be validated for process time series data of cyber-physical systems' programmable logic controllers (PLC)

Modelling The Digital Twin For Data-Driven Product Development - A Literature Review

Due to advanced connectivity and increasing distribution of product-service, more and more data is available from the products used and produced. Scientific publications often describe that this product data can be applied in product development to make it more efficient and that the digital twin can play a central role in data provision and interoperability. However, less attention is paid to how the digital twin should be designed for this purpose and how it should be adequately modelled for these use cases. Therefore, this paper presents a structured literature review to analyse which methods are already described in science to model digital twins in a target-oriented way for use cases of data-driven product development. Not only are the procedures interesting, but also the type of digital twin for which they are intended and whether they describe the procedure at the level of a rough macrostructure or detailed microstructure

Data-driven Prediction of Internal Turbulences in Production Using Synthetic Data

Production planning and control are characterized by unplanned events or so-called turbulences. Turbulences can be external, originating outside the company (e.g., delayed delivery by a supplier), or internal, originating within the company (e.g., failures of production and intralogistics resources). Turbulences can have far-reaching consequences for companies and their customers, such as delivery delays due to process delays. For target-optimized handling of turbulences in production, forecasting methods incorporating process data in combination with the use of existing flexibility corridors of flexible production systems offer great potential. Probabilistic, data-driven forecasting methods allow determining the corresponding probabilities of potential turbulences. However, a parallel application of different forecasting methods is required to identify an appropriate one for the specific application. This requires a large database, which often is unavailable and, therefore, must be created first. A simulation-based approach to generate synthetic data is used and validated to create the necessary database of input parameters for the prediction of internal turbulences. To this end, a minimal system for conducting simulation experiments on turbulence scenarios was developed and implemented. A multi-method simulation of the minimal system synthetically generates the required process data, using agent-based modeling for the autonomously controlled system elements and event-based modeling for the stochastic turbulence events. Based on this generated synthetic data and the variation of the input parameters in the forecast, a comparative study of data-driven probabilistic forecasting methods was conducted using a data analytics tool. Forecasting methods of different types (including regression, Bayesian models, nonlinear models, decision trees, ensemble, deep learning) were analyzed in terms of prediction quality, standard deviation, and computation time. This resulted in the identification of appropriate forecasting methods, and required input parameters for the considered turbulences

Opposites Attract: An Approach to Collaborative Supply Chain Management between Semiconductor and Automotive Companies

This article illustrates the differences between the semiconductor and the automotive industry and the subsequent challenges to their common supply chain. The weak points at the interfaces between the two supply chains will systematically be identified and assessed. Based on this analysis, a toolkit for collaborative supply chain planning and execution between the automotive and the semiconductor industry is presented. A fit/gap analysis assesses the measures and their potential to solve the supply chain challenges in a systematic manner. The model is built upon existing supply chain management frameworks and defines a set of specific optimization measures for the problem at hand. These are designed to ensure a better alignment of planning and control processes between the automotive and the semiconductor industry

Approach for Evaluating Changeable Production Systems in a Battery Module Production Use Case

Volatile markets continue to complicate manufacturing companies’ production system design, leading to efficiency losses due to imperfect system setups. In such a market environment, a perfect system setup cannot be achieved. Therefore, changeable production systems that cope with immanent uncertainty gain interest in research and industry. For several decades, changeable production systems have been in the research and development stage. The advantages and disadvantages are well investigated. So far, however, they have gained only limited acceptance in industry. One of the reasons is the difficult evaluation of the benefits. Existing investment calculation methods either neglect many effects of changeability, such as easier adaptation to unpredictable events, or are too complex and therefore too time-consuming to become standard. Thus, a practical evaluation method is needed that considers these changeability aspects. This paper deviates the industry requirements regarding an evaluation method based on an industry survey and develops a practical approach for an evaluation method for a changeable production system considering monetary and non-monetary aspects. The approach is characterized by a calculation that is as accurate as possible considering the existing input factors. The method shows that changeable production systems excel in environments with frequent need for adaptation. The approach is applied to a battery module assembly in the ARENA2036 research campus

Conceptualizing A Digital Twin Based On The Asset Administration Shell For The Implementation Of Use Case Specific Digital Services

In the context of ongoing digitalization, manufacturing companies face new challenges and the need to expand their portfolios to include new digital services. Enriching their portfolio with digital services can be an opportunity for manufacturing companies to position themselves in emerging collaborative production networks and thus make their business model fit for the future. The paper ties in with the activities from the Product Lifecycle Enrichment as a Service (PLCEaaS) research project. Within this context, use cases for digital services have already been derived, modelled, and documented in various workshops. The respective digital services directly address external customers and internal stakeholders from development. The central enabler for the new digital services is the digital twin based on the asset administration shell, which makes the necessary data available and thus supports interoperability. The asset administration shell is enriched with use case-specific submodels. The procedure for structuring these submodels is shown in this paper using the research project as an example. This includes modelling the digital services with a standardized modelling language based on the semi-structured use cases described so far. As a result, we obtain an asset administration shell enriched with several submodels - some of which may be based on standardization activities already underway or represent proprietary submodels. Likewise, it is considered whether more submodels are required to implement the domain-specific use cases that are currently not yet addressed in standardization activities. The paper ends with an outlook on the further research activities that are necessary to prototype the planned project and describes which criteria can be used to evaluate the defined submodels in the later course of the project

DesignChain: Process Automation From Recording Of Customer Requirements To Production Release

Growing price pressure due to an increasing number of global suppliers, increasing individualization of products and ever-shorter development cycles are challenges facing the engineering industry. In this context, mass personalization represents the customized production of customer products with batch size one at the low unit costs of mass production. The possibilities of digitalization and automation of technical order processing open up the opportunity for companies to significantly reduce their complexity costs and lead times and thus increase their competitiveness. Many companies already use a range of simulation tools and configuration solutions but only as stand-alones. Often, the expert knowledge of employees is hidden in "knowledge silos" and is rarely networked across processes. The concept "DesignChain" will address these challenges by automating and digitalizing technical process planning from recording customer requirements to releasing a product to the shop floor. Configurators within DesignChain allow for mapping variant-rich products. This transformation of customer requirements into product properties makes it possible to generate even complex CAD models, such as models for large-scale equipment based on specific rules. An automated CAx chain will help to digitally transfer production-relevant documents to the shop floor for parts fabrication. This process, which can be fully automated, allows for the customized creation of variants based on current approval statuses

Comparing Research Trends and Industrial Adoption of Manufacturing Operations Management Solutions

For decades, the operations on the shop floor of manufacturing organizations have been supported by Manufacturing Execution Systems. In this paper, we investigate the trends of Manufacturing Operations Management in the research community and analyze of the adoption in the industry. Our literature review identifies the following trends for Manufacturing Operations Management: distributed system architectures, cloud technology, and use of standards. We conducted a survey targeting Manufacturing Operations Management solution providers and adopters to explore the adoption of these trends. The survey results show that the use of standards is already addressed to some extent by the industry. Practitioners anticipate distributed system architectures for Manufacturing Operations Management solutions in the future. However, practitioners are still reluctant towards cloud-only technology and will continue to be so in the foreseeable future

Bridging The Gap: A Framework For Structuring The Asset Administration Shell In Digital Twin Implementation For Industry 4.0

The digital twin is a core technology for implementing Industry 4.0 scenarios in scientific and industrial applications. One upcoming variant of the digital twin is the concept of the asset administration shell, representing an approach to standardization. This approach must be adapted to specific use cases and applied in a target-oriented manner. However, no comprehensive guidance exists on structuring and implementing asset administration shells based on the digital twin in manufacturing environments. This issue pertains to defining and organizing the relevant data and mapping domain-specific limitations and characteristics within the hierarchical structure of the asset administration shell's components. This paper introduces an approach to structuring the asset administration shell to address this gap. This approach capitalizes on domain-specific expertise, industry standards, and established best practices, providing a framework. We validate the presented approach by applying it to the use case of distributed high-rate electrolyser production. The overarching objective of this research is to bridge the gap between theoretical concepts and practical applications