Evaluation Of Data-Based, Service-Oriented Business Models For Machine Tool Manufacturers

The potential of data utilization in the manufacturing industry enables companies to develop new service offerings for their customers. This creates an opportunity for manufacturers of machine tools and their components to implement innovative, service-oriented business models. These business opportunities can open new sources of revenue and optimize the economic performance of a company. A development of innovative, data-based and service-oriented business approaches involves a number of paradigm shifts for traditionally product-centred machine tool manufacturers. However, a large proportion of companies in the industry only have very limited know-how in data utilization. As a result, most of the projects initiated in the industry to develop data-based service offerings fail. Besides problems with the available database, a lack of understanding of the business approach and a failure to assess the entrepreneurial feasibility of offerings are the main causes of failure. This paper presents an approach to evaluating data-based, service-oriented business models for machine tool manufacturers. Starting with an overview of the state of the art in business models and data-based applications in production, the special features of data-based business models are examined. In doing so, the paradigm shift from an analogue, product-centric to a data-based, service-oriented business model is highlighted. Based on this, requirements for the method are derived and its place in the development process of business models is defined. After a presentation of the developed method, it is finally applied in a practical example of a manufacturer for milling machines

Analyzing Inter-Temporal Correlations Of KPIs For Cross-Value Stream Performance Management

Key performance indicators (KPIs) play a pivotal role in assessing and optimizing industrial processes, addressing dimensions such as time, quality, and cost. However, unclear relationships and dependencies among individual KPIs impede efficient performance management. To ensure that optimizing individual goals do not come at the expense of others, recognizing and elucidating these relationships becomes imperative. This paper introduces a decision support system capable of identifying, filtering, and presenting inter-temporal correlations in KPIs. The system is based on the general structure of assistance systems and consists of a data storage layer, an application layer and a presentation layer. Inter-temporal correlations refer to relationships between variables across different time periods. The advantage of identifying these inter-temporal correlations is that they allow time for countermeasures to be taken. As a result, the system enables a transition from a reactive to a proactive deviation management approach. The practical applicability of the system is demonstrated by using a dataset from industry

A Concept To Support Decision-Making In Inspection Processes of Circular Economy

The consequences of climate change and current economic and political crises are presenting manufacturing companies with ever greater challenges. On the one hand, increasingly stringent regulations regarding environmental sustainability must be complied with. On the other hand, the use of resources must be adapted to increasingly unstable global supply chains. The application of circular economy strategies holds great potential in terms of business resilience and sustainability. However, despite being highly effective, circular processes have so far only been implemented in very few discrete manufacturing companies. A key component in the optimization of processes towards an economically viable circular economy lies around inspection processes. The information gained about the product’s condition during the inspection process forms the basis for a decision to select suitable subsequent treatment strategies. Decision-making processes are mainly based on the experience of experts gained over many years. Due to the increasing shortage of skilled workers, it is therefore important to record and systematically standardize this knowledge to assist unskilled employees in inspection processes in the future. Therefore, this paper presents an approach to support decision-making processes in industrial diagnosis processes. First, the basic concepts of assistance systems and circular economy, as well as the associated decision-making processes, are presented. Finally, the developed concept for decision support is introduced and transferred to a practical application

Using Metrics For The Assessment Of Human Interaction With Worker Assistance Systems

Factories often include worker assistance systems to support assembly through cognitive guidance, connected tools and sensors. The benefits are improvements in productivity and quality. While the measurement and display of productivity and quality is widely spread, the underlying human interaction with worker assistance systems is not yet established to be assessed in key performance indicators. Those metrics are necessary to measure the level of human-centered design of assembly stations and worker assistance systems. The metrics are meant to yield detailed insights on the information perception of workers and the resulting work activities. Besides the discovery of needs for reconfiguration of worker assistance systems and assembly stations, the metrics can also be used as a basis for comparison to quantify differences between multiple setups. This paper investigates metrics for the assessment of human-centered assembly station design and worker assistance systems. Therefore, multiple categories of metrics on human-technology-interaction are derived from literature and contextualized for worker assistance systems. For validation, selected metrics are implemented at an industrial-grade assembly station for an electric motor replica in a learning factory. The findings in the validation allow the assessment of the information perception and the work activities regarding material picking, handling and ergonomics

Digital Assistance Systems to Implement Machine Learning in Manufacturing: A Systematic Review

Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to overcome this competence gap and let domain experts with limited machine learning programming skills build viable applications are digital assistance systems that support the implementation. At the present, there is no comprehensive overview over corresponding assistance systems. Thus, within this study a systematic literature review based on the PRISMA-P process was conducted. Twenty-nine papers were identified and analyzed in depth regarding machine learning use case, required resources and research outlook. Six key findings as well as requirements for future developments are derived from the investigation. As such, the existing assistance systems basically focus on technical aspects whereas the integration of the users as well as validation in industrial environments lack behind. Future assistance systems should put more emphasis on the users and integrate them both in development and validation

Modelle und Strategien zur Einführung des Computer Integrated Manufacturing (CIM) – Ein Literaturüberblick

Deutsch: Im Rahmen der seit einigen Jahren verstärkten Aktivitäten von Wissenschaft und Wirtschaft im Bereich der digital vernetzten Produktion kann eine Auseinandersetzung mit den Erfahrungen und Erkenntnissen von Computer Integrated Manufacturing (CIM) hilfreich sein. Hierfür werden in diesem Beitrag 37 CIM-Modelle kurz beschrieben, weiterführende Literaturhinweise angegeben und die Visualisierungen der Grundstrukturen abgebildet. Darüber hinaus werden Literaturhinweise zu einigen Planungs- und Umsetzungsstrategien im Kontext CIM gegeben sowie der Stand verfügbarer CIM-Reifegradmodelle dargestellt. English: Within the framework of the activities of science and business in the area of digitally networked production, which has been intensified for a number of years, an examination of the experiences and knowledge of Computer Integrated Manufacturing (CIM) can be helpful. For this purpose, 37 CIM models are briefly described in this article, additional literature references are given and the visualisations of the basic structures are depicted. In addition, there are references to some planning and implementation strategies in the CIM context, as well as the status of available CIM maturity models

A guide to develop competency-oriented Lean Learning Factories systematically

In the last decade, various lean learning factories were established in industry and academia around the globe. They are used for experience-based training, education, and practice-oriented research. Learning factories provide a reality-conform production environment as a learning environment. Processes and technologies of the learning factory are based on real industrial sites. Learning factories doesn't only contain single workplaces or machines, but changeable multilink value added chains. Trainees can test and discover lean approaches in this environment and experience the holistic range of technological, organizational, and social issues linked to the approaches. The main goal of learning factories is an effective competency development, i.e. the development of the participants’ ability (including motivational and emotional aspects) to master complex, unfamiliar situations. In order to reach this goal a systematic approach for the competency-oriented design of learning factory courses and systems is needed. Such a competency-oriented approach for the development of lean learning factories is presented, integrating the conceptual design levels ‘learning factory’, ‘teaching module’, and ‘learning situation’. This approach addresses issues of intuitively designed learning factories and therefore enables an effective development of intended competencies. As a result lean learning factories including teaching modules and learning situations meeting the requirements of industry can be designed with a reduced effort and an increased success in the transfer to real problem situations. Among others, a case study of designing a learning module in the environment of the process learning factory CiP in the field of "Lean Quality" is presented in detail

Old-Fashioned Pep Rallies to Spotlight Football, Soccer; UD Grad, Bar Code Inventor Returns to Alma Mater; JFK Statue Comes Home in Time for Homecoming; 300 Runners Expected for Homecoming 5k Race

News release announces that two pep rallies will be held during Homecoming Weekend; Paul McEnroe will talk about his work to develop the bar code; The restored JFK statue will be unveiled; The annual Thomas J. Frericks 5K run/walk will be held

An Approach For Analysis Of Human Interaction With Worker Assistance Systems Based On Eye Tracking And Motion Capturing

Human behavior in production systems influences productivity, product quality, work safety and overall process performance. To guide human behavior, digital worker assistance systems can be used to support cognitive decision tasks and sensory perception tasks. In doing so, the design of the assistance systems affects user experience and work results. To optimize and develop human-centric productions systems, data on human behavior and interaction with manufacturing equipment must be collected and analyzed. This analysis is expected to yield benefits regarding process monitoring, quality assurance, user experience and ergonomics. In addition, the results could be used for training purposes to monitor skill improvements. This paper presents a framework for data acquisition and analysis of human interaction with digital worker assistance systems. In addition to the overall system architecture, the individual development steps are discussed. An eye tracking device and a motion capturing camera are used for data collection and provide live information about human behavior in conjunction with a digital worker assistance system. The data is stored in a database and analyzed by custom analysis algorithms. The results are displayed in a dashboard application and show that the presented framework with eye tracking and motion capturing is suitable for the analysis of human interaction with worker assistance systems

Traceability System’s Impact On Process Mining in Production

From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. Market forces such as rising amounts of product variants and decreasing batch sizes lead to higher complexity in manufacturing processes. Therefore, production management’s demand for data-based process transparency is growing continuously as well as the number of companies turning to process mining to address these challenges. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency