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The successful use of Business Analytics is increasingly becoming a differentiating competitive factor. The ability to extract data-driven insights and integrate them into decision-making is becoming growingly important. The underlying technologies are evolving exponentially, the value proposition differs from simple descriptive applications to automated decision-making. Existing approaches found in literature and practice to classify those levels only insufficiently mark down the boundaries between the different technology levels. As a consequence, it is often unclear which characteristics of the technology interact with the working environment, which can be described as a socio-technical system. Using a systematic literature review, this paper identifies the characteristics of Business Analytics and delineates three types of Business Analytics based on case studies. Thus, a starting point for the socio-technical system design and optimization for the use of Business Analytics is created.
Competitive differentiation in the manufacturing sector is no longer based on product and service innovations alone but on the ability to monetize the usage phase of products and services. To this end, manufacturers are increasingly looking at so-called subscription business models as a way of supplementing the traditional sale of products and services. Since supplier success in the subscription business is directly dependent on customer success, the setup and expansion of a so-called Customer Success Management (CSM) is required. While CSM has already been established in the software industry for several years, companies in the manufacturing sector are often still in the conceptual phase of a CSM, parallel to the setup and expansion of their subscription business. Therefore, this paper aims to support the set-up of a CSM by providing a reference data model, based on case study research, that can be used to support the organizational or daily CSM tasks and to serve as a blueprint for conceptualizing CSM-specific IT systems.
The use of chatbots has hardly been established in B2B companies to date and involves various challenges. The goal of this paper is to identify the biggest barriers to the successful implementation of chatbots in B2B customer service and to develop measures to overcome them. The barriers are identified by conducting expert interviews within the framework of Eisenhardt's case study research. These are examined through a socio-technical analysis focusing on people, technology, and organization. By means of systematic literature research and in-depth interviews with German chatbot providers and customers of chatbots, measures for overcoming the barriers are identified. Using interviews with experts from German chatbot providers, the responsible stakeholders of each measure according to the RASCI Responsibility Matrix are determined. A total of 46 implementation barriers and 100 measures to overcome these barriers are identified. The study shows that there are major barriers in the areas of people, technology, and organization of a socio-technical system that can cause the implementation of a chatbot to fail. A holistic view is therefore essential. The results provide firms with a guideline on how to overcome potential barriers during chatbot implementation in B2B customer service.
Forecasting-based skills management, which is oriented to the respective corporate goals, is gaining enormous importance as a central management tool. The aim is to predict future skills requirements and match them with existing interorganizational skills. Companies are required to anticipate changes in markets, industries, and technologies at an early stage as well as to identify changes in job profiles within an occupational profile by tapping into and evaluating various data sources. Based on these findings, they can then make informed decisions regarding skill gaps, for example, to implement targeted further training measures. Forecasting-based skills management offers the opportunity to optimally qualify employees for constantly changing tasks. At the same time, however, the targeted development of such skills requires a high level of time, financial and personnel resources, which small and medium-sized enterprises (SMEs) generally do not have at their disposal. In addition, many SMEs are not yet aware of the importance of this issue. Within the framework of research and industrial projects of the Smart Work department at the FIR (Institute for Industrial Management) at the RWTH Aachen University, an AI-based skills forecasting tool will be developed. The goal of the paper is to conceptualize the future machine learning method, that is able to generate individualized skills forecasts and recommendations for SMEs. This is achieved by linking societal forecasts and sector trends with company-specific conditions and skills. In order to generate a corresponding database, the derivation system is made available to various companies (large companies and SMEs) in order to obtain as many data sets as possible. The data sets obtained via the derivation system are then used as training data sets for the machine learning method, with the help of which an automatic derivation of competencies depending on new trends is to be made possible.
Digitization is constantly affecting the working world and is of enormous interest in many fields of science. But to what extent are innovative technologies actually being applied in regional SMEs and what are the obstacles to their introduction? From a psychological point of view, it is essential to consider the employee's health and the effects of innovative technologies on their everyday work. The aim of using innovative technologies should not be to completely replace human labor or to dequalify employees, but to relieve the workforce and free up working time for more meaningful activities. One concept that should be included in the human-centered design of human-machine interaction in artificial intelligence is the HAI-MMI concept (Huchler, 2020), which offers starting points for high-quality collaboration at various levels. To reduce the gap between science and industry, this paper focuses on the actual demands of SME in the Aachen region in Germany referring to a requirements analysis within the research project AKzentE4.0 (N = 50 SME) and discusses how appropriate innovative technologies of the Industry 4.0 and AI can be implemented and deployed in a human-centred way. Moreover, the establishment of a Human Factors Competence Center for Employment in Industry 4.0 is outlined, which is meant to be used for the dissemination of research results from the project and should narrow the gap between science and industry in the long run.
For developing a European industrial cooperation and involvement in the furniture industry, the international research project INEDIT conducted a survey for furniture customers. By finding out the needs and wishes of the customer regarding innovative products and the production process the project will establish a new way for designing and producing furniture. Within INEDIT a platform is built on which customized, technologically innovative and sustainable furniture can be created and produced in a co-creation process. The furniture industry should thus become significantly more flexible, transparent and sustainable. Following the "do-it-together" approach, a business ecosystem will be generated which creates added value not only for customers but also for designers, suppliers and manufacturing companies. In order to involve the customer even more actively in the design process and the production, the platform will provide access to a mix of digital and physical services and is linked to all other stakeholders in the value chain. To match the platform and the process to the needs, wishes and demands of the customer an anonymous survey with 300 participants was developed and conducted. By analyzing the survey, important factors were found for buying and for using furniture considering new technological inventions (e.g. 3D-printing or smart objects), sustainability of the products and the production process. Furthermore, the potential customer-group and their usage of the do-it-together process and additional activities can be tightened.
Systematisation Approach
(2023)
Current megatrends such as globalisation and digitalisation are increasing complexity, making systems for well-founded and short-term decision support indispensable. A necessary condition for reliable decision-making is high data quality. In practice, it is repeatedly shown that data quality is insufficient, especially in master and transaction data. Moreover, upcoming approaches for data-based decisions consistently raise the required level of data quality. Hence, the importance of handling insufficient data quality is currently and will remain elementary. Since the literature does not systematically consider the possibilities in the case of insufficient data quality, this paper presents a general model and systematic approach for handling those cases in real-world scenarios. The model developed here presents the various possibilities of handling insufficient data quality in a process-based approach as a framework for decision support. The individual aspects of the model are examined in more detail along the process chain from data acquisition to final data processing. Subsequently, the systematic approach is applied and contextualised for production planning and supply chain event management, respectively. Due to their general validity, the results enable companies to manage insufficient data quality systematically.
Objectives and Key Results (OKR) is an approach that focuses on the company's goals through trust-based agreements between leaders and employees. With the OKR framework in its original form, strategic business goals are aligned with the employees' active involvement, which promotes intrinsic motivation, transparency, commitment, and alignment. Inspired by the successes at Google and Intel and shaped by its use in the tech industry, the use of OKR increased across industries. Although companies within all sectors use the OKR framework, numerous implementation efforts fail. The challenges of practitioners are not fully addressed in the development of implementation concepts for OKR. One main reason is that these challenges are not taken into account in scientific publications. The paper aims to investigate to what extent existing OKR frameworks need to be adapted to provide companies with suiting implementation guidance. Firstly, OKR is placed in the context of academically widely discussed Performance Management Systems (PMS).
Secondly, criteria for successful PMS implementation are identified and used as a baseline for analyzing existing OKR implementation concepts. A systematic literature review shows the current state of research, identifying existing OKR implementation concepts from practice and theory. The OKR implementation concepts identified are systematically mapped to the series of identified criteria for PMS implementation. It is shown that the existing OKR frameworks do not address the described criteria necessary for a successful implementation of PMS, thus the adaptation of existing OKR implementation concepts is required.
The quarrying industry, which largely consists of less digitized SMEs, is an integral part of the German economy. More than 95% of the primary raw materials produced are used by the domestic construction industry. Quarrying companies operate demand-oriented with short planning horizons at several locations simultaneously. Due to the low level of digitization and the reluctance to share data, untapped efficiency potential in data-based demand forecasting and capacity planning arises. The situation is aggravated by the fact that SMEs have a heterogeneous mobile machinery so as not to become dependent on individual suppliers, and that transport distances of over 50 kilometers are uneconomical due to high transport costs and low material values. Within the research project PROmining a data-centric platform which improves demand forecast accuracy and multi-site capacity utilization is developed. One of the core functionalities of this platform is an industry-specific demand forecasting model. Against this background, this paper presents a methodology for establishing this forecasting model. To this end, expected demands of secondary industry sectors will be analyzed to improve mid-term volume-forecasting accuracy for the local quarrying industry. The data-centric platform will connect demand forecasting data with relevant key performance indicators of multi-site asset utilization. Following this methodology, operational planning horizons can be extended while significantly improving overall production efficiency. Thus, quarrying businesses are enabled to respond to fluctuating demand volumes effectively and can increase their personnel and machine utilization across multiple quarry sites.
The use of Business Analytics (BA) helps to improve the quality of decisions and reduces reaction latencies, especially in uncertain and volatile market situations. This expectation leads a continuously rising number of companies to make large investments in BA. The successful use of Business Analytics is increasingly becoming a differentiator. At the same time, the use of BA is not trivial, rather, it is subject to high socio-technical requirements. If these are not addressed, high risks arise that stand in the way of successful use. In particular, it is important to consider the risks in relation to the different types of BA in a differentiated way. So far, there is a lack of suitable approaches in the literature to consider these type-specific risks with regard to the socio-technical dimensions: people, technology, and organization. This paper addresses this gap by initially identifying risks in the use of Business Analytics. For this purpose, possible risks are identified using a systematic literature review and verified with a Delphi survey with various partners experienced in dealing with BA. Subsequently, the identified and validated risks are assigned to three different types of Business Analytics (Descriptive, Predictive and Prescriptive Analytics) and assessed in order to systematically address and reduce the risks. The result of this paper is an overview of the interactions between the socio-technically assigned risks, summarized in a risk catalog, and the different types of Business Analytics.
5G offers the manufacturing industry a wireless, fast and secure transmission technology with high range, low latency and the ability to connect a large number of devices. Existing transmission technologies are reaching their limits due to the increasing number of networked devices and high demands on reliability, data volume, security and latency. 5G fulfills these requirements and also combines the potential and use cases of previous transmission technologies so that unwanted isolated solutions can be merged. Use cases of transmission technologies that previously required a multitude of solutions can now be realized with a single technology. However, the general literature often refers to 5G use cases that can also be realized over cables in particular. In this paper, a literature review presents the current state of research on the various 5G application scenarios in production . Furthermore, concrete characteristics of 5G use cases are identified and assigned to the identified application scenarios. The goal is to verify the identified 5G use cases and to work out their 5G relevance in order to be able to concretely differentiate them from already existing Industrie 4.0 applications.
Crises are becoming more and more frequent. Whether natural disasters, economic crises, political events, or a pandemic - the right action mitigates the impact. The PAIRS project plans to minimize the surprise effect of these and to recommend appropriate actions based on data using artificial intelligence (AI). This paper conceptualizes a cascading model based on scenario technique, which acts as the basic approach in the project. The long-term discipline of scenario technique is integrated into the discipline of crisis management to enable short-term and continuous crises management in an automated manner. For this purpose, a practical crisis definition is given and interpreted as a process. Then, a cascading model is derived in which crises are continuously thought through using the scenario technique and three types of observations are classified: Incidents, disturbances, and crises. The presented model is exemplified within a non-technical application of a use case in the context of humanitarian logistics and the COVID-19 pandemic. Furthermore, first technical insights from the field of AI are given in the form of a semantic description composing a knowledge graph. In summary, a conceptual model is presented to enable situation-based crisis management with automated scenario generation by combining the two disciplines of crisis management with scenario technique.
Innovation is one of the key drivers of growth, development, and profitability, which increases competitive advantages and has recently been moving towards industry 4.0 technologically. This motivates companies to update their business models (BM) towards industry 4.0. Moreover, there is a technique with the primary characteristics for achieving this motivation called "cross-industry innovation". Cross-industry innovation is a new method of innovation that concerns the creative translation and imitation of existing solutions from other industries for responding to the needs of the current market, sectors, areas, or domains. The challenge is to find out how far managers can rely on that to innovate their BM towards Industry 4.0. The aim of this study was to investigate the application of cross-industry innovation for designing industry 4.0 BM and explore the extent to which companies can rely on it as it has not been used for this purpose previously. This study utilized a database analysis to compare cross-industry innovation practices with industry 4.0 BM's characteristics in terms of value proposition, value creation, and value capture levels. In addition, some interviews were conducted with companies that had previously implemented cross-industry innovation to validate and generalize the results. The results indicated that cross-industry innovation practices can better fulfill flexible and dynamic networks, connected information flows, high efficiency, high scalability, and high availability in terms of value creation as well as variabilization of prices and costs in terms of value capture. Therefore, it demonstrated that cross-industry innovation was a more dependable and applicable strategy for designing the BM of Industry 4.0 than current practices.
Development of a platform business model for co-creation ecosystems for sustainable furniture
(2023)
Existing design platforms with multi-dimensional value chains currently have deficits in terms of their business models, resulting in insufficient attention to sustainability goals and individual requirements for products of these platforms. Co-creation approaches, such as the Do-It-Together (DIT) approach for furniture, involve customers and manufacturers as equal partners in the design and production process. This allows customers to have more influence on the sustainability and individualization of products. The existing literature addresses sustainability-oriented design principles for platform business models, but concrete platform business models for multidimensional DIT cocreation of furniture are still missing. Therefore, the objective of this paper is to develop a business model for a DIT co-creation platform for the furniture industry based on a four-step business model innovation framework. This method will then be applied to a specific project scenario to derive a project-specific DIT co-creation business model. This generates knowledge about the collaborative manufacture of sustainable and customized furniture and contributes to the cross-sectoral transfer of platform business models for the development of sustainable products.
The manufacturing industry consumes 54% of global energy and attributes for 20% of global CO2 emissions, demonstrating the industry’s role as global driver of climate change. Therefore, reducing its carbon footprint has become a major challenge as its current energy and resource consumption are not sustainable. Industrie 4.0 presents a chance to transform the prevailing paradigms of industrial value creation and advance sustainable developments. By using information and communication technologies for the intelligent networking of machines and processes, it has the potential to reduce energy and material consumption and is considered a key contributor to sustainable manufacturing as proclaimed by the European Commission in the term “twin transition”. As organizations still struggle to utilize the potential of Industrie 4.0 for a sustainable transformation, this paper presents a framework to successfully align their own twin transition. The framework is built upon three key design principles (micro level: leverage eco-efficient operations, meso level: facilitate circularity and macro level: foster value co-creation) derived using case study research by Eisenhardt, and four structural dimensions (resources, information systems, organizational structure and culture) based on the acatech Industrie 4.0 Maturity Index. Eleven interconnected areas of action are defined within the framework and offer a holistic and practical approach on how to leverage an organization’s twin transition. Within the conducted research, the framework was applied to the challenge of information quality and transparency required for high-value secondary plastics in the manufacturing industry. The result is a digital platform design that enables information transactions for secondary plastics and establishes a circular ecosystem. This shows the applicability of the framework and its potential to facilitate a structured approach for designing twin transitions in the manufacturing industry.
Die Vernetzung von Mitarbeiter*innen und Maschinen sowie die zunehmende Automatisierung, auch von Wissensarbeit, wird die Rolle der Beschäftigten im industriellen Wertschöpfungsprozess fundamental verändern. Aus diesem Grund ist arbeitsbezogene Kompetenzentwicklung aus wirtschaftlicher, gesellschaftlicher sowie sozialer Perspektive ein zentraler Schlüsselaspekt für die mittelfristige Sicherung der Wettbewerbsfähigkeit. Personalabteilungen haben bislang jedoch meist nur bedingt Kenntnisse über die bevorstehenden Veränderungen und die sich daraus ergebenden Kompetenzanforderungen an die Mitarbeiter*innen. Ziel des Forschungsvorhabens LidA war es, die sich aufgrund der fortschreitenden Digitalisierung verändernden Kompetenzanforderungen entlang definierter Industrie-4.0-Reifegradmodelle zu spezifizieren. Hierzu wurden Beschäftigte befähigt, indem zum einen ihre Selbstlernkompetenz gefördert wurde und zum anderen individuelle Lernpfade abgeleitet worden sind. Anschließend wurden diese mit passender Didaktik in Lehr- und Lernmodule überführt und auf einer bewährten Open-Source-Plattform für eine breite Nutzergruppe verfügbar gemacht. Diese soll einem breiten Nutzerkreis, speziell KMU, eine bedarfsgerechte Schulung der Mitarbeiter*innen im Zeitalter des digitalen Wandels gewährleisten.
Long-term production management defines the future production structure and ensures the long-term competitiveness. Companies around the world currently have to deal with the challenge of making decisions in an uncertain and rapidly changing environment. The quality of decision-making suffers from the rapidly changing global market requirements and the uniqueness and infrequency with which decisions are made. Since decisions in long-term production management can rarely be reversed and are associated with high costs, an increase in decision quality is urgently needed. To this end, four different applications are presented in the following, which support the decision process by increasing decision quality and make uncertainty manageable. For each of the applications presented, a separate digital shadow was built with the objective of being able to make better decisions from existing data from production and the environment. In addition, a linking of the applications is being pursued:
The Best Practice Sharing App creates transparency about existing production knowledge through the data-based identification of comparable production processes in the production network and helps to share best practices between sites. With the Supply Chain Cockpit, resilience can be increased through a data-based design of the procurement strategy that enables to manage disruptions. By adapting the procurement strategy for example by choosing suppliers at different locations the impact of disruptions can be reduced. While the Supply Chain Cockpit focuses on the strategy and decisions that affect the external partners (e.g., suppliers), the Data-Driven Site Selection concentrates on determining the sites of the company-internal global production network by creating transparency in the decision process of site selections. Different external data from various sources are analyzed and visualized in an appropriate way to support the decision process. Finally, the issue of sustainability is also crucial for successful long-term production management. Thus, the Sustainable Footprint Design App presents an approach that takes into account key sustainability indicators for network design. [https://link.springer.com/referenceworkentry/10.1007/978-3-030-98062-7_15-1]
Vor dem Hintergrund zunehmend komplexer und vernetzter Wertschöpfungsnetzwerke und in Zeiten sich ständig verändernder Rahmenbedingungen steigt für Unternehmen die Bedeutung einer resilienten Gestaltung ihrer Wertschöpfungsnetzwerke. Durch die hohe Vernetzung in einem Wertschöpfungsnetzwerk entsteht eine starke Abhängigkeit zwischen den einzelnen Akteuren. Störungen haben somit häufig nicht nur Auswirkungen auf einzelne Unternehmen, sondern betreffen verschiedene Akteure der Wertschöpfungsnetzwerke. Tritt nun eine Störung auf, kann sich diese im gesamten Netzwerk ausbreiten. Erst der konkrete Eintritt solcher Störungen im großen Umfang – wie zuletzt im Zuge der Corona-Pandemie oder der Blockierung des Suez-Kanals – führt Unternehmen regelmäßig dazu, sich mit ihren Wertschöpfungsnetzwerken auseinander zu setzen. Eine Möglichkeit zur Sicherung der Leistungsfähigkeit in einem volatilen Umfeld stellt der Aufbau von Resilienz dar. Insgesamt ist es hierbei das Ziel, Wertschöpfungsnetzwerke so zu gestalten, dass sie im Falle einer Störung möglichst wenig beeinträchtigt sind und schnell in den ursprünglichen oder einen besseren Zustand zurückkehren können.
Europa als erster klimaneutraler Kontinent bis 2050 – unter diesem ambitionierten Ziel treibt die Europäische Union eines der größten Transformationsprogramme dieses Jahrhunderts voran. Das Leben und die Gesellschaft wie sie heute existiert, werden in allen Bereichen signifikanten Musterwechseln unterliegen. Von zentraler Bedeutung bei dieser Transformation wird die Mobilität von Personen und Gütern sein. Eine Reduktion von 90 % der Treibhausgasemissionen soll in weniger als drei Dekaden realisiert werden. Insbesondere im Bereich der Urbanen Logistik ist ein nahtloses Zusammenspiel der verschiedensten Akteure, unterstützt durch neuartige digitale und physische Infrastrukturen, notwendig, um eine nachhaltige Zielerreichung bei mindestens konstantem Serviceniveau sicherzustellen. Cross-industrielle Ansätze, die über das Zusammenspiel von komplementären Lösungsbausteinen Co-Creation ermöglichen, werden zum zentralen Wettbewerbsvorteil für alle Akteure. Die Gestaltung von Business Ecosystems rückt deshalb zunehmend in den Fokus und wird aufgrund des enormen Potenzials für die Urbane Logistik in diesem Beitrag beleuchtet.
Anwendungsfälle wie intelligente Routenoptimierung und fortschrittliche Simulationsalgorithmen repräsentieren das riesige Einsatzspektrum von Methoden der künstlichen Intelligenz. Steigende Anforderungen an Liefertermintreue, Flexibilität und Transparenz wie bspw. Emissionsverfolgung, erfordern zunehmend den Einsatz von KI. Die Nutzung dieser Schlüsseltechnologie und die Hebung der Potenziale scheitern oft an der Komplexität in Bezug auf die Eingrenzung und Identifikation von wirtschaftlich relevanten Anwendungsfällen. Unternehmen müssen den Business Fit zwischen den wirtschaftlichen Erfolgsaussichten und den dafür benötigten digitalen Bausteinen herstellen. Mit dem Digital-Architecture Management lassen sich die relevanten KI-basierten Anwendungsfälle identifizieren und eine Roadmap aufbauen, um die datenbasierte Entscheidungsfähigkeit in der Logistik zu verbessern.
Feeding the growing world population is a scientific and economic challenge. The target variables to be optimised are the yield that can be produced on a given area and the reduction of the resources used for this purpose. High-wage countries are faced with the problem that the use of personnel is a significant cost driver. Developing countries, on the other hand, usually operate on much smaller field sizes, so that the work in the field is still strongly characterised by manual labour. One solution to meet these challenges is the use of smaller autonomous harvesting robots. These can be networked into a swarm of machines to work even larger fields. The networking of autonomous agricultural machines is a key use case for rural 5G networks. 5G technology can offer many advantages over older mobile communications standards and therefore make use cases more efficient or enable new ones. Various use cases are also conceivable in the field of agriculture, yet it is unclear how 5G networks can and must be specified for this purpose. In this paper, using the example of 5G-connected harvesters powered by swarm robotics, we present the challenges that have arisen and the specification that has been developed.
In road haulage, transports are interrupted by truck drivers to comply with driving and rest times. On long-distance routes, these interruptions lead to a considerable increase in transport time. Transport interruption can be avoided by so-called relay traffic: a vehicle (e. g. semi-trailer) is handed over to a rested driver at the end of the driving time. This type of transport requires a certain company size. In Germany, however, transport companies have 11 employees on average. Intra-company relay traffic is therefore not economically viable for most transport companies. To organize an intermodal transport across forwarding companies, long-distance routes need to be split into partial routes to divide them between freight forwarders and carriers. This paper presents a data concept for an algorithm to find the best possible route sections along a previously defined start and endpoint. The developed data concept includes order-specific data, forwarder-specific data, real-time traffic data, geographical data as well as data from freight forwarding software and telematics to be the basis for the route sectioning algorithm. In this paper, different data sources, external services and logistic systems are analyzed and evaluated. It is shown which data is needed and what the best ways are to select and derive this data from the different data sources.
Due to shorter product life cycles and the increasing internationalization of competition, companies are confronted with increasing complexity in supply chain management. Event-based systems are used to reduce this complexity and to support employees' decisions. Such event-based systems include tracking & tracing systems on the one hand and supply chain event management on the other. Tracking & tracing systems only have the functions of monitoring and reporting deviations, whereas supply chain event management systems also function as simulation, control, and measurement. The central element connecting these systems is the event. It forms the information basis for mapping and matching the process sequences in the event-based systems. The events received from the supply chain partner form the basis for all downstream steps and must, therefore, contain the correct data. Since the data quality is insufficient in numerous use cases and incorrect data in supply chain event management is not considered in the literature, this paper deals with the description and typification of incorrect event data. Based on a systematic literature review, typical sources of errors in the acquisition and transmission of event data are discussed. The results are then applied to event data so that a typification of incorrect event types is possible. The results help to significantly improve event-based systems for use in practice by preventing incorrect reactions through the detection of incorrect event data.
Companies operate in an increasingly volatile environment where different developments like shorter product lifecycles, the demand for customized products and globalization increase the complexity and interconnectivity in supply chains. Current events like Brexit, the COVID-19 pandemic or the blockade of the Suez canal have caused major disruptions in supply chains. This demonstrates that many companies are insufficiently prepared for disruptions. As disruptions in supply chains are expected to occur even more frequently in the future, the need for sufficient preparation increases. Increasing resilience provides one way of dealing with disruptions. Resilience can be understood as the ability of a system to cope with disruptions and to ensure the competitiveness of a company. In particular, it enables the preparation for unexpected disruptions. The level of resilience is thereby significantly influenced by actions initiated prior to a disruption. Although companies recognize the need to increase their resilience, it is not systematically implemented. One major challenge is the multidimensionality and complexity of the resilience construct. To systematically design resilience an understanding of the components of resilience is required. However, a common understanding of constituent parts of resilience is currently lacking. This paper, therefore, proposes a general framework for structuring resilience by decomposing the multidimensional concept into its individual components. The framework contributes to an understanding of the interrelationships between the individual components and identifies resilience principles as target directions for the design of resilience. It thus sets the basis for a qualitative assessment of resilience and enables the analysis of resilience-building measures in terms of their impact on resilience. Moreover, an approach for applying the framework to different contexts is presented and then used to detail the framework for the context of procurement.
The environment in which companies operate is increasingly volatile and complex. This results in an increased exposure to disruptions. Past disruptions have especially affected procurement. Thus, companies need to prepare for disruptions. The preparedness for disruptions in the context of procurement is significantly influenced by the design of the procurement strategy. However, a high number of purchased articles and a variety of influencing factors lead to high complexity in procurement. The systematic design of the procurement strategy should therefore take into account the criticality of the purchased articles. This enables to focus on the purchased articles that have a high impact on the disruption preparedness. Existing approaches regarding the design of the procurement strategy in uncertain environments either lack practical applicability and objective evaluation or focus on the criticality of raw materials rather than of purchased articles. Therefore, a data-based approach for the systematic design of the procurement strategy in the context of the Internet of Production has been proposed. One central aspect of this approach is the identification of success-critical purchased articles. Thus, this paper proposes a framework for characterizing purchased articles regarding supply risks by combining two systematic analyses. First, a systematic literature review is performed to answer the question of what factors can be used to describe the supply risks of purchased articles. The results are analyzed regarding sources and impacts of risks and thus contribute to a structured characterization of supply risks. Second, existing criticality assessment approaches for raw materials are analyzed to identify categories and indicators that describe purchased articles. The results of both reviews provide the basis for linking product characteristics with supply risks and assessing product criticality which will be integrated into an app prototype.
Industry 4.0 and Smart Maintenance represent a great opportunity to make manufacturing and maintenance more effective, safer, and reliable. However, they also represent massive change and corresponding challenges for industrial companies, as many different options and starting points have to be weighed and the individual right paths for achieving Smart Maintenance need to be identified. In our paper, we describe our approach to evaluating maintenance organizations in a case study for the oil and gas industry, developing a shared vision for the future, and deriving economical and effective measures. We will demonstrate our approach, by showcasing a specific example from the oil and gas industry, where a need for action on HSE-relevant critical flanges in the company's piping systems was identified. We describe the steps, that were taken to identify the need for action, the specifications of the project and the criticality analysis of the piping system. This resulted in the derivation of a digitalization measure for critical flanges, which was first commercially analyzed and then the flanges were equipped with a continuous monitoring solution. Finally, a conclusion is drawn on the performed procedure and the achieved improvements.
Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT’s, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT’s have been suggested.
However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech’s Industry 4.0 Maturity Index: Resources, Information Systems, Organizational Structure and Culture.
Ongoing digitalization and Industry 4.0 enable the development of new business models due to the increase in available data and digital connected products. A promising business model type for the machinery and plant engineering industry are subscription models, consisting of products and services offered in return for continuous payments. However, subscription-based business models are associated with extensive changes in the traditional machinery and plant engineering industry, in particular, for small and medium-sized companies (SMEs). Established concepts for the development of value propositions and business models neglect important aspects, such as the integrated development and optimization of products and services across the entire life cycle or the data infrastructure. This paper presents a concept for a methodology to support SMEs developing value propositions within subscription models. Therefore, the systematic identification of customer benefits, the determination and prioritization of subscription relevant functionalities as well as the design of product and service elements addressing those functionalities are the main aspects on which the focus is placed on. The result is a subscription value proposition canvas for SMEs to address the impact of subscription models on products and services.
In Germany’s transition to a more sustainable industrial landscape, electricity generated by wind turbines (WT) remains a mainstay of the energy mix. Operating and maintenance costs, which account for roughly 25% of electricity generation costs in onshore WTs make improvements of maintenance activities a key lever in the economic operation of WTs. Prescriptive maintenance is a possible approach for improved maintenance activities. It is a concept where asset condition data is used to recommend specific actions and has great potential for the operation of wind parks. However, especially small, but also large wind park operators, and maintenance service providers often struggle with the implementation of such a new maintenance approach. As a part of the research project ReStroK, a learning game has been developed to support the training and familiarization of maintenance technicians with the concepts and underlying principles of this maintenance approach. In this paper, the concept for the development of a learning game will be presented. Multiple scenarios for its usage and their corresponding requirements will be discussed and an overview over the game will be given.
More and more manufacturing companies are starting to transform the transaction-based business model into a customer value-based subscription business to monetize the potential of digitization in times of saturated markets. However, historically evolved, linear acquisition processes, focusing the transactionoriented product sales, prevent this development substantially. Elemental features of the subscription business such as recurring payments, short-term release cycles, data-driven learning, and a focus on customer success are not considered in this approach. Since existing transactional-driven acquisition approaches are not successfully applicable to the subscription business, a systematic approach to an acquisition cycle of the subscription business in the manufacturing industry is presented, aiming at a long-term participative business. Applying a grounded theory approach, a task-oriented model for themanufacturing industry was developed.
The model consisting of five main tasks and 14 basis tasks serves as best practice to support manufacturing companies in adapting or redesigning acquisition activities for their subscription business models.
Inhaltsangabe Band:
Die vernetzte Digitalisierung hat die produzierende Industrie fundamental verändert. Im Rahmen dessen eröffnen sich produzierenden Unternehmen kontinuierlich neue Chancen, in einem zunehmend dynamischen und durch das Internet geprägten Wettbewerb, wirtschaftliche Erfolge zu erzielen. Durch die veränderten Rahmenbedingungen der vernetzten Digitalisierung müssen produzierende Unternehmen jedoch neue Ansätze für die Organisation der digitalen Transformation verfolgen: Sie müssen die neue Führungsaufgabe Digitalisierungsmanagement gestalten. Dabei muss das Digitalisierungsmanagement eine breite Aufgabenvielfalt abdecken.
Dieses Buch befähigt produzierende Unternehmen die digitale Transformation erfolgreich zu gestalten. Dazu werden Nutzen und Funktionsweisen der wesentlichen Aufgaben des Digitalisierungs- und Informationsmanagements praxisnah dargestellt. Ein spezifisch für produzierende Unternehmen, die eine digitale Transformation anvisieren, entwickeltes Digitalisierungs- und Informationsmanagement-Modell verknüpft schließlich die Inhalte.
Das vorliegende Buch ist als ein Nachschlagewerk für Führungskräfte und Entscheider entwickelt worden, die die Herausforderungen der Realisierung von digitalen Geschäftsmodellen, digitalisierten Produkten und digitalen Geschäftsprozessen angehen wollen. Die Methoden in diesem Buch helfen dabei, die richtigen Managementaufgaben zu verfolgen und diese in der Unternehmensorganisation umzusetzen. Dabei werden auch die Schnittstellen zwischen dem strategischen Digitalisierungsmanagement und dem taktischen bis operativen Informationsmanagement behandelt. Das Buch bietet einen schnellen und einfachen Zugriff auf die wichtigsten Methoden und viele unterstützende Beispiele. Es ist Teil der Reihe „Handbuch Produktion und Management“ und ergänzt dessen Ordnungsrahmen.
(Quelle: https://link.springer.com/book/10.1007/978-3-662-63758-6)
Smart Services als Enabler von Subscription-Geschäftsmodellen in der produzierenden Industrie
(2022)
[Der Sammelband] Widmet sich den in Wissenschaft und Praxis aktuell intensiv diskutierten Fragestellungen zu Smart Services. Befasst sich mit Geschäftsmodellen, Erlösmodellen und Kooperationsmodellen von Smart Services. Geht auf branchenspezifische Besonderheiten von Smart Services ein. (link.springer.com)
The operation of CNC milling is expensive because of the cost-intensive use of cutting tools. The wear and tear of CNC tools influence the tool lifetime. Today’s machines are not capable of accurately estimating the tool abrasion during the machining process. Therefore, manufacturers rely on reactive maintenance, a tool
change after breakage, or a preventive maintenance approach, a tool change according to predefined tool specifications. In either case, maintenance costs are high due to a loss of machine utilization or premature tool change. To find the optimal point of tool change, it is necessary to monitor CNC process parameters during machining and use advanced data analytics to predict the tool abrasion. However, data science expertise is limited in small-medium sized manufacturing companies. The long operating life of machines often does not justify investments in new machines before the end of operating life. The publication describes a cost-efficient approach to upgrade legacy CNC machines with a Tool Wear Prediction Upgrade Kit. A practical solution is presented with a holistic hardware/software setup, including edge device, and multiple sensors. The prediction of tool wear is based on machine learning. The user interface visualizes the machine condition for the maintenance personnel in the shop floor. The approach is conceptualized and discussed based on industry requirements. Future work is outlined.
Technology management can significantly influence the strategic decisions of a company and thus cause success or failure. Basic templates for technology management are technology radars as well as the determination of the technology readiness level (TRL) to be able to evaluate the maturity of newly deployed technologies (e.g., newcomer vs. established). The radars, as well as the TRL, are identified in time-consuming, manual research by subject matter experts from external consultancies. This process is often repeated due to the further development and new development of technologies so that the necessary research becomes an ongoing task. The TechRad research project, therefore, aims to automate the identification of the TRL as well as technology radars using web crawling and Natural Language Processing (NLP). To commercialize the pre-competitive prototype, the development of a pre-competitive business model is the goal of this paper. Based on customer analyses, a target group definition is created. Based on user interviews, the precompetitive business model will be detailed in a four-step approach using a business model canvas and a value proposition canvas.
Driven by different trends, such as digitalization, the number of companies aiming for successful business transformation is increasing, while new structures and systems are paving the way. Strategic agile management systems offer significant potential benefits given the increasing speed of the evolving environment in which organizations find themselves these days. To select and implement the appropriate strategic agile management system, companies need to understand the underlying theoretical principles to be able to select the most suitable for the respective company and to introduce it based on individual adaption. Within this paper, a morphology is presented to improve theoretical knowledge about strategic agile management systems. Creating a common understanding of strategic agile management systems and their current areas of application creates a suitable frame of reference for future research projects.
While digitization is a strategic advantage in numerous industries such as the automotive industry or mechanical engineering, other industries like the German quarrying industry have not yet established a transformation towards a digitized industry. This leads to inefficient work and inaccurate forecasting capabilities. To address these challenges, digital platforms can incentivize digitization
by supporting the capacity utilization and forecasting capability of these companies. In this paper, the quarrying industry is analyzed by a morphology and different types of companies are identified. Knowing the digital maturity of these companies and by determining the key factors to forecast demands and the capacity utilization, different operating models are derived. Combined with a morphology and the value creation system, different scenarios for the identification of platform services are examined. These scenarios are weighted in a utility analysis to get an operating model blueprint to develop and establish digital platforms in less digitized industries.
Understanding the Organizational Impact of Robotic Process Automation: A Socio-Technical Perspective
(2022)
Interest in AI-driven automation software is growing constantly across
all industries, as these technologies enable companies to almost automate administrative processes completely and significantly increase operational efficiency.
However, many implementation attempts fail due to a lack of understanding of how these technologies affect the various socio-technical aspects that are intertwined in an organisation. This leads to a widening gap between value propositions of automation software and the ability of companies to exploit them. For long-term
success, collaboration between humans and software robots in the organization must be optimised. Therefore, the social, technical, and organizational impact of Robotic Process Automation was investigated. Following a socio-technical systems approach, a model was developed and validated in a use case of a company in the mechanical engineering sector. Knowing the influencing factors before launching large-scale automation initiatives will help practitioners to better exploit
efficiency potentials and increase the long-term success.
In der neuen Expertise des Forschungsbeirats Industrie 4.0 untersuchen das FIR e. V. an der RWTH Aachen und das Industrie 4.0 Maturity Center den Status-quo und die aktuellen Herausforderungen der deutschen Industrie bei der Nutzung und wirtschaftlichen Verwertung von industriellen Daten. Handlungsoptionen für Unternehmen, Verbände, Politik und Wissenschaft zeigen auf, wie der Nutzungsgrad der Datenbasis erhöht werden kann und wie sich Potenziale bei der Monetarisierung ausschöpfen lassen. Der Fokus liegt dabei auf produzierenden Unternehmen.
Generation of a Data Model For Quotation Costing Of Make To Order Manufacturers From Case Studies
(2022)
For contract or make to order manufacturers, quotation costing is a complex process that is mainly performed based on experience. Due to the high diversity of the product range of these mostly small or medium-sized companies (SMEs) and the poor data situation at the time of quotation preparation, the quality of the calculation is subject to strong variations and uncertainties. The gap between the initial quotation costing and the actual costs to be spent (pre- and post-calculation) is crucial to the existence of SMEs. Digitalization in general can help companies to get a better understanding of processes and to generate data. For improving these processes, an understanding of the important data for that specific process is crucial. Accurate quotation costing for customized products is time-consuming and resource-intensive, as there is a lack of an overview of data to be used within the process. This paper therefore derives a data model for supporting quotation costing in the company, based on literature-based costing procedures and recorded case studies for quotation and calculation. Based on the results, SMEs will have a first overview of the needed data for quotation costing to optimize their calculation process.
Um auf steigende Kundenanforderungen und das sich änderndes Unternehmensumfeld reagieren zu können, müssen Unternehmen ihre Agilität und Reaktionsfähigkeit, insbesondere in Produktionsprozessen, erhöhen. Dafür müssen die Auswirkungen der möglichen Änderungen im Unternehmensumfeld auf die eigenen Geschäfts- und Produktionsprozesse untersucht und verstanden werden. Das Prozessverständnis allein reicht jedoch nicht: Es werden Daten aus unterschiedlichen Quellen benötigt, um die Ereignisse in der Prozess- und Lieferketten nachzuverfolgen, um das Material eindeutig zu charakterisieren und in Unternehmen vorhandene Algorithmen oder Modelle mit Eingangsdaten zu versorgen. Daher spielt die Datenverfügbarkeit eine wichtige Rolle auf dem Weg zur adaptiven Produktion. In diesem Beitrag wird die Wichtigkeit der Datenverfügbarkeit erläutert sowie ein Konzept der Datenplattform zum sicheren, überbetrieblichen Datenaustausch vorgestellt.
Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in gen-eral. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary eval-uation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be sum-marized in a 2D matrix to support decision-making.
Manufacturing companies (MFRs) are increasingly extending their
portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.
Künstliche Intelligenz (KI) hat als Technologie in den vergangenen Jahren Marktreife erlangt. Es existiert eine Vielzahl benutzerfreundlicher Produkte und Services, welche die Anwendung von KI im Alltag und im Unternehmen vereinfachen. Die Herausforderung, vor denen Anwendende, gerade im betriebswirtschaftlichen Kontext, stehen, ist nicht die technische Machbarkeit einer KI-Applikation, sondern deren organisatorisch und rechtlich zulässige Gestaltung. Zu einer zunehmenden Dynamik in der Gesetzgebung kommt ein gesellschaftliches Interesse an der Kontrolle und Transparenz über die für KI-Modelle erhobenen Daten. Die Diskussion über Datensouveränität im geschäftlichen und privaten Alltag rückt mehr und mehr in das Zentrum der öffentlichen Aufmerksamkeit.
Datenbasierte KI-Anwendungen stehen damit in einem Spannungsfeld zwischen den Potenzialen, die das Erheben und Teilen von Daten über Unternehmensgrenzen hinweg bietet, und der Herausforderung, die Datensouveränität der involvierten Personen zu wahren. Die vorliegende Studie soll erstens über die Auswirkungen der Datensouveränität und die damit verbundenen aktuellen und kommenden Regularien auf KI-Anwendungsfälle aufklären. Dafür wurden Expertinnen und Experten aus den Bereichen Recht, KI- und Organisationsforschung befragt. Zweitens zeigt die Studie Potenziale und Best Practices von KI-Anwendungsfällen mit überbetrieblichem Datenaustausch auf. Dafür wurden Fallstudien in Unternehmen durchgeführt, die bereits erfolgreich Datenaustausch in ihre Geschäftsmodelle integriert haben, um ihre KI-Applikationen zu betreiben und zu verbessern.
Data-driven transparency in end-to-end operations in real-time is seen as a key benefit of the fourth industrial revolution. In the context of a factory, it enables fast and precise diagnoses and corrections of deviations and, thus, contributes to the idea of an agile enterprise. Since a factory is a complex socio-technical system, multiple technical, organizational and cultural capabilities need
to be established and aligned. In recent studies, the underlying broad accessibility of data and corresponding analytics tools are called “data democratization”. In this study, we examine the status quo of the relevant capabilities for data democratization in the manufacturing industry.
(1) and outline the way forward.
(2) The insights are based on 259 studies on the digital maturity of factories from multiple industries and regions of the world using the acatech Industrie 4.0 Maturity Index as a framework. For this work, a subset of the data was selected.
(3) As a result, the examined factories show a lack of capabilities across all dimensions of the framework (IT systems, resources, organizational structure, culture).
(4) Thus, we conclude that the outlined implementation approach needs to comprise the technical backbone for a data pipeline as well as capability building and an organizational transformation.
Die Globalisierung und der steigende Wettbewerbsdruck erfordern, dass Supply Chains heutzutage komplexe Anforderungen erfüllen. Dabei müssen sie gleichzeitig flexibel genug sein, um an kurzfristige Veränderungen angepasst werden zu können. Ein unternehmensübergreifender Datenaustausch ermöglicht den Akteuren durch schnelle Informationsweitergabe über auftretende Ereignisse entlang der Supply Chain, dynamisch auf aktuelle Gegebenheiten zu reagieren und dadurch hervorgerufene mögliche Schäden zu minimieren. Auch wenn viele Unternehmen mit der Bereitstellung von Daten noch zurückhaltend sind, gehen die Vorteile des Datenaustauschs weit über die Verkürzung der Reaktionszeit hinaus.
Das Gegenteil von Theorie ist die Praxis. So sagt man landläufig und unterstellt damit oft, dass wissenschaftiche Erkenntnisse nicht immer für den Alltag taugen. Dass Theorie aber nicht gleich Theorie ist und Wissenschaft und Praxis trotz aller Unterschiedlichkeit aufeinander angewiesen sind, darauf weist das
FIR an der RWTH Aachen schon mit der Auflösung seines Akronyms hin: „Forschung. Innovation. Realisierung."
In the food industry, a very large potential of data ecosystems is seen, in which data is understood, exchanged and monetized as an economic asset. However, despite the enormous economic potential, companies in the food industry continue to rely on traditional, product-oriented business models. Existing data in the value chain of industrial food production, e.g., in harvesting, logistics, and production processes, is primarily used for internal optimization and is not monetized in the form of data products. Especially the pricing of data products is a key challenge for data-based business models due to their special characteristics compared to conventional, analog offerings and multiple design options. The goal of this work is therefore to solve this issue by developing a framework that allows the identification of pricing models for data products in the industrial food production. For this purpose, following the procedure of typology formation, essential design parameters and the respective characteristics are derived. Furthermore, three types for pricing models of data products are shown. The results will serve not only stakeholders in the food industry but also manufacturing companies in general as input for an orientation of their databased business models.
Eine wesentliche Bedingung zur Optimierung der Wertschöpfungsprozesse ist die Transparenz über die leistungsbestimmenden Faktoren eines Unternehmens. Die Ermittlung dieser Faktoren stellt für viele Industriebetriebe eine Herausforderung dar. Im Rahmen der Veröffentlichung wird daher eine Vorgehensweise zur systematischen Identifikation von Einflussfaktoren der Unternehmenskennzahlen vorgestellt, welche die Grundlage zur Ableitung von individuellen Stellhebeln zur Steigerung der Unternehmensleistungsfähigkeit darstellt.
Recent developments have demonstrated the challenges and impacts of disruptions in supply chains. Current disruptions especially affected procurement and have indicated a lack of resilience. Resilience aims at being prepared, decreasing the impact, and enabling fast reactions and adaption in case of disruptions. The systematic design of resilience in procurement is significantly influenced by proactive and strategic actions before disruptions occur. Thus, the procurement strategy plays a major role when increasing resilience. The procurement strategy is influenced by various factors. Thus, a data-based approach for its systematic design is required. Based on the vision of the Internet of Production (IoP), this paper presents a data-based approach for designing procurement strategies. The IoP is a framework that enables cross-domain collaboration by providing semantically adequate and contextual data from production, development, and usage in real-time at an appropriate granularity. The paper aims at analyzing the state of the art regarding the design of procurement strategy in uncertain environments and the identification of success-critical purchased articles. Based on this, an approach is developed that is structured along the action research cycle and uses CRISP-DM to further detail the different steps. Through the use of these frameworks, both practical applicability and objective evaluation are ensured. The proposed approach thus allows the systematic evaluation of purchased articles regarding supply risks and lies the foundation for the adaption of the procurement strategy. The resulting approach is the foundation for future practical application of different use cases. As one central use case for the presented approach, the paper introduces the textile industry and its supply chains.
Als Beispiel einer globalen Krisensituation zeigt die COVID-19-Pandemie eindrucksvoll die Schwachstellen heutiger Wertschöpfungsnetzwerke. Vor dem Hintergrund zunehmend komplexer und vernetzter Wertschöpfungsnetzwerke steigt für Unternehmen die Bedeutung einer resilienten Gestaltung derselben. Dabei wird davon ausgegangen, dass ähnliche Krisensituationen in Zukunft häufiger auftreten werden. Ziel dieser Expertise ist es, Unternehmen bei der Identifikation von Potenzialen und Maßnahmen für die resiliente Gestaltung ihrer Wertschöpfungsnetzwerke zu unterstützen.
In the age of digitalization, manufacturing companies are under increased pressure to change due to product complexity, growing customer requirements and digital business models. The increasing digitization of processes and products is opening up numerous opportunities for mechanical engineering companies to exploit the resulting potential for value creation. Subscription business is a new form of business model in the mechanical engineering industry, which aims to continuously increase customer benefit to align the interests of both companies and customers. Characterized by a permanent data exchange, databased learning about customer behavior, and the transfer into continuous innovations to increase customer value, subscription business helps to make Industry 4.0 profitable. The fact that machines and plants are connected to the internet and exchange large amounts of data results in critical information security risks. In addition, the loss of knowledge and control, data misuse and espionage, as well as the manipulation of transaction or production data in the context of subscription transactions are particularly high risks. Complementary to direct and obvious consequences such as loss of production, the attacks are increasingly shifting to non-transparent and creeping impairments of production or product quality, which are only apparent at a late stage, or the influencing of payment flows. A transparent presentation of possible risks and their scope, as well as their interrelationships, does not exist. This paper shows a research approach in which the structure of subscription models and their different manifestations based on their risks and vulnerabilities are characterized. This allows suitable cyber security measures to be taken at an early stage. From this basis, companies can secure existing or planned subscription business models and thus strengthen the trust of business partners and customers.
Die Zukunftspotenziale der digitalen Technologie könnten den Dienstleistungssektor entscheidend transformieren und damit der schrumpfenden Wettbewerbsfähigkeit der deutschen Wirtschaft neuen Schwung verleihen.
Das Schiff des Wirtschaftsstandorts Deutschland schwankt in rauer werdender See. Es schwankt weniger, weil die traditionellen deutschen Wertschöpfungssäulen (insbesondere die Flaggschiffe Automobil- und Maschinenbau sowie Chemie- und Logistikindustrie) hierzulande an Know how eingebüßt hätten; es sind vielmehr die großen Technologiedurchbrüche der letzten Jahrzehnte, die die deutschen Tugenden, welche mehr als ein Jahrhundert lang für einen Spitzenplatz unter den großen Wirtschaftsmächten gesorgt haben, drastisch an Bedeutung verlieren lassen. Perfektionismus, Verarbeitungsqualität, Zuverlässigkeit und Langlebigkeit von Produkten aller Art sicherten der deutschen Wirtschaft über viele Jahrzehnte hinweg internationales Ansehen. Das führte allerdings zu einer gewissen Selbstzufriedenheit, die die eigene Spitzenposition in der Welt als Selbstläufer ansah. Verliebt in die eigene Perfektion (der Strategieberater und Blogger Sascha Lobo spricht plakativ von einer „Spaltmaßfixierung“ ganzer Wirtschaftszweige) und an permanenter rein inkrementeller Innovation orientiert, hinkt Deutschland auf wichtigen Gebieten der künftigen Wertschöpfungsfelder dem Wettbewerb gefährlich hinterher – insbesondere auf dem für die Zukunft entscheidenden Technologiegebiet der Digitalisierung.
Eine Herausforderung für produzierende Unternehmen in der Entwicklung intelligenter Produkte besteht darin, dass die Zielstellung, die mit einem intelligenten Produkt verfolgt wird, nicht expliziert ist. Zudem ist oftmals nicht spezifiziert, in welchem Anwendungsfall ein intelligentes Produkt agieren soll. Produzierende Unternehmen benötigen Unterstützung, um eine zielorientierte und folglich wirtschaftliche Melioration existierender Produkte zu gewährleisten. Ebendiese Melioration wird im Kontext von intelligenten Produkten als Smartifizierung bezeichnet und stellt damit einen Entwicklungsprozess dar, der ein bestehendes Produkt als Ausgangssituation im Sinne einer Anpassungskonstruktion expliziert. Die originäre Produktfunktion wird folglich nicht verändert, sondern das Produkt um digitale Funktionen und Dienstleistungen erweitert. Der Artikel befasst sich daher erstens mit der Beschreibung generischer Ziele für den Einsatz intelligenter Produkte im Maschinenbau. Eine Zusammenstellung und Erläuterung solcher Ziele unterstützt Unternehmen, eine Präzisierung der Zielfestlegung in der Initiierungsphase eines Smartifizierungsprojekts durchzuführen. Zweitens wird unter Anwendung der Ziel-Mittel-Beziehung ein Anwendungsfall intelligenter Produkte beschrieben. Abschließend werden beide Aspekte in einer Methode zusammengefasst, wie mittels Ziel- und Anwendungsfallbetrachtung Anforderungen abgeleitet und wie diese Elemente in Vorgehensmodelle der Produktentwicklung eingebettet werden können. Exemplarisch wird anhand einer Stanzmaschine aufgezeigt wie die Methode und die sich daraus ableitenden Ergebnisse im Smartifizierungsprozess zur Entwicklung einer intelligenten Stanzmaschine eingesetzt werden.
Nachhaltige Lieferketten
(2021)
Das Thema der Nachhaltigkeit ist nicht zuletzt aufgrund aktueller Entwicklungen zunehmend in den gesellschaftlichen und unternehmerischen Fokus gelangt. Pandemien, Naturkatastrophen, aber auch regulatorische Auflagen zur Erfüllung von Klimaschutzzielen haben zu einem Umdenken geführt. Im Rahmen des Thementags „Sustainable Supply-Chain-Management“ hat das FIR an der RWTH Aachen zusammen mit Expert:innen aus Wirtschaft und Forschung Thesen erarbeitet, die widerspiegeln, wie das Thema Nachhaltigkeit aktuell in deutschen Unternehmen wahrgenommen und umgesetzt wird und welche besondere Bedeutung dem Supply-Chain-Management dabei zukommt. Nachhfolgend werden diese und deren Implikationen für die Praxis vorgestellt.
Towards a Methodology to Determine Intersubjective Data Values in Industrial Business Activities
(2021)
This paper contributes to a valuation framework for valuing data as an intangible asset. Especially those industrial manufacturers developing and delivering holistic digital solutions are limited in calculating the true business value of data initiatives. Since the value of data is strongly dependent on the respective use case, a completely objective valuation is not possible. This complicates decision-making on the internal side regarding investments in digital transformation, and on the external side to communicate existing benefits to third parties via financial reporting. Therefore, the target is to design a valuation framework that allows industrial manufacturers to determine an intersubjective, i.e., traceable and transparent, data value. In order to develop a framework that can be applied in practice, the approach is based on industrial case study research.
"New Service-Work"
(2021)
Durch die Corona-Krise befinden wir uns auf einmal alle in einer völlig neuen Arbeitswelt. Innerhalb kürzester Zeit waren Unternehmen im Service gezwungen, Antworten für – vielleicht auch manchmal schon länger bestehende Fragen – rund um das Thema „New Work“ zu finden. Daher hat sich der KVD in den vergangenen Monaten im Rahmen einer ausgewiesenen Expertenrunde aus Praxis und Forschung mit dem Thema „New Service-Work“ auseinandergesetzt. Vor dem Hintergrund der aktuellen Herausforderungen haben wir die zentralen Handlungsfelder für die Umsetzung dieser neuen Arbeitswelt einmal intensiver beleuchtet. In über 25 digitalen Treffen wurden die Chancen, Risiken oder auch Hemmnisse zu den jeweiligen Handlungsfeldern intensiv diskutiert und aufbereitet. Mit einigen aktuellen, innovativen Lösungsansätzen aus unseren Mitgliedsunternehmen hoffen wir, auch Ihnen erste Hinweise oder Impulse geben zu können, wie auch bald in Ihrem Unternehmen „New Service-Work“ Einzug halten könnte.
Blockchain-Lösungen sind bisher vor allem im Finanzbereich bekannt
und erfolgreich. Doch ihre unbestreitbaren Vorteile bieten weit darüber
hinaus Potenzial und machen sie auch für industrielle Anwendungen
interessant. Vor allem Lieferketten mit ihren komplexen Strukturen,
vielen Beteiligten sowie verschiedensten Material-, Informations- und
Finanzströmen lassen sich mit der Technologie erheblich effizienter
gestalten.
Mehr als nur ein Hype
(2021)
Blockchain-Lösungen sind bisher vor allem im Finanzbereich bekannt und erfolgreich. Doch ihre unbestreitbaren Vorteile bieten weit darüber hinaus Potenzial und machen sie auch für industrielle Anwendungen interessant. Vor allem Lieferketten mit ihren komplexen Strukturen, vielen Beteiligten sowie verschiedensten Material-, Informations- und Finanzströmen lassen sich mit der Technologie erheblich effizienter gestalten.
Das Gegenteil von Theorie ist die Praxis. So sagt man landläufig und unterstellt damit oft, dass wissenschaftliche Erkenntnisse nicht immer für den Alltag taugen. Dass Theorie aber nicht gleich Theorie ist und Wissenschaft und Praxis trotz aller Unterschiedlichkeit aufeinander angewiesen sind, darauf weist das FIR an der RWTH Aachen schon mit der Auflösung seines Akronyms hin: „Forschung. Innovation. Realisierung.“ Hier zielen alle Forschungsaktivitäten darauf ab, Lösungen für reale Herausforderungen aus der Praxis zu schaffen, die am Ende auch umsetzbar sind. Eine nutzenbringende Verbindung zwischen den beiden scheinbar so unterschiedlichen Welten ist dafür unabdingbar und diese Lücke schließt das FIR mit Industriekooperationen, Wissens und Technologietransfer sowie Weiterbildungsangeboten auf vielen Ebenen. Nicht zuletzt positionierte sich das FIR als leitendes Institut des Clusters Smart Logistik auf dem RWTH Aachen Campus und füllt diese Rolle seit über 10 Jahren erfolgreich aus.
Schwerwiegende Gesundheits- und Wirtschaftskrisen wie die Weltfinanzkrise (ab 2007) oder die Covid-19-Pandemie im Jahr 2020 haben aufgezeigt, in welch turbulentem Umfeld sich die globalisierte Welt heutzutage befindet und wie schnell gewohnte Abläufe in Wertschöpfungsketten unterbrochen und angepasst werden müssen. Die alltäglichen Anforderungen in dem sich immer schneller wandelnden digitalen Zeitalter wachsen stetig und sind komplexer denn je.
Unternehmen sind angehalten, immer kurzzyklischer auf sich ändernde
Bedingungen und Störungen zu reagieren und strategische Entscheidüngen
zur Gestaltung von Wertschöpfungsketten zu treffen. Nur mithilfe
einer umfassenden Datengrundlage und -kommunikation kann die strategische Planung der Supply-Chain effektiv erfolgen und somit die benötigte Robustheit und Agilität ermöglicht werden.
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
Erfolgreiche Serviceinnovation im Zeitalter industrieller, datenbasierter Dienstleistungen unterscheidet sich deutlich von bisherigen Ansätzen der klassischen Dienstleistungsentwicklung. Diese Erkenntnis konnte aus einem breit angelegten Benchmarking in der deutschen Industrie gewonnen werden. Die Benchmarking-Studie identifizierte besonders erfolgreiche Unternehmen, deren Methoden und Ansätze zur Gestaltung innovativer Dienstleistungen in Form von Fallstudien im Detail untersucht wurden. Als Kernergebnis ergeben sich sechs Prinzipien, die erfolgreiche Serviceinnovation für datenbasierte Dienstleistungen auszeichnen.
Manufacturing companies are constantly increasing their efforts in the subscription business, also known as product-as-a-service business, offering usage and outcome based solutions (value-in-use) instead of transactional services and products (value-in-exchange). Customers are becoming contractual subscribers of the solution in return for recurring, performance-related payments. To address arising, inevitable challenges like (1) reducing customer churn, (2) increasing usage intensity and outcome quality, (3) ensuring the adoption of product and software releases as well as (4) fostering customer loyalty, leading manufacturing companies are setting up a new organizational, customer-facing unit, called Customer Success Management (CSM). This unit has its origins in the software-as-a-service business, operating next to established entities like sales, key account management and customer service. Since there are currently no holistic models for an end-to-end description of CSM-tasks in the manufacturing industry, this paper contributes to a taskoriented reference model, using a grounded theory approach, examining both manufacturing and software companies. Containing a reference framework with 8 main tasks, 17 basic tasks and 76 elementary tasks, the reference model supports manufacturing companies in adapting and customizing a company-specific CSM concept.
Electricity generated by wind turbines (WT) is a pillar of the transition to renewable energy [1]. In order to economically utilize WTs, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WTs, are a focus of cost reduction activities [2]. A prescriptive maintenance approach can support in achieving this goal. Prescriptive maintenance is a maintenance approach, where asset condition data is collected and analyzed to recommend specific actions to prevent breakdowns and reduce downtimes. However, the processing and analysis of data is quite complex. Especially unstructured data (such as comments of service technicians in free text fields) is often left unused, as companies, mostly SMEs lack the capacity to carry out these analyses. In this work we propose an approach to utilize the information from service reports, maintenance reports as well as status records from SCADA systems for the development of a prescriptive maintenance approach to onshore WTs. To achieve this, an ontology was utilized in this approach to codify implicit knowledge of service technicians and aid in making unstructured data usable for further analysis. The ontology was used to link historical service and maintenance reports with status codes, thus enabling automated analysis. In interviews with WT topic experts and through further research, damage mechanisms and corresponding maintenance measures were identified and a measure catalogue was developed to support service and maintenance activities. The recognition of the root cause of problems allows for a prescriptive maintenance approach that recommends targeted actions to reduce downtimes and optimize maintenance activities, it also allows to effectively control the outcome of maintenance activities and optimize their execution.
Pricing for Smart-Product-Service-Systems in Subscription Business Models for Production Industries
(2021)
In the production industry, subscription business models have the potential to create long-term relationships where a supplier provides a continuous value-oriented service to a customer based on digitalisation. Monetising this increase in value through pricing represents a central challenge for suppliers in subscription business. Unlike the current dominant transactional business, the focus of pricing is on the value-in-use of the customer (e.g. on the increase in output for the customer). In this regard, there is so far no pricing approach for practice that allows the linking of the performance data of the customer with the periodically charged price. However, in subscription businesses, such an approach is required to create win-win situations for the customer and supplier through continuous performance improvement. Therefore, this paper develops a novel process model for pricing of smart-product-service-systems in subscription business for production industries. This process can serve as basis for suppliers of subscriptions in the production industry to align pricing with the created value-in-use. In the long term, this allows companies to systematically develop their pricing to monetise the potential of digitalisation.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.
Künstliche Intelligenz (KI) hat sich über die letzten Jahre stetig zu einem Thema mit strategischer Priorität für Unternehmen entwickelt. Das zeigt sich nicht zuletzt in der gesteigerten Investitionsbereitschaft deutscher Unternehmen in KI-Projekte. Wirtschaftliche Akteure haben erkannt, dass durch eine sinnvolle Nutzung von KI-Technologien Wettbewerbsvorteile erzielt werden können. Die vorliegende Studie legt das Augenmerk auf den industriellen Einsatz einer KI-Technologie, die bereits heute von vielen Unternehmen erfolgreich genutzt wird: Die natürliche Sprachverarbeitung (engl. Natural Language Processing, kurz NLP). Die wirtschaftlichen Potenziale der Technologie liegen dabei in ihrer Fähigkeit, betriebliche Abläufe zu automatisieren und die Schnittstelle zwischen Mensch und Maschine zu verbessern und zu vereinfachen. Ziel der Studie ist es, die Potenziale der NLP-Technologie für Unternehmen nutzbar zu machen, indem konkrete Anwendungsfälle und allgemeine Handlungsempfehlungen sowie Nutzen und Risiken aufgezeigt werden.
Prinzipien zur erfolgreichen Umsetzung von KI-Geschäftsmodellinnovationen
In Zeiten des zunehmenden globalen Wettbewerbs und hoch vernetzter Wertschöpfungsketten entwickelt sich Künstliche Intelligenz zu einem immer wichtiger werdenden Wettbewerbsfaktor für Unternehmen am Wirtschaftsstandort Deutschland. Durch den Einsatz von KI-Verfahren können nicht nur interne Geschäftsprozesse kostensenkend optimiert, sondern auch neue, digitale Geschäftsfelder und -modelle erschlossen werden. Es lassen sich zum einen Trends identifizieren, denen der Einsatz von KI in deutschen Unternehmen folgt. Zum anderen zeigt sich, dass sich KI unterschiedlich stark auf verschiedene Dimensionen innovativer Geschäftsmodelle auswirkt. Insgesamt lassen sich so Prinzipien ableiten, die die erfolgreiche Umsetzung von KI-Geschäftsmodellinnovationen beschreiben.
Neue Technologie- und Anwendungstrends kennzeichnen KI-Nutzung
Die tatsächliche KI-Landschaft in den Wertschöpfungsketten von KI-nutzenden Unternehmen ist durch Trends gekennzeichnet. Diese lassen sich in Technologie- und Anwendungstrends unterteilen. Experteninterviews zeigen beispielsweise, dass KI-Anwendungen bevorzugt auf Cloud-Infrastrukturen entwickelt und bereitgestellt werden. Das wiederum rückt die Frage nach der Wahrung der Datensouveränität in den Vordergrund. Anwendung findet KI tendenziell zur Prognose und Überwachung.
Sechs Prinzipien beeinflussen die erfolgreiche Umsetzung von KI-Geschäftsmodellinnovationen
Fallstudien über ein breites Spektrum der deutschen Wirtschaft beleuchten, welche Aspekte eines KI-basierten Geschäftsmodells den größten Effekt auf das Unternehmen haben. Hier lässt sich ein besonders hoher Einfluss von KI auf das Nutzenversprechen neuartiger, digitaler Leistungen der Unternehmen an die Kundinnen und Kunden feststellen. So lassen sich sechs Erfolgsprinzipien zur erfolgreichen Implementierung von KI-Technologien identifizieren, um die wirtschaftliche Nutzung von KI für Unternehmen in Deutschland im globalen Wettbewerb weiter zu steigern. So empfiehlt es sich zum Beispiel – neben der Auswahl des richtigen KI-Anwendungsfalles – ebenfalls darauf zu achten, dass die KI-Anwendung sowohl den Anbietenden wie auch den Anwendenden nützt. Diese und weitere Erfolgsprinzipien werden detailliert in der Studie Künstliche Intelligenz – Geschäftsmodellinnovationen und Entwicklungstrends beschrieben.
For most industries, Artificial Intelligence (AI) holds substantial potentials. In the last decades, the extent of data created worldwide is exponentially increasing, and this trend is likely to continue. However, despite the prospects, many companies are not yet using AI at all or not generating added value. Often, an AI project does not exceed its pilot phase and is not scaled up. The problems to create value from AI applications in companies are manifold, especially since AI itself is diverse and there is no ‘one size fits all’ approach. One often stated obstacle, why many AI projects fail, is a missing AI strategy. This leads to isolated solutions, which do not consider synergies, scalability and seldom result in added value for the company. To create a company-specific AI strategy with a top-down approach, a generic but holistic framework is needed. This paper proposes a strategic AI procedure model that enables companies to define a specific AI strategy for successfully implementing AI solutions. In addition, we demonstrate in this paper how we apply the introduced strategic AI procedure model on an AI-based flexible monitoring and regulation system for power distribution grid operators in the context of an ongoing research project.
Der vorliegende Beitrag beschreibt eine Vorgehensweise zur kurzfristigen Umstellung von Blended-Learning- oder Präsenzangeboten. Hierbei werden neben möglichst schnell umsetzbaren technischen Lösungen auch notwendige organisatorische Anpassungen thematisiert und anhand des E-Mas-Weiterbildungsprogramms illustriert.
Subscription business transforms traditional business models of machinery and plant engineering. Many manufacturing companies struggle to pull out the potential created by Industry 4.0 and make it economically usable. In addition to technological innovations, it is necessary to transform the business model. This leads to a shift from ownership-based and product-centric business models to outcome-based business models, which focus on the customer's value and thus realize a unique value proposition and competitive advantage – the outcome economy. Based on a case study analysis among manufacturing companies, this paper provides further clarification including a definition and constituent characteristics of subscription business models in machinery and plant engineering.
In immer komplexer werdenden Wertschöpfungsketten wird die Geschwindigkeit, mit der Informationen weitergegeben und entsprechende Maßnahmen umgesetzt werden können, zu einem entscheidenden Wettbewerbsvorteil. In der Realität kommt es jedoch auf dem Weg zwischen einem Ereignis und einer passenden Reaktion zu verschiedenen zeitlichen Verzögerungen, sogenannten Latenzen, die die Agilität eines Unternehmens erheblich hemmen. Insbesondere das Supply-Chain-Management mit seiner koordinierenden Funktion wird dadurch vor enorme Herausforderungen gestellt. Schlüsseltechnologien im Zeitalter von Digitalisierung und Industrie 4.0 bieten jedoch enorme Potenziale, die verschiedenen Formen von Latenzen zu reduzieren. Der Beitrag untersucht die unternehmensübergreifenden Effekte dieser Verzögerungen entlang der Supply-Chain und beleuchtet darüber hinaus die Potentiale konkreter digitaler Technologien auf selbige.
A large number of product-accompanying services in the machinery and plant engineering industry is based on the cross-company exchange of data and information. By providing services, additional sales potential on the manufacturer side as well as far-reaching product and process advantages for appliers can be reached. However, the necessary cross-company exchange of information is nowadays limited due to a lack of trust in the interacting partner and the applicable existing technologies, which results in significant losses in the terms of business potential. The uncovering of this potential now seems to be made possible by the use of the Blockchain technology. Through the key factors security, immutability, transparency and decentralisation, it serves as an enabler for cross-company communication and product-accompanying services. The technological implementation of a Blockchain can take on a broad spectrum of attributes, which can lead to decisive restrictions for the execution of services. This justifies the necessity for a qualified and context-related assessment of service-types-individual specifications and the resulting requirements on the system. Within the scope of this paper, different types of product-accompanying services are identified and analysed regarding their requirements for a Blockchain-based machinery and plant connection. This can serve as a basis for a qualified and goal-oriented configuration of the Blockchain.
The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.
The aim of the related research project eCloud is to enable small and medium sized enterprises (SMEs) to implement flexible energy management without in-depth energy knowledge and with little distraction from day-to-day business, which is prepared for current and future challenges in the field of energy use. The overall result is a validated prototype for a plug and automate capable (i.e. without implementation effort) operational energy management, which can be successively set up in SMEs based on a cloud platform. Through its gradual and modular implementation, energy management meets the individual needs of each company and contributes to energy system transformation and climate protection by reducing energy costs and greenhouse gas emissions by up to 25%. In total, three expansion stages are available with the levels of monitoring, load management and grid usage, which consist of various Software as a Service (SaaS) modules from the cloud that can be retrieved as required. Thus, the user only needs a minimal hardware intervention in his production and saves a complex IT infrastructure. The methodology developed has been successfully applied by two user companies so far. This proves the effectiveness of the method.
Since 2016, the “Digital in NRW” Competence Centre has been supporting SMEs in the manufacturing industry in designing their individual digital transformation. With an Industry 4.0 maturity assessment, we define the status quo of SMEs, derive SME-specific measures from this, develop a digitalization roadmap and accompany the SME transformation. This paper presents the results of the four-year SME support. By analyzing the results of all maturity assessments, potential analysis and design workshops, we present the most frequent and most effective measures for a successful digital transformation of SMEs. The result of the paper is an action guideline for SMEs to initiate their own digital transformation based on formalized experience.
Störungen und Änderungen des Produktionssystems führen zu Kosten und Aufwänden, bieten jedoch auch die Chance zur kontinuierlichen Verbesserung.
Um Änderungsanfragen zu erfassen, können etablierte Ansätze genutzt werden. Diese vernachlässigen jedoch die Anforderungen, denen sich ein Produktionssystem im Zeitalter der Digitalisierung ausgesetzt sieht. Der vorliegende Beitrag stellt einen Ansatz zur standardisierten Erfassung von Änderungsanfragen vor, welcher die Ausgangsbasis für die Bewertung von Änderungsanfragen in bestehenden IT-Systemen bietet.
In recent years, the complexity of the management of supply chains has increased significantly due to the growing individualization of products and dynamics of the market environment. To remain competitive, ensuring efficient and flexible processes and procedures along the entire supply chain are of particular importance for companies. Especially in the inter-company context, decisions must be made as quickly and correctly as possible. To enable good decision-making processes data must be processed and provided in a targeted manner. Currently, however, the necessary transparency is often lacking within the supply chains. In this article, a software-based assistance system for decision support on supply chain level is presented that aims to increase the transparency and efficiency of the decision-making process. A concept for decision support on supply chain level is presented. This paper focuses on the conceptual linkage of relevant decisions and data. Therefore, indicators are identified and linked with the relevant decisions. Moreover, a suitable way of visualizing the identified indicators for each decision in a user-friendly manner is defined. These results are then used to implement the software tool.
Data Driven Services
(2020)
Als größter Berufsverband für Beschäftigte im Kundendienst und im After-Sales-Service innerhalb der DACH-Region verbindet der Kundendienst-Verband Deutschland e. V., kurz KVD, unterschiedliche Akteure
im Thema Service, so zum Beispiel aus Wissenschaft und Wirtschaft. Dabei gelingt es dem KVD nicht nur, seine Mitglieder untereinander zu vernetzen, sondern ihnen stets brandaktuelle Inhalte anzubieten.
Die enge Kooperation mit Forschungseinrichtungen ermöglicht es dem KVD und seinen Mitgliedern immer wieder, neue Themen und nützliche Werkzeuge für die praktische Anwendung zur Verfügung zu stellen.
Progress in the development of small electric and hybrid aircraft promises business opportunities for thin-haul air mobility services. In order to develop demand-oriented flight plan scenarios for Germany, this paper presents a model to estimate the marked volume of thin-haul air mobility. To quantify the potential demand, our model includes the steps of trip generation, trip distribution and mode choice. Trip generation and distribution takes place between 412 geographic subdivisions of Germany and is based on calibrated traffic forecast data for the year 2030. For the first time the five relevant modes of transport, namely: car, intercity train, intercity bus, commercial aircraft and thin-haul air mobility services, have been included in one model. The step of choosing the transport mode is implemented via a generalized cost approach, taking into account travel costs and travel time. Additionally, route modeling of all transport modes is enhanced by real market data using large-scale data readouts of web interfaces. As primary result we predict a market share of 6 % or 81 million trips per year for thin-haul air mobility services. The demand concentrates on a small number of airports: 30 % of the trips are estimated to be between only 20 airports. Hubs and main routes are identified to offer the potential for scheduled air services.
Auf Basis einer systematischen Literaturanalyse wurden insgesamt 11 Kennzahlen identifiziert, welche die Grundlage zur Beschreibung der operativen Leistungsfähigkeit von Unternehmen bilden. Die Kennzahlen wurden in die vier Leistungsdimensionen Effizienz, Qualität, Zeit und Flexibilität eingeteilt.
Es geht um die Entwicklung eines Software-Tools zur Unterstützung bei der Auswahl von geeigneten 3D-Druckdienstleistern im Kontext der additiven Ersatzteillogistik. Im Fokus steht der Logistikdienstleister als potentieller Nutzer des Softwaretools. Das Softwaretool erfüllt zwei zentrale Funktionen: Überprüfung ob ein Ersatzteil additiv gefertigt werden soll und Auswahl eines konkreten Produzenten durch Matchingalgorithmus.
The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity.
In manufacturing, adherence to delivery dates is one of the main logistic goals. The production control department has to cope with short-term deviations from the planned route sheets. Because of unforeseen disruptions, e.g. machine breakdowns or shortage of material or personnel, in some situations, the promised delivery date to the customer is at stake. In practice, a fast and reasonable decision on how to deal with the delayed order is required. This decision process is often based on a qualitative analysis relying on the planner’s subjective assessment of a complex situation. To improve the quality of possible countermeasures this paper presents an application, which supports the decision process through a quantified analysis using real-time data from business application systems in combination with a simulation of the value stream. The developed app is part of the decision process and estimates the effect of selected countermeasures to accelerate a delayed order. Performance indicators illustrate the effect of the countermeasures on the specific order as well as the whole system. This approach empowers the planner to assess unforeseen situations and aims to improve the quality of the decision-making process. This paper describes the architecture of the application, its simulation ecosystem, the relevant data and the decision process to select the most effective countermeasures.
This paper contributes to an assessment framework for valuing data as an asset. Particularly industrial manufacturers developing and delivering Smart Product Service Systems (Smart PSS) are comprehensively depended on the business value derived by processing data. However, there is a lack in a framework for capturing and comparing the Smart PSS data value with the purpose of increasing the accountability of data initiatives. Therefore a qualitative data value assessment approach was developed and specified on Smart PSS, based on an industrial case study research. [https://link.springer.com/chapter/10.1007/978-3-030-57997-5_39]
Industrie 4.0 is said to have major positive effects on productivity in manufacturing companies. However, these effects are not visible yet. One reason for this is the lack of understanding of maintenance services as a crucial value contributing partner in production processes, although scientific literature already highlighted the importance of indirect maintenance costs. In order to retrieve the unused potential of maintenance services, a digital shadow in form of a sufficiently precise digital representation is required, providing a data model for the value of maintenance actions so that asset and maintenance strategies can be optimized later on. Using case study research for process manufacturers, the first research contribution of this paper consists of 21 value contributing elements being identified. The second contribution is a reference processes model, showing seven major process steps as well as the required intra-organization interaction on an information technology system level. Therefore, it provides the base for the missing data model shaping the targeted digital shadow of maintenance services’ value contribution. [https://link.springer.com/chapter/10.1007/978-3-030-57993-7_69]
Reliability-centered maintenance for production assets is a well-established concept for the most effective and efficient disposition of maintenance resources. Unfortunately, the approach takes a lot of effort and relies heavily on the knowledge of individuals. Reliability data in Computerized Maintenance Management System (CMMS) is scarce and almost never used well. An automated risk assessment system would have the potential to contribute to the dissemination and effective use of risk information and analysis. The individuality of production setting, however, prevents current systems from being practically relevant for most industries. The presented approach combines ontologies to store and link knowledge, an information logistics model displaying the various information streams, and the Internet of production to take the different user systems and infrastructure layers into account. The provided model of a reference digital shadow for risk information and a detailed information logistics model will help software companies to improve reliability software, standardize and enable assets owners to establish a customized digital shadow for their production networks. [https://link.springer.com/chapter/10.1007/978-3-030-57993-7_2]
Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.
The do-it-yourself mentality is particularly widespread in the furniture sector. Homemade furniture is very popular. The individualisation of furniture can be observed in internet forums, such as the online platform Pinterest. These creative ideas of potential customers show a need for individualized sustainable pieces of furniture. The current production structures, however, do not allow individual production according to the end customer's specifications. In addition, information logistics faces a major challenge: making the creative ideas of end consumers available to producers in parametric form. Topics such as customer requirements in relation to sustainable production, material specifications, industrial property rights, fair production conditions and traceability are the focus of this data interchange. An open and innovative European furniture ecosystem must be created to connect all stakeholders in the production process. This is made possible by a platform that channels the creativity of consumers and makes it designable and producible through the professional skills of designers. This requires the involvement of manufacturing specialists who can produce personalised products through sustainable intelligent production technologies. An exchange of information must also take place securely and quickly in order to protect the personal rights of the sources of ideas. This is being developed in the EU research project INEDIT - Open Innovation Ecosystem for do-it-together process. By connecting many different stakeholders along the entire value creation process, a change towards efficient collaborative collaboration is achieved. This paper presents a project insight for the development of an international co-creation platform by presenting the problem and linking it to a potential solution.
Trends und Entwicklungen
(2020)
Der traditionelle After Sales Service inklusive der hohen Margen und Gewinnbeiträge steht einem Wandel gegenüber. Digitale Transformation, Globalisierung oder disruptive Geschäftsmodelle verändern die etablierten Rahmenbedingungen des Geschäftsbereiches zunehmend. Daher sollten sich Unternehmen auf diesen Wandel vorbereiten und Veränderungen in der eigenen Organisation anstoßen. Aus diesem Grund wird in dem letzten Kapitel des vorliegenden Buches auf wichtige Trends im After Sales Service eingegangen. Zu nennen sind hierbei der Ansatz der Servitization (Abschn. 7.1), die Digitale Transformation im After Sales Service (Abschn. 7.2), digitale Geschäftsmodelle (Abschn. 7.3), das Smart Service Engineering (Abschn. 7.4) oder der Einfluss der Elektromobilität auf den automobilen After Sales Service (Abschn. 7.5).
Der Technologie- und Trendradar 2022 enthält die neusten Technologien und Trends des vergangenen Jahres. Im aktualisierten Radar wurden die Technologiereifegrade in den Steckbriefen neu bewertet, die Anwendungen, Potenziale und Herausforderungen der Technologien wo nötig aktualisiert und neue Technologien aufgenommen.
Der Technologie- und Trendradar 2022 enthält elf neue Steckbriefe. Das Technologiefeld Vernetzung wurde um Eventgetriebene IT-Architekturen, Internet of Behaviors und Web3 erweitert. Dem Feld Virtualisierung wurde die Technologie Metaverse hinzugefügt. Das Technologiefeld Datenverarbeitung wurde um den Trend Data-Centric AI ergänzt, das Feld Prozesse um den Trend Digitale Souveränität. Im Technologiefeld Produkte wurden die Technologien Edge AI, Inter Planetary File System (IPFS), Photonische Siliziumchips, Soft-Robotik und Neuromorphic Computing aufgenommen.
Task-Specific Decision Support Systems in Multi-Level Production Systems based on the digital shadow
(2019)
Due to the increasing spread of Information and Communication Technologies (ICT) suitable for shop floors, the production environment can more easily be digitally connected to the various decision making levels of a production system. This connectivity as well as an increasing availability of high-resolution feedback data, can be used for decision support for all levels of the company and supply chain. To enable data driven decision support, different data sources were structured and linked. The data was combined in task-specific digital shadows, selecting clustering and aggregation rules to gain information. Visual interfaces for task-specific decision support systems (DSS) were developed and evaluated positively by domain experts. The complexity of decision making on different levels was successfully reduced as an effect of the processed amounts of data. These interfaces support decision making, but can additionally be improved if DSS are extended with smart agents as proposed in the Internet of Production.
Glück per Abo
(2019)
Was charakterisiert einen Menschen jenseits seines offensichtlichen Verhaltens und seiner äußeren Gestalt? Wohl nichts so sehr wie seine Bedürfnisse und Wünsche. „Am Ende existiert der Mensch nur durch seine Bedürfnisse“, bringt es der Dichter Friedrich Hebbel überspitzt auf den Punkt. Doch wie sehen diese Bedürfnisse aus?
The technical development of the 5G mobile communication technology has been successfully completed. Now, vendor companies struggle with the analysis of industrial application and sales strategies as well as the development of business cases for their customers. Since this challenge is faced by many technology providers with innovative technologies in the “trough of disillusionment”, FIR’s information technology management has developed a methodology to bridge the gap, based on the example of 5G. This paper presents a methodology for identifying applications and defining business cases to select the most profitable ones. We also validate the methodology in the 5Gang research project.
Eine Herausforderung für produzierende Unternehmen in der Entwicklung intelligenter Produkte besteht darin, dass die Zielstellung, die mit einem intelligenten Produkt verfolgt wird, nicht expliziert ist. Zudem ist oftmals nicht spezifiziert, in welchem Anwendungsfall ein intelligentes Produkt agieren soll. Produzierende Unternehmen benötigen Unterstützung, um eine zielorientierte und folglich wirtschaftliche Melioration existierender Produkte zu gewährleisten. Ebendiese Melioration wird im Kontext von intelligenten Produkten als Smartifizierung bezeichnet und stellt damit einen Entwicklungsprozess dar, der ein bestehendes Produkt als Ausgangssituation im Sinne einer Anpassungskonstruktion expliziert. Die originäre Produktfunktion wird folglich nicht verändert, sondern das Produkt um digitale Funktionen und Dienstleistungen erweitert. Der Artikel befasst sich daher erstens mit der Beschreibung generischer Ziele für den Einsatz intelligenter Produkte im Maschinenbau. Eine Zusammenstellung und Erläuterung solcher Ziele unterstützt Unternehmen, eine Präzisierung der Zielfestlegung in der Initiierungsphase eines Smartifizierungsprojekts durchzuführen. Zweitens wird unter Anwendung der Ziel-Mittel-Beziehung ein Anwendungsfall intelligenter Produkte beschrieben. Abschließend werden beide Aspekte in einer Methode zusammengefasst, wie mittels Ziel- und Anwendungsfallbetrachtung Anforderungen abgeleitet und wie diese Elemente in Vorgehensmodelle der Produktentwicklung eingebettet werden können. Exemplarisch wird anhand eines Energiekompensators aufzeigt, wie die Methode und die sich daraus ableitenden Ergebnisse im Smartifizierungsprozess zur Entwicklung eines intelligenten Energiekompensators eingesetzt werden.