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Nowadays, the market for information and communication technologies used for IOT-applications grows daily. Since companies need technologies to transform their business processes corresponding to the digital revolution, they need to know which technologies are available, and fit the best for their use case. Their inertial issue is the lacking overview of technologies suitable to connect their production or logistics. Hence, this paper presents a methodology to select technologies (and combinations) based on their functions. It differentiates between information and communication technologies, digital technologies and connecting technologies by the physical function and its role in a cyber-physical system. Depending on the use case, the applicability of every technology varies. Due to that reason, the paper illustrates a ranked qualification of the technologies for typical use cases, focussing tracking and tracing issues in the intralogistics of producing companies. The evaluation is performed upon a literature research, a market study to identify suitable technologies, and various expert interviews to assess the applicability of the technologies.
Many ERP systems support configurable materials. Due to an ever increasing number of product variants the benefits of this approach are well understood. However, these implementations are not standardized. In this article we propose a new standard interface for the exchange of configuration data. This would lead to further benefits as systems as Advanced Planning systems could better use manufacturing flexibility while web shops as Amazon could easily integrate manufacturers of complex products with much reduced implementation effort.
Towards the Generation of Setup Matrices from Route Sheets and Feedback Data with Data Analytics
(2018)
The function or department of production control in manufacturing companies deals with short-term scheduling of orders and the management of deviations during order execution. Depending on the equipment and characteristics of orders, sequence dependent setup times might occur. In these cases for companies that focus on high utilization of their assets due to long phases of ramp up and high energy costs, it might be optimal to choose sequences with minimal setup time times between orders. Identifying such sequences requires detailed and correct information regarding the specific setup times. With increasing product variety and shorter lot sizes, it becomes more difficult and rather time intense to determine these values manually. One approach is to analyse the relevant features of the orders described in the route sheets or recipes to find similarities in materials and required tools. This paper presents a methodology, which supports setup optimized sequencing for sequence dependent setup times through constructing the setup matrix from such route sheets with the use of data analytics.
Manufacturing companies worldwide recognized the high potential of Industrie 4.0 in order to increasing production efficiency. Key benefits include creation of integrated systems, networked products and improvement of service portfolios. However, for many companies deriving and evaluating necessary measures to use Industrie 4.0 potentials represents a major challenge. This paper introduces the "acatech Industrie 4.0 Maturity Index" as an approach to meet this challenge. The development of multidimensional maturity model intents to provide companies an assessment methodology. The aim is to capture the status quo in companies in order to be able to develop individual roadmaps for the successful introduction of Industrie 4.0 and manage the transformation progressively.
Service Engineering Models
(2019)
Since the field of service engineering emerged in the late 20th century, the service industry has undergone drastic changes. Among the reasons for these changes is the increasing digitalization, which has made it difficult for companies to successfully develop new service offerings. While numerous service engineering models are available to provide guidance during the design of new services, many of them cannot keep up with the requirements of today’s economic environment. The present paper examines the requirements that service engineering models need to meet in order to be suitable guidelines for the digital age. To this end, the introduction illustrates how digitalization has changed the service industry. Afterwards, selected service engineering models and related norms are presented. Finally, a set of requirements for modern service engineering models derived from best practices from recent years is introduced.
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.
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.
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.
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.
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.
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.
Klar Schiff
(2009)
Im Rahmen dieser Studie untersuchten das Forschungsinstitut für Rationalisierung e. V. an der RWTH Aachen und die Universität St. Gallen
(Lehrstuhl Produktionsmanagement) 24 Veröffentlichungen von 11 Beratungsunternehmen. Dabei wurden über 200 Aussagen zur Bewältigung der Krise bewertungsneutral identifiziert und analysiert.
Factory automation and production are currently
undergoing massive changes, and 5G is considered being a key
enabler. In this paper, we state uses cases for using 5G in the
factory of the future, which are motivated by actual needs of the
industry partners of the “5Gang” consortium. Based on these use
cases and the ones by 3GPP, a 5G system architecture for the
factory of the future is proposed. It is set in relation to existing
architectural frameworks.
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.
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.
Digitalization is changing the industrial landscape in a way we did not anticipate. The manufacturing industries worldwide are working to develop strategies and concepts for what is labelled with different terms such as the Industrial Internet of Things in the USA or Industrie 4.0 in Germany. Many industrialized economies are driven by the production sector and this sector needs specific approaches and instruments to take up other than those approaches we know from start-ups and ventures coming from Silicon Valley and other places. In this paper, we demonstrate an appropriate approach to transform producing companies in a systematic and evolutionary approach.
In particular, the objective of this paper is to provide results from two initiatives which conceptually build upon each other and are of particular relevance for the production industry. First, we present a global survey on the state of implementation and the future perspectives of the concept Industrie 4.0 from 2016. Findings from this study have forced parts of the German industry to heavily invest into a common approach to accelerate change towards Industry 4.0 in order to stay competitive in worldwide economy. This approach is presented in a second part.
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.
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.
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.
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.
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.
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.
[Study] Blockchain
(2019)
Distributed ledger technologies, of which the best known example is blockchain, were expected to make their big breakthrough in 2018. Instead, the opposite happened. Cryptocurrency price slumps and delays in promising projects became symptoms of a new sense of caution. Organizations tried to use blockchain in unsuitable applications, and underestimated implementation hurdles. Despite this, the need for effective data exchange and data management in today's connected world remains high. Decentralized solutions, intelligent sensors, global supply chains and vast quantities of customer data will further stimulate demand for specialized and powerful data management systems. Blockchain therefore remains one option to enable a secure and interconnected world. The following five-step approach will help you harness blockchain's potential, avoiding common mistakes and overcoming implementation hurdles on your way.
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.
Ziel des Forschungsbereichs "Selbstoptimierende Produktionssysteme" ist es, sowohl technische als auch soziotechnische Produktionssysteme zu entwickeln, die durch Selbstoptimierung eine bessere Performance erreichen, als bei der Auslegung geplant und erwartet werden kann. Im Fokus steht die Steigerung der Produktivität in der Produktion direkt vor Ort. Bedeutend ist die dezentrale Entscheidungsfähigkeit der Mitarbeiter auf dem Shopfloor und in unterstützenden Bereichen, sowie der kognitiven und adaptiven Systeme und Netzwerke in der Produktion.
Die Verschärfung des Wettbewerbsumfelds produzierender Unternehmen und die als Antwort hierauf in den Fokus rückenden agilen Methoden vergrößern die Bedeutung einer effizienten Handhabung von Änderungsprozessen. Am Beispiel des Maschinen- und Anlagenbauers Ortlinghaus zeigt der Beitrag, dass eine Kombination aus ungeeigneten Änderungsprozessen und mangelhaftem IT-Support in der Praxis oft die schnelle und gleichzeitig qualitätsgesicherte Durchführung von Änderungsprozessen verhindert. Der Zielkonflikt aus geringem Zeitbedarf und hoher Prozessqualität lässt sich durch Anpassungen in der IT-Unterstützung reduzieren. Hierdurch können Erfolgsfaktoren für ein effizientes Änderungsmanagement gehoben und die Problemfelder der Workflowunterstützung, Informationsverteilung und Datenhandhabung verbessert werden. Zentrales Hindernis zur Adressierung der Erfolgsfaktoren stellt die aktuell zur Abwicklung von Change Requests genutzte Arbeitsumgebung dar. Der Beitrag präsentiert hierfür als zentralen Lösungsansatz die Internet of Production Infrastruktur. Das Potenzial der Internet of Production Infrastruktur im Kontext des Änderungsmanagements wird anhand von drei Anwendungsbeispielen verdeutlicht. Abschließend wird der Migrationspfad für Unternehmen bei der Einführung eines effizienten Änderungsmanagements aufgezeigt.
This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.
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]
Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past years – leading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future. [https://link.springer.com/chapter/10.1007/978-3-030-28464-0_13]
Industrie 4.0 is changing the industrial landscape in an unanticipated way. The vision for manufacturing industries is to transform to an agile company, in order to react on occurring events in real-time and make data based decisions. The realization requires also new capabilities for the information management. To achieve this goal agile companies require taking measured data, analyzing it, deriving knowledge out of this and support with the knowledge their employees. This is crucial for a successful Industrie 4.0 implementation, but many manufacturing companies struggling with these requirements. This paper identifies the required capabilities for the information management to achieve a successful Industrie 4.0 implementation. [https://link.springer.com/chapter/10.1007/978-3-319-65151-4_3]
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.