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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.
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.
Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.
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]
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.
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.
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.
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.
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.
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.