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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.
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.
Die Blockchain-Technologie (BCT) ist eine der vielversprechendsten Technologien der Gegenwart, die in Zukunft insbesondere für produzierende Unternehmen eine noch größere Bedeutung haben wird, um die unternehmensübergreifende Zusammenarbeit zu verbessern und Prozesse gegenüber dem Kunden transparenter zu gestalten. Trotzdessen wird die BCT als vertrauensschaffendes Instrument noch nicht in der Breite angewendet. In diesem Beitrag werden neben den Potenzialen die Herausforderungen für den Einsatz der BCT erörtert und auf Basis des St. Gallener Management-Modells ein Lösungskonzept hergeleitet, welches dem potenziellen Anwender der BCT mögliche Anwendungsszenarien aufzeigt.
Schlüsselfaktoren für den industriellen Einsatz Additiver Fertigung in produzierenden Unternehmen
(2020)
Die Additive Fertigung (AM) ist insbesondere als Hilfsmittel bei der Produktentwicklung weit verbreitet. 71 Prozent der produzierenden Unternehmen setzen AM für die Fertigung von Prototypen und Pilotserien ein. Derzeit eignet sich AM jedoch nicht mehr nur für die Fertigung von Prototypen und Pilotserien, sondern gewinnt auch zur Herstellung von Endprodukten aus metallischen Werkstoffen an Bedeutung. Der vorliegende Beitrag verfolgt das Ziel, Schlüsselfaktoren zu identifizieren, die den industriellen Einsatz von AM in produzierenden Unternehmen am stärksten prägen. Damit wird zugleich die Grundlage geschaffen für ein systematisches Vorausdenken der Zukunft.
The planning and implementation of migration projects in global production networks is a complex planning task that is confronted with a dynamic global environment with highly complex interdependencies. Today's migration approaches are either large projects or isolated local
investments. As such, they are not suitable for simultaneously addressing interdependencies and continuity. This paper illustrates a holistic and continuous methodology for rolling migration planning and implementation in global production networks. Seven steps enable the transformation from the current state of the production network into a target state regarding internal as well as external dynamics and interactions.
The number of cyber-attacks on small and medium enterprises (SMEs) is constantly increasing. SMEs do not recognize the attacks until the damage has occurred. Only then, they fight with measures to increase IT-security and IT-safety. Many studies come to the point that this refers to a lack of budget, expertise and awareness of the need for IT-security. There are many compendia with recommendations for action, but they are too comprehensive and unspecific to the individual needs of SMEs. In this paper, we present the results of a research activity on the gaps that address the challenges faced by SMEs. In addition, we develop a concept for a serious gaming approach that includes an economic perspective on IT-security measures and shows how SMEs can derive their own IT-seurity target state
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.
Manufacturing companies face the challenge of selecting digitalization measures that fit their strategy. Measures that are initiated and not aligned with the company’s strategy carry the risk of failing due to lack of relevance. This leads to an ineffective use of scarce human and financial resources. This paper presents a target system to help companies select relevant digitalization measures compliant with their strategy for IT-OT-integration projects. The target system was developed based on literature research and expert interviews, and later validated in two use cases. The target system considers the goals of production companies and combines them with digitalization measures. The measures are classified by different maturity levels required for their realization. Thus, the target system enables manufacturing companies to evaluate digitalization measures with regards to their strategic relevance and the required Industrie 4.0 maturity level for their realization. This ensures an effective use of resources.
The number of available technologies is constantly rising. Be it additive manufacturing, artificial intelligence (AI) or distributed ledger technologies. The choice of the right technologies may decide the fate of a company. Due to the overwhelming amount of information sources, regular technology market research becomes increasingly challenging, especially for SMEs. In order to assist the technology management process, the authors will introduce the architecture of an automated, AI-based technology radar. The architecture will automatically collect data from relevant sources, assess the relevance of the respective technology (i.e. their maturity level) and then visualize it on the radar map.
Aktuell ist noch nicht geklärt, wie sich das Zusammenwirken von Menschen und betrieblichen Anwendungssystemen bei der Bearbeitung der Aufgaben der PPS nach der Umsetzung von Industrie 4.0 entwickelt. Zur Systematisierung der Auswirkungen von Industrie 4.0 auf die PPS werden in diesem Beitrag die sechs Reifegradstufen des acatech Industrie-4.0-Maturity-Index mit der Aufgabensicht des Aachener PPS-Modells kombiniert und die Reifegradstufen für ausgewählte Unteraufgaben der PPS spezifiziert.
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.
In an increasingly changing market environment, the long-term survival of companies depends on their ability to reduce latencies in adapting to new market conditions. One strategy to meet this challenge is the anchoring of data-driven decision making, which leads to an increasing use of advanced information technologies and, subsequently, to an increase in the amount of data stored. The complexity of processing these data spurred the demand for advanced statistical methods and functions called Business Analytics. Companies are, despite all promised benefits, overwhelmed with the implementation of Business Analytics as indicated by a failure rate of 65 to 80 %. This paper provides an empirically validated, multi-dimensional model that takes an integrative look at critical success factors for the implementation
of Business Analytics and based on which management recommendations can be generated. For this purpose, constructs of the model are conceptualized, before a structural equation model is developed. This model is then validated with data from 69 industrial partners in the food industry. It is shown amongst others, that the three success factors top management support, IT infrastructure and system quality are pivotal to increase the company performance.
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]
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]