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Eine Transformation findet einen Abschluss, nachdem der gewünschte Zielzustand erreicht wurde. Wie sieht es bei der digitalen Transformation aus? Kann es im Hinblick auf technologische Entwicklungen jemals zu einem Ende kommen? Oder befindet sich ein Unternehmen hierbei in einer kontinuierlichen Transformation durch die Weiterentwicklung der Digitalisierung? Wenn ja, wie kann ein Unternehmen mit diesem ständigen Wandel effizient und sicher umgehen? (Quelle: https://link.springer.com/chapter/10.1007/978-3-662-63758-6_17 )
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.
Subscription business models provide an important component for monetizing the potential of Industrie 4.0. Subscription business is based on a long-term and participative business relationship between customer and provider. However, only digitalization offers the necessary framework conditions to realize the characteristic recurring and performance-based billing, and to ensure the necessary transparency about the usage phase of products as well as continuous performance improvements in the customer process. Against this background, companies must not only recognize the much-cited potential that lies in the total dedication to the success of individual subscription customers. Rather, the central obstacles must be addressed, examined, and subsequently overcome in a targeted manner in order to successfully establish subscription business models and place them on the market.
The digital transformation is changing the way companies think and design their manufacturing environment. Both due to the increasing number of connections between IoT-Devices, tooling machines, and production lines and the phenomenon of the convergence of IT and OT, systems are becoming more complex than years ago. Organizational and cultural changes within manufacturing companies strengthen this trend and form Industry 4.0 environments and cyber-physical production systems (CPPS). As these systems do not longer stay alone but are connected to each other and the company’s outside, the size of the potential attack surface is increasing as well. Besides that, manufacturing companies, small and medium-sized in particular, are facing complex challenges based on lack of knowledge, budget, and time to understand as well as to interpret their current situation and risk level and therefore to derive necessary counter-measures. Efficient as well as pragmatic tools and methods for these companies do not exist. This paper shows a research approach in which the company-specific set-up of Industry 4.0 environment and CPPS is characterized by its potential vulnerabilities. This enables companies to evaluate their risk potential before setting up this kind of environments and to undJo,erstand the potential consequences more precisely. By doing so, companies can derive and prioritize important counter-measures and so to strengthen their level of cyber-security efficiently. This will decrease the number of cyber-security attacks and increase the company’s competitiveness.
Feasibility Analysis of Entity Recognition as a Means to Create an Autonomous Technology Radar
(2021)
Mit den neuesten Technologietrends auf dem Laufenden zu bleiben, ist für Fertigungsunternehmen eine entscheidende Aufgabe, um auf einem global wettbewerbsfähigen Markt erfolgreich zu bleiben. Die Erstellung eines Technologieradars ist ein etablierter, jedoch meist manueller Prozess zur Visualisierung der neuesten Technologietrends.
Der Herausforderung, Technologien zu identifizieren und zu visualisieren, widmet sich das Projekt TechRad, das maschinelles Lernen einsetzt, um ein autonomes Technologie-Scouting-Radar zu realisieren. Eine der Kernfunktionen ist die Identifizierung von Technologien in Textdokumenten. Dies wird durch natürliche Sprachverarbeitung (NLP) realisiert.
Dieser Beitrag fasst die Herausforderungen und möglichen Lösungen für den Einsatz von Entity Recognition zur Identifikation relevanter Technologien in Textdokumenten zusammen. Die Autoren stellen eine frühe Phase der Implementierung des Entity Recognition Modells vor. Dies beinhaltet die Auswahl von Transfer Learning als geeignete Methode, die Erstellung eines Datensatzes, der aus verschiedenen Datenquellen besteht, sowie den angewandten Modell-Trainings-Prozess. Abschließend wird die Leistungsfähigkeit der gewählten Methode in einer Reihe von Tests überprüft und bewertet.
Through data-based insights into customer behavior, products and service offers can be improved. For manufacturing companies, smart product-service systems (SPSS) offer the possibility to collect customer data during the usage phase of the product. As the focus on customer analytics is too often on sales and marketing, SPSS are overlooked as a source of customer data. However, manufacturing companies need to integrate data from all interactions with their customers along the complete customer journey to achieve a holistic data-based view of the customers. To identify these interactions and the customer data derived from them, the concept of a digital shadow will be applied to the customer journey. The projected results for the presented work in progress are a reference process model for the customer journey in manufacturing and a data model of the customer data created along this process.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.