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Eine Steigerung des Wertbeitrags der Instandhaltung kann nur im Zusammenspiel mit der Belegschaft sowie internen und externen Anspruchsgruppen geschehen.
Dabei bieten digitale Technologien eine Möglichkeit, Prozesse und Entscheidungen punktuell effizienter und besser zu machen. Eine nachhaltige Transformation muss jedoch nicht nur technologisch, sondern vor allem auch methodisch gestaltet werden.
Dabei müssen bewährte Methoden der zuverlässigkeitsorientierten
Instandhaltung und des Lean Managements mit den digitalen Technologien zusammenspielen, um den größtmöglichen Effekt für das Unternehmen zu erzielen.
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.
Today, however, agility is seen more than ever as a critical success factor for companies. In times of an increasing degree of digital interconnection and minimum viable products, a mentality is entering the industrial service sector that has so far only been exemplified by Internet companies (e.g. Google): New products and especially digital services are developed in highly iterative processes. To this end, customers are involved in early test phases of development and provide feedback on individual functional modules, which – in contrast to the previous approach – are only gradually assembled into a market-ready “100 percent version”. But especially with the development of new digital services, companies must ensure more than ever that both the existing analog service business and the design of new digital services are geared to effectiveness and efficiency in order to meet the growing demands of customers and competitors.
To achieve this, companies must not only be familiar with the products currently on the market, but also master the entire product history, which in some cases goes back more than 30 years and varies greatly from one industry to another.
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.
Augmented Reality (AR) bietet ein großes Nutzenpotenzial im Bereich der industriellen Dienstleistungen. Der genaue monetäre und qualitative Nutzen ist jedoch, wie bei IT-Investitionen im Allgemeinen, schwer zu bewerten. Im Rahmen des Forschungsprojekts Datenmanagement for Augmented Reality (DM4AR) wurde aus diesem Grund ein Bewertungsmodell entwickelt, welches den Nutzen von AR im industriellen Service messbar macht.
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]
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
Ziel des Beitrags ist es, aufzuzeigen, wie produzierende Unternehmen entlang der Customer-Journey systematisch kundenbezogene Daten erheben können. Nach einer Einleitung zur Motivation der Themenstellung, einer Begriffserläuterung und einer Vorstellung des Studiendesigns wird ein Referenzprozessmodell der Kundeninteraktionen produzierender Unternehmen gestaltet, darauf aufbauend ein Datenmodell des digitalen Schattens der Kundeninteraktionen abgeleitet und zuletzt ein Vorgehensmodell zur Implementierung des digitalen Schattens der Kundeninteraktionen präsentiert.
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.
Manufacturing companies (MFRs) are increasingly extending their
portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.