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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]
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]
Industrie-4.0-Technologien können dazu beitragen, die deutlich ge-steigerte Daten- und Informationsverfügbarkeit während des Produk-tionsprozesses gezielt zu verwerten und den Austausch zwischen pro-duzierenden Unternehmen und Logistikdienstleistern für effiziente und nachhaltige Lieferketten zu fördern. Im Projekt 'SynLApp' wird hierzu ein webbasiertes Assistenzsystem konzipiert, das Prognosen innerhalb der Lieferketten ermöglicht. Dieser Beitrag zeigt aktuelle Rechercheergebnisse zu bestehenden Methoden und Best Practices hinsichtlich der unternehmensübergreifenden Synchronisierung und beschreibt das Projektvorgehen und den Ansatz einer technischen Umsetzung via Simulation.
Digital Leadership
(2020)
This article describes digital leadership-specifically character and competency-that differentiate digitally mature organizations from digitally developing organizations. We assess the differentiated actions of leaders of digitally mature organizations and discuss their results. The study is based on Patterns of Digitization survey with insights from 559 decision makers across five geographic regions-America, Europe, Asia, Africa, and Oceania designed to assess how companies are implementing digital transformation, the various strategies they employ, the investments they make, and the actions they take to achieve large-scale institutionalized digital transformations. The insights gleaned from the study should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to catch up.
Patterns of Digitization
(2020)
This article describes the results of Patterns of Digitization survey designed to assess how companies are implementing digital transformation. The survey includes the various strategies companies employ, the technologies they invest in, and, in particular, the actions they take to overcome the organizational resistance that is common to most large-scale transformations. Digital transformation is reshaping entire segments of our society and industries of every type:
communications, retail, and increasingly healthcare, medicine, agriculture, and manufacturing.
While a few companies seem to reach front-runner status, the majority seem to lag. This phenomenon is a top concern of boardrooms worldwide and motivated the development of this study. To help these organizations, we highlight the important actions all companies are taking as well as the differentiated actions digitally mature companies are undertaking to transform their businesses. These insights should help lagging companies understand what is involved in
implementing a digital transformation and what they need to do to catch up.
Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.