Management

The GDPR’s State of the Art

The GDPR’s State of the Art

Effects of a loosely embedded reference term on the example of identification and consent
Fabian Stephan, Christian Koot
The GDPR’s state of the art is a reference term - decoupled from fixed technical and organizational standards at a given time. Therefore, it is vital to define how requirements should be methodically derived from the state of the art, as stating no hint in the regulation led to insecurities amongst the GDPR’s addressees. This article presents an approach from the German IT association TeleTrust which can help companies to reduce their insecurities. The problems with the state of the art in the effort saver digital world are shown on the example of identification and consent.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 5 | Pages 63-66
Determining a Promising Industry 4.0 Target Position

Determining a Promising Industry 4.0 Target Position

Decision-making for companies taking into account external influences
Christoph Pierenkemper, Jannik Reinhold, Roman Dumitrescu ORCID Icon, Jürgen Gausemeier
Using industry 4.0 maturity models, companies can systematically record their performance in the context of industry 4.0. When the status quo is determined, the question “Where do we want to be in future?” is usually associated at the same time. However, companies are not always in a position to introduce what is fundamentally possible. Therefore, this question is not trivial. If a company is supposedly aware of its I4.0 target position, external influences often lead to the fact that the achievement of the target is made more difficult or hindered. It is therefore important to take these circumstances into account. This paper shows how environmental developments can be taken into account when determining a promising I4.0 target position. The target position forms the starting point for the implementation of industry 4.0 in the company.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 5 | Pages 30-34 | DOI 10.30844/I40M_19-5_S30-34
Industrial Realization of Knowledge-Based Maintenance Strategies

Industrial Realization of Knowledge-Based Maintenance Strategies

Ein instandhaltungsspezifisches Reifegradmodell für Produktionsunternehmen am Weg zur Smart Factory
Tanja Nemeth, Fazel Ansari, Wilfried Sihn
In order to cope with the complexity and automation of cyberphysical production systems (CPPS), knowledge-based maintenance (KBM) strategies and models have been identified as a key factor. They are intended to secure and improve machine availability and process stability. Although many companies are willing to invest in these innovations, they lack the certainty of having the necessary competence and capacity. In order to overcome this problem, the authors present a holistic process model for the evaluation and identification of strengths and weaknesses on an operational, tactical and strategic level by applying a multidimensional analytical approach.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 5 | Pages 17-20
Managing Digital Transformation

Managing Digital Transformation

Wie Unternehmen die digitale Transformation strukturiert meistern
Roman Dumitrescu ORCID Icon, André Lipsmeier, Thorsten Westermann, Arno Kühn
Digitalization is a strategic core issue that has to be anchored in the strategy of every company. The challenge in this context is that there is no uniform pattern for the digital transformation of a company. Instead, each company has to develop its own company-specific plan how it will position itself in the context of digitalization. Furthermore, the development of an individual digitalization strategy is required. The following article presents a planning approach for the development of such a digitalization strategy, based on three major steps.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 55-58 | DOI 10.30844/I40M_19-4_S55-58
Agility as Consequence or Prerequisite of Digitization?

Agility as Consequence or Prerequisite of Digitization?

Dominic Lindner, Michael Amberg
Companies have always been in a constant state of change. This change is today closely linked to the buzzword’s “digitization” and “agility”. Agile methods, especially in complex projects, can pave the way for targeted digitization and, on the other hand, provide a more agile way of working for digital technologies. Through group discussions with managers from small and medium-sized IT companies, this article focuses on the question of whether agility is the precondition or consequence of targeted digitization. This article is aimed at decision-makers from SMEs who want to increase the degree of agility in the company in the context of increasing digitization.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 30-34 | DOI 10.30844/I40M_19-4_S30-34
Process Model for the Industry 4.0

Process Model for the Industry 4.0

Structured Introduction and Implementation of Digitalizations Measures in the Manufacturing Industry
Simon Hennegriff, Sebastian Terstegen, Sascha Stowasser, Holger Dander ORCID Icon, Patrick Adler
Comparison and evaluation from research findings of 28 process models considering digitalization measures are presented. Furthermore, our own-developed process model, based upon interviews with professional managers, is reported. Our process model enables managers to deal with technology coming along with industry 4.0, such as the implementation of socio-technologies.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 47-50
Changes in Practice, Identity, and Knowledge in the Industry 4.0

Changes in Practice, Identity, and Knowledge in the Industry 4.0

Barbara Kump
When digitalising and automating work processes, it is often overlooked that this can trigger serious changes for the organisation. This article shows that such changes can lead to an incongruence between “what an organization does” (practice), “what it can do” (knowledge) and “who it is” (identity). These incongruities must be overcome in order to implement change successfully. If managers are aware of this, many problems such as the collapse of existing routines, knowledge gaps or the departure of important employees can be foreseen and solved.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 2 | Pages 18-22 | DOI 10.30844/I40M_19-2_S18-22
Common Sense Instead of MBA

Common Sense Instead of MBA

How to recognize sustainable leaders
Hans Rosenkranz
Management tools are a dime a dozen. The US-American strategy consultancy Bain & Company, for example, analyses regularly the 25 most popular of them worldwide. However, the best tool is only as good as its user. The proper and efficient utilization requires common sense. If a manager has it or not can be identified by the following qualities: He knows that others see him different from how he sees himself. He sets high value on a respectful feedback culture in his company, and he counts on the power of cooperation.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 2 | Pages 57-60 | DOI 10.30844/I40M_19-2_S57-60
Systematic Goal Definition in Digital Change

Systematic Goal Definition in Digital Change

Development of a Checklist to Support Digital Change Processes
Lisa Mlekus, Günter W. Maier
Companies are increasingly acquiring new technologies that enable higher quality and efficiency. Every technology adoption is also a change process which affects the employees and their work and thus needs to be managed in an optimal way. This article is focused on the importance of goal definition during a change process. To facilitate this process, a checklist with 81 goals is presented. The checklist was developed based on scientific literature and practice-oriented tools and can be used by project teams to focus their activities on a holistic change process and track the goal progress.
Industrie 4.0 Management | Volume 34 | 2018 | Edition 6 | Pages 60-65
Edge Computing from the Perspective of Artificial Intelligence

Edge Computing from the Perspective of Artificial Intelligence

Dirk Hecker, Michael Mock, Joachim Sicking, Angi Voss, Tim Wirtz
Machine learning is the key technology of almost every instance of modern Artificial Intelligence. Enormous datasets are produced in digitized industrial processes and in the Internet of Things, which can well be exploited by learning in deep artificial neural networks. Standard machine learning algorithms require these datasets to be centralized before learning a model. Several good reasons - ranging from data privacy over latency to economic efficiency - favor learning at the edge so that reasoning is fast and no local data is transferred. The article shows how decentralized learning works and how to evaluate it. Moreover, we point to special resource-efficient learning algorithms and discuss small remaining risks of data reconstruction.
Industrie 4.0 Management | Volume 34 | 2018 | Edition 6 | Pages 13-16
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