Management

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.

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.

Digitisation offers enormous possibilities but also holds entrepreneurial risks such as data security aspects and misinvestments. Especially small and medium sized enterprises (SMEs) are facing problems by trying to keep pace with digital progress. A helpful tool would be a classification scheme specifically designed for SMEs that shows to which degree the company has already implemented digitisation technologies. Therefore we discuss why such a model is crucial, conduct a comprehensive literature survey and outline a new model.

Recent advancements in cyber physical systems (CPS) and industry 4.0 concepts are expected to result in a disruptive change of business processes in industry and commerce. In particular, this refers also to supply chain management (SCM) and logistics systems and processes. Methodically, maturity models can be used to determine the maturity level of SCM and logistics organisations. In this paper we present an extension for a maturity model being able to check the industry 4.0 compatibility of SCM systems and processes. Moreover, the aim is to provide a tool supporting the transformation towards SCM 4.0-ready systems and processes. The requirements for the digital transformation process are described and important fields of actions are discussed.

Currently the implementation of digital technologies in response to important competition requirements is promoted in many places. Consequently, the working environment of employees in operative logistics is going to change significantly. This article provides an overview of the possible uses of future-oriented technologies in different logistics processes as well as the thereby changing subtasks and competence requirements of operative employees in the working world 4.0.

In the context of Industry 4.0 the change and digitalisation of technical information move more closely in the focus of research. During the whole life cycle of a product, relevant information arises, that has to be stored appropriately. Future industrial applications require adaptive information management, e.g., individual and appropriate delivery of data, connection of information, target-oriented search or visualisation of information. The focus of this contribution is on challenges of the digitalization of technical documentations with respect to Industry 4.0 paradigms. The handling of the technical documentation over the complete lifecycle of a product is discussed.

The developed Readiness Model supports enterprises to determine the current state in relation to Industry 4.0 as well as the target state. Based on strategy improvement measures are derived to succeed in attaining the target Industry 4.0 state. Enterprises profit by individualized understanding of Industry 4.0 and specific project proposals. The dissemination of Industry 4.0 is supported through this Readiness Model. In addition a benchmark data base is filled to enable comparisons of enterprises and to observe the historical development of the industry 4.0 readiness of several industries.

The increase of complexity in the field of production due to “Industrie 4.0” causes also a rapid increase of the complexity of tasks on the shopfloor level. Thus, efficient methods for the systematic identification of the competence deficits of the employees as well as new forms of knowledge management for an adequate administration of knowledge have to be established.

What do new technologies and the increasing cyber threat hold in store for business and production in 2016? How should organizations be preparing themselves? What should IT security leaders be doing as a priority in the coming year? These are the questions of leading security analysts and consultants at TÜV Rheinland to tackle. The year 2016 will see an increasing number of attacks and the emergence of new targets. The complexity and sophistication of attacks, initiated by increasingly capable and technically well-equipped cyber criminals, will continue to rise. Industrial Control System Security (ICS) and Incident Response is more important than ever.