Management

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.

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.