I4S+ content for subscribers only

One ear on the phone while checking e-mails and revising a text at the same time - that’s life today in many offices all over the world. Constant digital change makes great demands of companies and their staff. People are expected to be available 24/7 while multi-tasking and coping with high noise levels in open-plan offices as well as dealing with constant disruptions in the work process. Together with working on-call or on-demand, these are just some of the challenges and tasks that have to be managed. How can staff continue to be efficient and productive despite all these and more changes to the working world? Are companies prepared for the demands of the digital age? Which adjustments will they have to make to protect staff from excessive demands on the one hand, while boosting efficiency and productivity on the other?

Current advances result in increasingly complex production programs. Through combination of additive manufacturing and Industry 4.0, new elements can be formed and - as a whole - enable to economically manufacture the above mentioned programs. The Bionic Smart Factory 4.0 provides a framework, structuring them in terms of relation and interaction. Their development and implementation is being promoted through their evaluation against the determinants of complex production programs.

In accordance to the study “Manufacturing Data Analytics” published by the University of St. Gallen in cooperation with RWTH Aachen in 2017, various aspects of industrial data usage are examined. Different topics such as technical systems, implementation status and organizational approaches are analysed. While some companies are still in a launching stage, other companies are already able to make predictions through comprehensive data collection and exploitation. Thereby, they can significantly improve their efficiency in production.

Industrial companies face the challenge to manage an increasing number of documents digitally and reliably. Document Management Software (DMS) enables the efficient generation, retrieval and filing of documents. However, the range of software solutions is vast. Furthermore, many introduction projects fail due to inherent organizational challenges. This article introduces a methodology for preparing the successful implementation of a DMS in both theory and practice.

The digital transformation led to disruptive changes in business models of leading companies. Big Data serves as one of the key enables in this area. The transfer of this concept in the production domain towards an Industrial Big Data is key challenge for producing companies. Although exemplary key projects exist, no available characterization of structural elements in Industrial Big Data Processes exists. Therefore, this article aims at presenting initial structural elements of Industrial Big Data projects based on exemplary use cases.

Rising requirements for converting machines lead to more complex processes. Working with biogenic materials results in ever changing properties and so breakdowns are unavoidable and occur frequently. Many plant operators lack of a profound knowledge of these processes and remediate the impact of a breakdown and not the cause. To give operators hints on lasting elimination the cause we proclaim a self-learning assistance system providing information based on the current situation. To detect situations, unknown to the PLC, machine learning algorithms for pattern mining on field level sensors are used.

Against the background of latest developments such as Cyber-Physical Systems and Internet of Things, manufacturers need to adapt their business models. This digital transformation is often associated with implementation challenges, even for market leaders. It will require further efforts and time before customers will benefit from these new trends. This article highlights the implementation reality in German and Swiss mechanical engineering companies, based on a multi-case study with examples from pharma, packaging and construction industry.

Intensified networking and digitalization of systems affect an increasing number of sectors. At the same time a great variety of different concepts, ideas, expectations as well as fears have emerged around Industry 4.0. A look into the newspapers is enough to understand that the profound connection of critical structures does also hold profound dangers. For the future it is crucial to consider a way of using the new mass of data and information to protect these structures. Evaluating big data and transforming it into smart data with support of Artificial Intelligence will be a significant security factor in the future.

Finished vehicle logistics is of particular importance for the automotive industry, because it is the link between manufacturer and customers. If problems occur during the distribution of a car, it becomes visible to the customer, unless the problems can be solved during the following processes. Process interfaces between companies are frequently causing issues. That is why a survey was executed in order to identify those issues at the interfaces between car manufacturers and logistics service providers.

With the industrial internet the digitization of communication processes receives a new impulse. By application of social networks within firms, the collaboration of and knowledge transfer between workers can be supported and rationalized. This paper focuses on Enterprise Social Networks and discusses the challenges and opportunities when implementing these digital communication tools. On basis of a real world case study we illustrate some good practices.