Analytics

Opportunities arising from new technologies like smart mobile devices and augmented reality have a huge impact on the manufacturing industry but are not taken advantage of when it comes to productivity analysis. As a consequence productivity analyses are rarely used and companies cannot benefit from a systematic approach to tackle improvement processes. This paper presents a productivity analysis method that uses a web-based application for data acquisition and is designed in a way that enables production staff to perform analyses on their own.

A core element of Industry 4.0 and IoT is the ability to collect extremely high volumes of data in real time. [1] In order to achieve a benefit for business process optimizations, it is necessary to generate knowledge from this data. For this, conditions must be created with regard to infrastructure and organization. This article describes how to use a knowledge discovery process model in production logistics in combination with the Acatech-I-4.0 reference model to derive a procedure that uses data analytics to systematically exploit the benefits of I-4.0 in business processes.

Managers across the globe discuss the impact of digitalization on their business models. Among many other functions, procurement is strongly impacted by this development. However, “Procurement 4.0” goes far beyond the increased use of IT systems. It also has an impact on strategic dimensions such as supplier relationships. Data in this context will have a more important, if not critical, role- whether as some sort of “currency” in negotiations or as the basis for many procurement decisions, from operative order processing to supplier selection.

There are numerous analysis methods available to support engineers working on continuous improvement projects. Digital transformation facilitates to reduce the effort for data acquisition and processing. The Institute of Production Management and Technology and the medical company Dräger have jointly developed a web application for multi-method analysis. This article describes its data structure and technology.

The ever increasing volatility in market conditions push the requirements of industrial companies for operational intelligence far beyond the capabilities of common business intelligence solutions. An integrated and comprehensive software solution was missing. Enterprise Operational Intelligence (EOI) closes the gap. EOI focuses Big Data on company strategies and (strategy-)compliant value chains. This incorporates a structured approach for all data to deliver value, including manufacturing-related data, Internet of Things and real-time processing, somewhat analogous to computer tomography. EOI allows leaders and managers to continuously adjust business processes to keep them in line with business strategies. Operations can be adjusted to new realities rapidly and the consequences of interventions can be understood quickly and comprehensively.