Analytics

Success factors that had long driven China’s economic boom have lost their legitimacy gradually. The emergence of the competitive indigenous technology houses in IT, telecommunications and software industries in the past two decades has made China’s industrial digitalisation feasible, which the nation would urgently need to base its further growth more on technology and innovation. Consequently, China’s economic performance would become more solid and sustainable. Internet+ predicts the general direction of digitalisation in China’s industries and represents the concept that is currently enthusiastically debated by the economists, politicians and in the media. The integration of information technology with other manufacturing industries is particularly emphasised in this context. The article examines the background and the terminology, takes a look at the market model and some technical issues of Internet+. A case study accompanies the “excursion” to China, in order to give ...

The advancing digitization leads to new possibilities for the design and digital support of business processes. In particular, non-R&D-intensive, mostly small and medium-sized enterprises, face great challenges in realizing these potentials. In the context of this article, various application scenarios are outlined. A detailed example of a non-R&D-intensive company shows how the procurement can be supported by the analysis and forecasting of relevant data, e.g. process data or the availability and costs of components, as well as the creation of the offer.

Over the last years, the importance of logistics has changed significantly. While logistics used to be a core function of most companies, logistics services nowadays are often outsourced to service providers. This leads to new organizational structures and enables innovative business models. Caused by the digitalization of logistics, efforts for integration and coordination rise and can only made controllable by intelligent use of IT. This contribution examines the field of tension of logistics and IT. It shows which challenges to face and how to overcome these by using Big Data technologies.

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.

Data Science is an established tool for knowledge discovery, in particular from economic data. The progressing digitization of products and production systems enables the broader application of Data Science in technical systems. However, the requirements and constraints, e.g. for control and optimization of production processes, differ significantly from established Data Science applications. Industrial Data Science addresses the issues of applying machine learning to technical systems in industrial setups. This article characterizes challenges of Industrial Data Science, gives application examples of and general indicators for Industrial Data Science.

Economy, enterprises and employees are sustainably affected by digital transformation. The new opportunities of education and training to evaluate learner behavior digitally are of particular interest. Learning analytics can be used for specific optimization of learning forms and content for the benefit of learners and of the training company.

Big Data Analytics and defined Key Performance Indicators (KPI) support the compliance organization´s scope of duty to continuously monitor and improve the compliance management system in a proper way. In order to perform their supervisory role, the management board needs a compliance monitoring and reporting on a regular basis. The article discusses the role of Big Data Analytics and the design of Key Performance Indicators (KPI) to establish an adequate monitoring and reporting system.

Analyzing and optimizing labor productivity is an important task in productions with high personnel costs. Existing methods allow to identify potentials require a lot of effort. This article presents an automated approach to analyze labor productivity with 3D cameras. The developed method is based on the primary-secondary analysis.

The importance of data science for production and logistics continues to grow because more data are available due to Industry 4.0-Applications used for process and system optimization. In addition, the improved methods and tools for data analysis enable an easier processing of application-specific issues. This article is the second part relating to data science in production and logistics. While the first article dealt with the definition of terms and the potential of data analysis, the article at hand is dedicated to the application of data science in production and logistics by means of various application examples.

The implementation of industry 4.0 concepts requires a new understanding of data processing and analysis. Data Science integrates approaches of mathematical modelling and performant implementation to analyse data of specific application areas. Within this first article, the basics of Data Science are presented and perspectives for a data-driven production and logistics are discussed. Within a second article in a following edition, the process steps for structured data analysis will be explained and illustrated by means of application examples.