data analysis

The analysis of machine data offers a lot of potential for manufacturing companies. It enables the identification and prognosis of deficits in industrial production processes. Using the example of additive manufacturing, a practice-oriented procedure to implement such an analysis is presented in this article. Using a gradient boosting algorithm, it is shown how a leakage error can be identified as well as predicted. Furthermore, requirements for the necessary database are discussed and practical recommendations for manufacturing companies are derived.

Manufacturing Analytics is the evaluation and use of data in the production context. This article shows which potentials can be realised by Manufacturing Analytics in the context of reactive quality management. First, a general definition of the term Manufacturing Analytics is given and then its classification in the context of quality management is carried out. On the basis of a literature analysis and the evaluation of existing use cases, findings regarding the potentials for reactive quality processes are derived. This shows that Manufacturing Analytics is particularly promising and can be used in root cause analysis, defect detection and avoidance. Subsequently, an application example is presented.

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.