useR!2017

Keywords: data science, predictive maintenance, industry 4.0, business, industry, use case,
The buzz for industry 4.0 continues – digitalizing business processes is one of the main aims of companies in the 21st century. One topic gains particular importance: predictive maintenance. Enterprises use this method in order to cut production and maintenance costs and to increase reliability.
Being able to predict machine failures, performance drops or quality deterioration is a huge benefit for companies. With this knowledge, maintenance and failure costs can be reduced and optimized.
With the help of R and its massive community, analysts can apply the best algorithms and methods for predictive maintenance. When a good analytic model for predictive maintenance has been found, companies are challenged to implement them in their own environments and workflows. Especially regarding the workflow across different departments, it is necessary to find an appropriate solution which is capable of interdisciplinary work, as well.
My talk will show how this challenge was solved for TRUMPF Laser GmbH, a subsidiary of TRUMPF, a world-leading high-technology company which offers production solutions in the machine tool, laser and electronic sectors. I would like to share my experience with R and predictive maintenance in a real-world industry scenario and show the audience how to automate R code and visualize it in a front-end solution for all departments involved.