Machine Data Analytics: Identification of equipment data relevant for production performance – Bayer



Company Name / Department

Bayer, Consumer Health, Product Supply

Contact Person

Marc Moos, Christian Peter Klein


GP Grenzach Produktions GmbH, Grenzach, DE

Study programme(s)

Industrial Engineering

Community Data2Move
Start Date Sept/ Oct 2022
Housing arranged by company No


1611 EUR per month


Company Description

The purpose of Bayer Consumer Health Product Supply is to build the products our consumers love and that transform their lives.
Product Supply provides about 9,400 products to Bayer Consumer Health. Our 5,100 employees at 12 Bayer-owned production sites, as well as more than 200 external contract manufacturers, produce and ensure a steady and flexible supply of these products to our customers and consumers.

Consumer Health Product Supply´s vision is to continuously advance our trusted daily health solutions with passion, competence, and innovation. One of our strategic enablers is the digitalization of our network.

In this context we are now offering following internship at our supply center Grenzach, Germany, in the south-west of the country close to the Swiss and French borders and center of excellence for Bayer`s ointment and cream production.

Project Description

Accessing all relevant production data and analyzing this data is an important foundation for digitalization initiatives within the supply centers of Bayer Consumer Health.

The project offered in this internship involves collection, filtering and first analysis of production data with the help of common data analysis methods for improving the production processes on a deep-dive data base. A technical understanding of the considered production equipment and its associated data should be developed in order to prioritize the performance-relevant production data and reflect the results together with production experts. A general understanding of data access and data automatization (e.g. from AWS cloud, SAP) is desirable. The focus will be on the analysis of downtimes and scrap of filling and packaging production lines.

The results should be developed in a generic way so that the improvement concept  can be ideally transfered, shared in a knowledge base and used for future digitalization activities.

Goals of the Project

The goal of the project is to analyze and automate production data and based on that identify relevant parameters for production improvement (e.g. equipment downtime and scrap reduction).  At best, this developed data-based approach can be rolled out to other production equipment in general.


Identified set of important equipment data relevant for production performance.  

Essential Student Knowledge

Engineering (e.g. mechanical / electrical), business informatics, informatics, or data science background with German and English language skills, preferably with first data analytical and programming experience.

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