Manufacturing data science is the discipline of extracting knowledge from large volumes of manufacturing data. As data scientists, we solve challenges such as the reduction of failed batches, root cause analysis for unexplained manufacturing deviations and the optimization of manufacturing processes.
A typical project starts with a detailed discussion of the manufacturing process along process flow, block-flow diagrams and risk assessment documents. In customer workshops, we specify the goals of the analysis and discuss open issues and the available data sources. Frequently, we also visit the manufacturing process on-site. Thereby, we gain a deep understanding of the manufacturing process which is necessary for the efficient analysis using data science tools.
Next steps are the transfer of the manufacturing data to be analyzed. We retrieve data from databases, MES, LIMS systems or multiple Excel spreadsheets. Sometimes, our client provides us with unstructured data in paper form, for example batch records. Then, we digitalizes data from batch records available in paper form. Anyway, we find a way to get a grab on the data. As soon as we have a grab on the data, we apply our data science approach based on the inCyght data science framework: Information is mined from large datasets using our algorithm database, including software sensors, and analyzed using multivariate methods. This enables to develop clear and interpretable hypothesis for root causes. inCyght information mining makes it possible to display information – already present in huge amounts of data – in a clear and meaningful way in order to make interpretations possible.
Following this framework we identify the targeted root causes. This is the most rewarding step, when we finally get an understanding of the variances in the data and can trace a manufacturing challenge back to process variables. Once we have identified the underlying root causes, we set up customer workshops to discuss preventive- or optimization strategies to overcome the manufacturing challenges.
Summarizing, you see that a manufacturing data science is not a job that is performed closed doors. We interact with our customers and the manufacturing processes on a daily basis. This requires, next to data science skills, inter-personal skills and engineering know-how.