Clustering and topic detection on factory error reports
The pharmaceutical company Boehringer Ingelheim manufactures a number of diabetes and cancer medication and animal health products.
In the manufacturing, packaging and distribution facilities, employees monitor the equipment and product at all stages for any anomalies.
The supplier used a Natural Language Processing clustering algorithm to assist pharmaceutical company Boehringer Ingelheim to gain insights into and discover topics in their manufacturing processes. They analysed the text dataset with NLP techniques and built a clustering model (topic discovery) using Latent Dirichlet Allocation.
The result of the static analysis allowed Boehringer to see clearly which were the main causes of manufacturing errors in their supply chain.