After processing operational data spanning four months, we identified approximately 1,500 batches, revealing 55 valid parameters.

We developed machine learning models to implement clustering and identifying similar batches based on the operational parameters. The objective is to identify the top 18 parameters among the 55 that are responsible for causing defects.

Based on data generated from benchmarked batches, ideal range recommendations were provided for all the identified important features.

A web application was developed to assist floor operators to make informed decisions regarding input parameter ranges and obtain predictions on bar softness and cracks. Regression techniques are employed to predict bar softness, while classification techniques are used to predict bar cracks within a batch.

We initiated the project by conducting internal research on the soap manufacturing process, engaging with industry experts, and visiting the client’s manufacturing plant to communicate with their on-site team. We also collaborated with SMEs to identify potential factors that could contribute to defects. Based on this, a factor map was developed, and hypotheses were formulated for testing.

The client uploaded data from legacy systems onto a drive. However, as the data was not initially intended for analytics, it was scattered across numerous unstructured files. To address this, custom algorithms were developed to clean and consolidate the data. Around 1,500 batches were identified, and the analytical dataset (ADS) was constructed.

Once the data processing was complete, an exploratory data analysis (EDA) was performed to assess data sufficiency, accuracy, and eliminate unnecessary information. Clustering techniques were employed to identify similar batches within the dataset, which led to the formation of four distinct clusters. From these clusters, two were singled out: one characterised by low bar softness and the other by high bar softness.

Subsequently, we merged these clusters, and machine learning algorithms were employed to determine key factors contributing to bar softness. Upon further analysis, it was found that out of the 55 parameters, 18 were identified as highly influential in determining bar softness. By leveraging advanced statistical techniques, optimal parameter ranges were derived and shared with the plant team.

During EDA, we also discovered that implementing golden batch recommendations to minimise bar softness resulted in a reduction in bar cracks. To validate this hypothesis, a simulation analysis was conducted.

A web application was created. This application served as a platform for operators to input parameter values conveniently. In the backend, machine learning models were utilised to retrieve the predicted Penetration Value (PV) and the probability of encountering bar cracks. These models analysed the provided values, delivering precise predictions for PV, and evaluating the likelihood of bar cracks.