Quality prediction and driver analysis for french fry production
The french fries producer was facing difficulties in tracking fries back to potatoes. The fries manufacturing process comprised of 21 steps, which resulted in various trackability problems. The management wanted to understand the different factors that drive the fries’ quality and optimizing system settings based on potatoes.
Neal Analytics pulled data from various sensors and created unit traceability across a 21-step process using Azure Data Factory. We enabled real-time Power BI visualization of the data for real-time, shop-floor decision-making. The team also deployed machine learning algorithms to predict fry length and improve batch quality for increased yield.
By implementing machine learning, the customer was able to better identify key drivers of fry length and use those insights to adjust output. This has resulted in increased french fry quality and line yield. Real-time Power BI visualization has also helped identify and intercept quality issues with the potatoes, which resulted in higher manufacturing line yield.