In 2020, a major consumer goods company approached KIS to improve the efficiency of its powdered soap manufacturing division. Their factory was already the most technologically advanced of its type in the world, and the company’s team was well-qualified to interpret and validate their data. But they were spending millions in materials and operating hours running test batches to formulate their products. The company knew there had to be a better approach. How could they accurately predict the quality of their product without wasting so much money? To find the answer, they turned to KIS.
Each soap product manufactured by the company featured a unique density of materials. Factors like temperature, water level, vacuum pressure, etc., all contributed to the densities.
To accurately predict the variables that produced each product’s density, we needed data taken from each production phase. Fortunately, the client had already outfitted the factory with sensors at each phase that took measurements and sent the data to a cloud.
Our challenge was to use this data to create three Machine Learning models capable of reliably predicting density after each phase.
With these models in place, users could adjust input variables to create new products and validate existing product formulas virtually without running physical test batches, saving the company time and money while simplifying the workflow.
To address the client’s problem, we broke the production process down into three distinct phases:
Our plan was to create models to predict the product’s density after each phase.
At first glance, Machine Learning seemed like the perfect solution for the client’s problem. But we wanted to be sure.
Often, ML is like a hammer in search of a nail. Due to the buzz surrounding it, companies use ML to address problems otherwise easily solved by more conventional means. KIS isn’t one of those companies.
We began by analyzing the client’s data to make sure Machine Learning really was the best solution.
The client sent KIS 18 months' worth of data on the three soap products produced at the factory. Our team validated the data and looked for correlations that would help ML models accurately predict product density.
In the end, our ML experts found that the project was a perfect match for Machine Learning’s capabilities.
Now that we’d determined that Machine Learning was the way to go, we had to decide which ML models would work best for this application. There are hundreds of ML models available, and choosing the best one for a given project is no easy task.
However, our experts understood the kinds of ML architectures that worked best with the data provided by the factory sensors. Instead of trying different models randomly, they came up with the best models for the job.
After only a month spent validating the data and running benchmarks, we arrived at a model that predicted outcomes from the drying and mixing phases with a maximum accuracy of 92%! This far exceeded human capabilities and proved that our models could reliably replace physical testing.
But, there was one area where KIS still saw room for improvement: The User Experience.
As accurate as our new Machine Learning models were, they wouldn’t do the company much good if no one knew how to use them.
Our solution was an attractive, user-friendly interface that replicated the factory virtually. This “digital twin” of the factory allowed users to visualize the production processes, manipulate product formulas, and read the outcomes as easily as if they were running physical test batches.
By placing ourselves in the employees’ shoes, we came up with a solution that not only worked but made their jobs even easier.
With our models implemented, the factory’s efficiency increased. The company’s ability to validate its products was at an all-time high, and the client was no longer wasting valuable time, money, and materials running test batches.
By taking a practical approach to this project, KIS implemented advanced Machine Learning technology in a way that solved the client’s production problem and simplified their factory’s workflow.
Without jumping to flashy solutions or overcomplicating the issue, we addressed the problem with the exact level of complexity it demanded.
Our team worked quickly and efficiently, creating ML models in under a month with enough accuracy to eliminate the need for physical testing at the factory.
As a result, these models saved the client from wasting materials and operation costs while providing employees with an easy-to-use UI that makes validating product formulas quick and easy.
The client was thrilled with the result and continues to work with us to this day. KIS is currently in the process of implementing the next model in the series which will no doubt continue to increase efficiency.
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