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4.4% increase in productivity with ML data analysis

5 minutes read
case-study
ClientNDA
IndustryIndustry 4.0

Our manufacturing client assembles air-blowing technology that plays a key role in the final manufactured product. The high scrap rate during production led to a significant profitability loss. The company had aimed to reduce scrap waste by at least 1%. This reduction would trigger a healthy $330k savings each year.

 

These key metrics lay the groundwork for a strategic investment aimed at improving production processes, reducing defects in manufactured components, and increasing the accuracy of blower balancing procedures.

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By utilizing machine learning (ML) data analytics and conducting rigorous testing, VirtusLab achieved a 4.4% reduction in the client's scrap rate, improving manufacturing efficiency. This helped facilitate smarter manufacturing processes overall.

The challenge

Although well-planned, the client’s production process resulted in a high scrap rate. Furthermore, scraps obtained at particular points in production could not be reprocessed or repurposed. After several failed attempts to solve this issue, our clients asked themselves if repetitive rebalancing was worth performing at all! This was the point in time when our client contacted VirtusLab.

The solution

VirtusLab received historical data about the production of two product types and collected more data to fill in some gaps. This helped us understand the manufacturing process, its technology, and environment, bringing us closer to the root cause of the high scrap rate.

We created a machine learning model based on our client’s know-how. Every step within the production process delivered data about step-specific errors. One error stood out during the first balancing attempt in both production processes of the two product types. The data also showed a steady increase in errors before the balancing attempt. 

The model delivered predictions showing how changing specific parameters could benefit production. VirtusLab found patterns to optimize production processes and indicate when a part certainly becomes scrap. According to these findings, VirtusLab suggested eliminating more products at earlier stages.

The results

Due to our consultancy, our client saw results in the first four days: 

  1. Decreased overall production time, which also helped increase the number of produced goods. 
  2. Enabled pinpointed analysis of specific parameters that had led to instances of production scrap. 
  3. Scrap rate reduction by 4.4%. 
  4. OEE increase by 4%. 

The Tech stack

Analysis

Dataiku_logo

Modelling part

python-logo

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VirtusLab's work has met the mark several times over, and their latest project is no exception. The team is efficient, hard-working, and trustworthy. Customers can expect a proactive team that drives results.

Stephen Rooke
Stephen RookeDirector of Software Development @ Extreme Reach

VirtusLab's engineers are truly Strapi extensions experts. Their knowledge and expertise in the area of Strapi plugins gave us the opportunity to lift our multi-brand CMS implementation to a different level.

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Leonardo PoddaEngineering Manager @ Facile.it

VirtusLab has been an incredible partner since the early development of Scala 3, essential to a mature and stable Scala 3 ecosystem.

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Martin OderskyHead of Programming Research Group @ EPFL

The VirtusLab team's in-depth knowledge, understanding, and experience of technology have been invaluable to us in developing our product. The team is professional and delivers on time – we greatly appreciated this efficiency when working with them.

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Michael GrantDirector of Development @ Cyber Sec Company