Predictive Maintenance Solution for Connected Blenders

Executive Summary

Our client, a U.S.-based Food and Beverage company, faced significant hurdles in monitoring their connected blenders across a vast network of over 20,000 locations worldwide. Limited data variety and velocity, coupled with an imperfect dataset, hindered their ability to make accurate predictions and drive effective decision-making.

That’s where eInfochips came in. Our team developed a cutting-edge predictive model that could anticipate potential blender failures or catastrophic errors with remarkable accuracy. By predicting 90% of normal events and 80% of catastrophic errors, the model enabled proactive error management with a lead time of three events.

This game-changing solution allowed our client to optimize machine utilization, reduce downtime, prevent costly repairs, and ensure smooth operations across their extensive network. With eInfochips’ expertise, the company can now confidently serve delicious milkshakes, smoothies, and blended frozen drinks to customers worldwide, from convenience stores to military bases.

Project Highlights

Predictive Maintenance Solution for Connected Blenders
  • Data Preprocessing
  • Data Wrangling
  • Model Selection and Evaluation
  • Model Validation and Assessment
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