Use Case
Data-Driven Durability: Using Predictive Maintenance to Optimize Filling Valve Longevity and Spare-Parts Spending
The Problem
Beverage manufacturers struggle to efficiently manage the health and maintenance of their filling valves, resulting in potential downtime, high maintenance costs, and inefficient resource allocation. Neglected filling valves cause inconsistent levels, product contamination due to residue or bacteria buildup, and malfunctions that lead to unplanned downtime.
To combat this, operators overhaul their valves too frequently and keep unnecessary spare parts on hand, increasing overall part holding and maintenance costs.
To combat this, operators overhaul their valves too frequently and keep unnecessary spare parts on hand, increasing overall part holding and maintenance costs.
The Solution
Our Filling Valve Dashboard provides comprehensive information about each filling valve's health and longevity, reporting on the operating hours each filling valve has reached and its remaining usefulness.
Using machine learning, this model can then predict the optimal time to overhaul each filling valve and alerts of any deterioration abnormalities.
Maintenance teams can view a prioritized list of valves needing immediate attention, preventing unplanned downtime. More visibility into filling valve status saves money on overall maintenance through increased valve usage (only overhaul when you need) and reduces the risk of sunk cost in spare parts.
Using machine learning, this model can then predict the optimal time to overhaul each filling valve and alerts of any deterioration abnormalities.
Maintenance teams can view a prioritized list of valves needing immediate attention, preventing unplanned downtime. More visibility into filling valve status saves money on overall maintenance through increased valve usage (only overhaul when you need) and reduces the risk of sunk cost in spare parts.
Use Case Results
€48k
Saved annually per machine
40%
Reduction in total overhauls
3%+
Increase in OPL
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