Manufacturing
Optimizing Plant Maintenance and Scheduling
Business Need
A leading furniture manufacturer faced significant issues with machine breakdowns during the manufacturing cycle. These breakdowns caused delays in delivery timelines, leading to customer dissatisfaction. The downtime of a single machine often created a ripple effect, resulting in delays across the entire assembly line. Additionally, the company incurred substantial monetary losses as laborers had to be paid hourly wages even while idling, waiting for the machines to become operational again.
Solution
The CA team analyzed the frequency of proactive versus reactive maintenance and recommended adjusting the schedules based on machine usage, breakdown history and cost analysis, ensuring more effective proactive maintenance. The team developed and implemented an intelligent recommendation model to automate production scheduling, based on order history and business rules, thus optimizing efficiency.
Highlights
Data-Driven Maintenance SchedulingAutomated Production SchedulingPredictive Analytics for Asset Management
Outcome
The optimized maintenance schedules significantly reduced reactive maintenance costs and minimized the impact of downtime on the manufacturing cycle. Automated scheduling led to a 72% increase in throughput and faster delivery, with the model reaching 90% accuracy, further improving efficiency and reducing labor costs.
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