Manufacturing
Optimizing maintenance plans with predictive analytics to reduce unscheduled downtime and costs
Business Need
A Midwest food plant faced a common challenge: an outdated, schedule-based maintenance program that led to unnecessary spending and wasted resources. To meet market demand, food manufacturing production schedules rely on their machinery to stay up and running. Downtime means lost revenue. There had to be a better way to optimize their $20M annual maintenance budget.
Solution
Customer Analytics partnered with the food producer to implement a custom AI and machine learning solution, transforming their maintenance strategy from reactive to predictive. By analyzing diverse data points (machine metrics, production volume, and even weather), our model accurately forecasted potential downtime up to 7 days in advance. Maintenance managers now have a clear view of machine status, enabling them to prioritize high-risk activities and defer low-risk ones.
Highlights
Predictive Analytics for Maintenance SchedulesAdvanced AnalyticsCustomer Application Development
Outcome
In the first year for a single machine, an estimated one-third of all maintenance activities will be deferred or eliminated. This success is now being scaled across additional production lines, projecting $4.5 million in annual cost savings.
• 1/3 maintenance activities deferred or eliminated
• 23% projected savings on annual $20M maintenance budget
• Enhanced production consistency increases product revenue and margins
PREVIOUSUsing AI to Tighten Supply Chain Efficiency
NEXTOptimizing Plant Maintenance and Scheduling