1. Unplanned maintenance can be extremely costly due to extended production downtimes
2. Unexpected production loss may affect supplier obligation, resulting in significant unanticipated costs.
While machine learning can take into account all available data and past history to predict the likelihood of failure for a given machine, decision optimization (DO) can take it a step further and generate a schedule that is optimal for a set of machines, subject to limited resources (e.g. maintenance crew availability), other constraints and dependencies (production plan and repair costs), and optimization metrics (minimizing total cost, minimizing late maintenance).
So it should provide business values like
• Boost Quality and Yield Performance
• Decreased maintenance costs,
• Saving on consumables and spare parts
• Enhanced Productivity
• Reduce Scrap
• Reduced equipment downtime period
• Improve Overall Equipment Effectiveness
• Optimized maintenance schedule
• Increase throughput by decreasing production lead times
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