In response, some manufacturers build in overcapacity to compensate for variances in unscheduled downtime in production. But the production of overcapacity takes capital assets that could be utilized. The better course of action? Reliability-centered production.
Knowing how, when and why equipment might fail or downtime may occur, enables operations managers to prevent it, thus increasing efficiency. Reliability-centered production enables operations to maximize existing assets and can help resolve inefficiencies in operations. It's a proactive approach that uses data in order to be effective.
Reliability-centered production requires an asset management system on the line that incorporates real-time information management, line configuration data and expert knowledge to augment the skills of the existing workforce. It can eliminate frequent downtime issues, reduce maintenance costs and potentially extend the life of plant equipment.
The workforce that takes ownership of manufacturing equipment has the largest impact on the equipment's performance and reliability. Focusing on reliability and risk, in conjunction with systematically improving system performance, relies upon individual machinery and production metrics (i.e. OEE) that are utilized to make strategic capital asset decisions.
Optimizing production additionally requires knowledge management tools that enable every production worker to make adjustments to enhance line performance and reliability. Tactical metrics are critical because they empower personnel with the knowledge to improve operations. Their counterparts, strategic metrics, drive long-term behavior and are useful at the management level to determine plant or line performance. These metrics can then be tracked and analyzed by a real-time asset management system.
Real-time asset management systems are more than visualization systems. As part of reliability-centered production, asset management requires systems capable of data collection, along with advanced algorithms that analyze machinery operational data in order to provide operators, maintenance and operations managers with actionable measures to improve overall line performance and reliability. Improving production machinery reliability requires understanding complex relationships between the individual machines to determine the most effective corrective actions. Leaving the decision to prioritize operational improvements such as fixing machinery jams or line balancing to production operators is too complex. Appropriate actions must be supported by analytics that optimize performance. In this way, supply chain excellence can be achieved as long as the metrics and analytics for reliability-centered production are in place.