Few deny that demands from the manufacturing and energy sectors will continue to drive industry growth. In this era of demanding conditions, operational efficiency has never been more vital. International pressures require machine shops to compete at a higher level, doing more with less, and manufacturing facilities have been forced into 24/7 operation. As a result, equipment reliability has become a core industry concern.
With increased competition saturating topline revenue growth, businesses are focusing on improving operational efficiencies and managing cost centers to impact their bottom lines. To achieve the coveted near-zero downtime, companies begin by evaluating their maintenance programs. Preventive maintenance had its place 20 years ago, but new technological advances allow for more refined and reliable methods of tracking machinery health and avoiding potential failures.
Predictive maintenance—as the name suggests—leverages data collection to enable users to predict when equipment failure might occur so they can implement maintenance before equipment breaks down. Several reliability-centered maintenance (RCM) programs attempt to do this with monthly or quarterly samplings of temperature, vibration, lubrication, loads, pressures and other parameters. Analytics enable engineers to move from an original equipment manufacturer (OEM) specified maintenance regimen to one that is defined by actual usage and observed failure intervals.
Prescriptive maintenance takes predictive maintenance to the next level by unlocking intelligence not only about when a machine will fail but about how it will fail. This allows engineers to prepare for specific failure modes and gather the necessary replacement parts. This knowledge also enables facilities to better learn and understand their equipment and to reduce unplanned downtime. These analytics focus on asset efficiency, which helps companies get ahead of failures and increase uptime.
Asset efficiency is critical, but, in order to fully thrive, companies must improve their overall operational efficiency. Five key metrics define operational efficiency, and continuous machine monitoring could be the answer to improving these metrics.
1. Percentage Yield
Manufacturing companies often measure operational efficiency by percentage yield. This strategy involves optimizing equipment and processes to produce the greatest number of quality products possible. Continuous tracking and quality testing are key to optimizing yields, but monitoring machinery also may help achieve this purpose.
Continuous machine monitoring empowers engineers with a real-time stream of information regarding the status and health of equipment. It unlocks (through data analytics) real-time insights into the slightest changes in operating conditions—changes that directly affect the quality of the products. This varies from traditional maintenance programs, which are limited by intermittent information and a lack of real-time insights.
Because parts rarely fail without warning, the goal of continuous monitoring is to identify weak or poorly implemented controls so that they can be corrected or replaced before yield is affected. Consistency in operation and smoothly running machines directly contribute to a higher percentage yield.
2. Capacity Utilization
Capacity utilization is ensuring a facility operates at maximum production capacity by improving equipment availability. If a facility uses a predictive and prescriptive approach via continuous monitoring, it will reduce the amount and cost of downtime. To achieve more uptime and better capacity utilization, plants should plan maintenance around load rather than planning load around maintenance.
Many plants face forced shutdowns, failures and idle assets as a result of equipment repair. One facility, for example, follows a two-production-shift followed by one-maintenance-shift cycle every day. These approaches lead to lost productivity and underutilized assets. Condition monitoring and improved maintenance programs can reverse these problems.
3. Overall Equipment Effectiveness
Operational efficiency is often measured by overall equipment effectiveness (OEE), which breaks a manufacturing facility’s performance into three measurable components: availability, performance and quality.
Continuous data gathering and analytics are key to understanding how assets are functioning in real time. Continuous monitoring offers actionable insights that empower operators to make better decisions. If machinery does not meet its intended level of performance, machine operators must be alerted immediately, not with uninterpretable numbers, but with actionable information (i.e. imbalanced loads, power mismatch, over/under lubrication, etc.). This information is especially imperative given that 5 to 15 percent of machine failures are a direct result of improper maintenance and incorrect usage.
Timely identification of problems combined with actionable data provides an ideal solution for improving operational efficiency.
4. Percentage Planned vs. Emergency Maintenance
All facilities need maintenance. They can save money by scheduling planned maintenance rather than incorporating it in an emergency. Planned downtime is one-sixth the cost of unplanned downtime.
Consider a continuous manufacturing operation (assume opportunity cost of unplanned downtime is $30,000 an hour) that has 100 hours of planned downtime annually and 200 hours of unplanned downtime. Flipping that ratio through the use of continuous machine monitoring and predictive/prescriptive maintenance (200 hours of planned downtime, 100 hours of unplanned downtime) would result in $2.5 million in savings.
5. Downtime in Proportion to Operating Time
Unplanned downtime requires hours spent diagnosing the issue, ordering parts, conducting the repair, getting the system back online and waiting for operating parameters to normalize. By getting ahead of the failure, a facility can eliminate several of those steps. If plants can predict maintenance, they can diagnose the problem and have parts ready ahead of time.
A simple 3 percent improvement in uptime or operational efficiency can result in a $2 million impact on any continuous manufacturing operation’s bottom line (assuming the cost of unplanned downtime is more than $20,000 an hour). This does not factor in the savings from the avoidance of other downtime-related losses—product losses, injuries or equipment damages.
The article “Achieving Effective Lubrication” in Reliable Plant and Lean Manufacturing Journal reports that contamination causes 70 to 85 percent of hydraulic system failures. Preventive maintenance does not take into account usage conditions or failure from improper handling. Intermittent maintenance schedules may not catch these inadequate conditions until it is too late. If a maintenance program is to be successful, a company should experience zero unplanned downtime. Continuous monitoring can make that a reality.
These five metrics, which can be improved by continuous machine monitoring, directly affect operational efficiency. The return on investment can often be achieved in less than two years.
The key is enabling solutions that reduce the cost and complexity of adoption, work well with IT departments, and are an easy retrofit option for existing infrastructure.