With the right implementation, continuous machine monitoring can decrease downtime and reduce costs
Few deny that the demands from the manufacturing and energy sectors will continue to drive industry growth. In this era of demanding conditions, operational efficiency in manufacturing facilities 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.
Maintenance and Operational Efficiency
With increased competition saturating topline revenue growth, businesses are increasingly focusing their energies on improving operational efficiencies and managing cost centers to impact their bottom lines. In an effort to achieve the coveted near-zero downtime, companies begin by evaluating their maintenance programs. Preventative maintenance had its place 20 years ago, but new technological advances allow for more refined and reliable methods of tracking the machinery health and avoiding potential failures.
Predictive maintenance—as the name suggests—leverages data collection from machinery to enable users to predict when equipment failure might occur so they can implement maintenance before equipment breakdowns occur. 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 information allows engineers to prepare for specific failure modes and with the necessary replacement parts. This knowledge also enables facilities to better learn and understand their equipment and to reduce unplanned downtime. These analytics center around asset efficiency, which helps companies get ahead of failures and increase uptime.
Asset efficiency is critical, but it is only one part of a larger solution for managing cost centers. 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.
- Greatest Number of Products Possible
Manufacturing companies often measure operational efficiency by percentage yield. This strategy includes optimizing equipment and product 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
their 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 produced. This varies from traditional maintenance programs, which are limited by intermittent information and their 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.
- Maximum Production Capacity in Facilities
Percentage yield is significant, but it is only the first step toward improving operational efficiency. Consider capacity utilization—ensuring a facility operates at maximum production capacity by improving equipment availability. If a facility moves to a predictive and prescriptive approach (via continuous monitoring), it will reduce the time and cost of maintenance-related downtime. To achieve more uptime and better capacity utilization, plants should plan maintenance around load rather than planning load around maintenance.
Many industrial plants face forced shutdowns, failures and idle assets as a result of equipment repair. One facility in particular 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.
- Overall Equipment Effectiveness: Availability, Performance, and Quality
Operational efficiency is frequently measured by overall equipment effectiveness (OEE), which breaks the performance of a manufacturing facility into three measurable components: availability, performance and quality.
Continuous data gathering and analytics are key to fully understanding how assets are functioning in real-time. Continuous monitoring offers actionable insights that empower operators to make better decisions. If machinery doesn’t meet its intended level of performance, machine operators must be alerted immediately—not just with a bunch of 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 and operation.
Timely identification of problems or weaknesses combined with actionable data provides an ideal solution for improving operational efficiency.
- Percentage Planned vs. Emergency Maintenance
All facilities need maintenance. The ideal situation is to save money by scheduling planned maintenance rather than incorporating it in an emergency situation. 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 directly as a result of improved maintenance.
- Downtime in Proportion to Operating Time
Improving operational efficiency saves the company lost opportunity costs and reduces overall cost of operation. With unplanned downtime, hours are 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. This approach enables improvements in operational efficiency by avoiding failure and removing preventable steps.
A simple 3 percent improvement in uptime or operational efficiency can result in an impact greater than $2 million on any continuous manufacturing operation’s bottom line (assuming the cost of unplanned downtime is greater 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.
According to the article “Achieving Effective Lubrication” in Reliable Plant and Lean Manufacturing Journal, contamination causes 70 to 85 percent of hydraulic system failures. Preventative 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 directly impact operational efficiency in any continuous industrial operation. Continuous machine monitoring technologies can positively impact these metrics by reducing cost centers, increasing productivity and impacting the company’s bottom line. The return on investment is clear and can often be achieved in less than two years.
The key for solution providers 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.