An ounce of prevention is worth a pound of cure is an age-old axiom, but it applies to modern manufacturing operations. Manufacturing plants operate at varying levels of efficiency. Aging and sometimes even new equipment can experience hiccups and fail, causing significant problems for manufacturers. In most cases, such issues could be prevented, but to do so, manufacturers must take the time and effort to create a strong maintenance management program.
Problems inevitably arise from time to time in any manufacturing operation, but creating a reliability-focused maintenance program can minimize their impact. A survey by PricewaterhouseCoopers found that 95% of companies that adopted predictive maintenance programs improved at least one key maintenance value driver, and 60% saw clear improvements in equipment uptime. In this article, we’ll explore steps for implementing a predictive maintenance plan that keeps your manufacturing floor humming along.
Manufacturers have a wide variety of equipment, which are often bought at different times, run by various teams, and serviced in different ways. To safeguard their return on investments, they strive to make this equipment last as long as possible. To do so effectively, they must first create a comprehensive inventory of all equipment assets, where each item is in its lifecycle, and the role each plays in operations. This information becomes the foundation for a total productive maintenance program.
Businesses sometimes rely on aging systems that are no longer supported by their manufacturers, making parts difficult to secure. As a result, it’s critical to have a spare parts management process in place to ensure sufficient replacements are available when needed. After compiling equipment data, focus on ensuring you have the materials needed to service older machines throughout their expected lifetimes.
When creating maintenance schedules, it’s important to minimize the impact of any downtime that will be required. Whenever possible, complete routine maintenance tasks during slow times, so they have the least impact on the business.
Human error is a major cause of downtime, particularly in manufacturing. Therefore, companies need to make sure that employees not only know how to use their equipment but also how to service it properly and what to do when problems arise. The right training can empower employees to address issues promptly and effectively.
Nowadays, manufacturers have more data at their disposal than ever before. Internet-of-things technology enables them to monitor and report on a wide and growing array of conditions that impact system performance. To make sense of all this data, it’s important to identify key performance indicators, such as unplanned and planned downtime, production rates, defects, and errors, that provide insight into overall equipment effectiveness. Visibility into how the performance of individual pieces of equipment impacts the entire manufacturing process allows manufactures to prioritize maintenance tasks that will be most impactful.
Equipment maintenance is dynamic and changes over time. Companies want to be proactive and understand what items are at risk of failure. Smart sensors, coupled with machine learning algorithms, help manufacturers detect anomalies in industrial machines. For instance, vibration analysis on a gear shaft can identifies misalignments, bent shafts, or other motor problems.
Preventive maintenance involves scheduling tasks at predetermined time intervals or equipment trigger points. Predictive maintenance, on the other hand is a more sophisticated, data-informed asset management strategy. Artificial intelligence, machine learning, and data analytics are used to gauge an asset’s efficiency and provide maintenance recommendations based on items like vibration analysis, oil analysis, and thermal imaging. Rather than following a set schedule, this process allows manufacturers to complete maintenance when it is needed. The approach can extend machinery lifecycles, reduce downtime, and cut maintenance costs.
Unplanned equipment breakdowns are costly, disruptive, and often a key contributor to unplanned downtime. Predictive maintenance gives manufacturers unprecedented power to prevent unplanned downtime. By strategically planning equipment maintenance based on real-time data, companies can minimize the risk of unexpected failures. Find more tips for enhancing efficiency and quality in your manufacturing operation on the SYSCON PlantStar blog.