If you’re in the facility management industry, you’ve noticed that things are changing quite quickly. New technology, economic pressures, and supply chain disruptions have all radically altered facility management trends and previously assumed assumptions. During this tumultuous time, it’s essential that you stay on top of emerging technology and emerging trends—both to remain competitive and avoid the potential for disruption. Today, we’ll look at the following critical facility management trends: Outsourcing to capitalize on a global labor force while making turnover more manageable. Relying more on sensor-based, Internet of Things (IoT) technologies for maintenance and management. Creating complete, connected smart...

Investing in a new facility management software system is a huge decision, and your organization could experience some level of disruption regarding the new technology—you need to know you’re making the right choice. Some computerized maintenance management system (CMMS) solutions run on preventive maintenance, and others use both preventive and predictive maintenance. Some are primarily mobile and SaaS-based, and others are on-premise tools. eMaint, UpKeep, Hippo, FMX, MaintainX—and, of course, LLumin’s CMMS+ are all leading CMMS solutions built for mobility and efficiency. Today we’ll look at: eMaint. A leading provider of preventive/predictive maintenance software solutions. UpKeep - An easy-to-use, newer...

As your facility grows, the marketplace changes, or regulations and standards evolve, gaps in efficiency become more apparent. However, by following facility management best practices, you improve your facility's efficiency, strengthen its operational processes, and prepare for scale.  Today, we're going to look at a few essential facility management best practices—and the tools and processes needed to support them. We will cover: Implementing an automated maintenance work order management system.Creating a customized preventative maintenance schedule.Improving communication with policies and procedures.Establishing the right metrics and benchmarks. Then we will discuss how LLumin’s Computerized Maintenance Management System (CMMS+) can help your facility...

Predictive maintenance analytics is a field of data analytics that assists organizations with their asset maintenance. Through predictive maintenance, organizations can determine when their assets are most likely to fail, optimizing their maintenance tasks and improving their operational efficiency. Predictive maintenance analytics is used throughout industries such as utilities and power, food and beverage, oil and gas, and industrial manufacturing. What Is Predictive Maintenance Analytics? By analyzing data collected from machine-level sensors, predictive maintenance analytics identifies patterns that indicate when a machine is likely to experience a failure. This information can then be used to schedule repairs or replacements before...

You rely on your assets, and you need to know if they are at risk of failure or degradation. Even the most well-maintained machines eventually break down. Unpredictable failure leads to expensive repairs, lost productivity, and even safety-related hazards. Before condition-based monitoring and predictive maintenance were feasible, companies estimated failure rates based on historical data. They knew, for instance, that a given asset might perform for 20,000 hours, so they planned for downtime between 15,000 and 25,000 hours. They had to manage their assets reactively and prepare for disruption. Today, we have better technology like sensors, 5G, and predictive maintenance...

Predictive maintenance technology detects potential equipment failures before they occur, avoiding or minimizing downtime. Machine learning is a powerful tool that analyzes data, learns, and creates increasingly accurate predictions. Together, predictive maintenance and machine learning will improve the reliability of an organization's maintenance and asset management strategy.  Below, we'll discuss the basics of predictive maintenance and machine learning and how each technology enhances the other. Predictive Maintenance and the Intelligent Facility According to Plant Engineering, 40% of today's facilities employ predictive maintenance to reduce downtime, improve ROI, and increase the overall longevity and efficiency of assets. However, over 50% of...

Facility success or failure often depends on the ability to minimize costs while maintaining asset performance, and to repair assets quickly when necessary. However, accomplishing this is easier said than done—especially for facilities that attempt to maintain a manual maintenance program. When an asset requires repairs or maintenance, the appropriate personnel must be alerted, replacement components must be ordered (if not already available), and any maintenance work should incur minimal facility or asset downtime. Determining when an asset requires replacement and preparing for that eventuality presents an additional challenge. Asset service life varies considerably based on the application, frequency of...

How frequently does your equipment fail? The mean time between failure (MTBF) formula isn’t complicated—all the complexities are introduced upon implementation. However, you will need to collect the right data and analyze it correctly to yield actionable insights. Today, we will explore: The mean time between failure formulaHow to use the formula effectively (with examples)How to reduce your mean time between failure. This bullet ends with a period, and the others don’t If you need to know more, reference Mean Time Between Failure (MTBF) and MTTR: a Complete Guide. So, let's dive in and look at the mean time between...

According to Deloitte, predictive maintenance lowers maintenance costs by 25%. It further reduces breakdowns by 70% and increases productivity by 25%. So, it's not a surprise that predictive maintenance technologies are revolutionizing the maintenance management industry. Predictive maintenance is more than just a technology—it's an entirely new approach to maintenance management. By using machine-level data and predictive analytics, organizations identify potential problems before they occur. Predictive maintenance empowers organizations to engage in proactive maintenance strategies and preventative measures. There are two primary components to predictive maintenance technologies Hardware - Sensors used to collect machine-level data. Software - Solutions used to...

A major goal of industrial facility managers and decision makers is to maximize machine uptime (the average amount of continual running time of machines) and minimize downtime (the average amount of continual time machines are not operating). Production and profits are directly tied to machine uptime. When machines are not operating (downtime), production bottlenecks often occur, which affects production efficiency throughout the facility. Further, production issues slow order fulfillment—which may result in the loss of long-term customers and customer referrals. It is clear that maximizing uptime while limiting downtime is extremely important. However, fully implementing a comprehensive strategy on how...