Maintenance, Industrial Solutions, Industrial Contractors, Contractor, Renfrow Industrial, South Carolina

What’s Your Maintenance Mindset?

There are many pain points that planners and schedulers in asset-heavy industries face. There are also many practices that work and some that don’t. In this article, the four best practices that schedulers can use to improve coordination and boost productivity in their organization will be highlighted.

1.) Communication is Key

At the top of this list is communication. It’s one of the key aspects of the scheduler’s job: to make sure that the right people are aware of what tasks they need to complete (and when), and that they are equipped with the resources to complete their task successfully. If your supervisors and/or technicians aren’t following the schedule or are making changes to it without the permission of the scheduler, they can impact daily or weekly workflows and put the entire schedule at risk.

Most plants or facilities are constantly facing changing priorities daily, which means the schedule must have the capability to be highly dynamic. If your organization is using a static scheduling method, meaning a schedule that must be updated manually on paper or through a spreadsheet, the information becomes out-of-date as soon as it leaves the scheduler’s hands. This presents a huge problem. Once your technicians lose visibility into the schedule and what their priorities really are, it’s a real challenge to get back on track and achieve good schedule compliance.

Some of the most helpful avenues for communication are daily meetings with your gatekeepers, supervisors, and sometimes key technicians. Gatekeepers will typically have a good idea of what items are of the highest priority and the consequence of not completing certain tasks on time. Maintenance supervisors are especially a vital link between the scheduler and the technicians and operations team. From the field, they can communicate to the scheduler progress updates of the work being completed. Aside from coordinating daily meetings with each key stakeholder on the maintenance team, the scheduler should also make sure they’re communicating frequently with their team throughout the course of the day.

Bottom line: Communication is critical in balancing out workload with available resources. Don’t just check in with your stakeholders one or two times a day; update each other several times per day.

2.) Prioritize Your Jobs

The effectiveness of your schedule hinges upon how you prioritize your work. Even if your technicians have access to a live schedule, poor prioritization of work will present several daily, weekly, or even monthly challenges for your team. This can include constant break ins/break outs, workflow disruptions for corrective maintenance, and wasted work hours. There are several key metrics you can integrate into your planning/scheduling process to ensure maximum productivity.

Let’s take, for example, the number of break-ins and break-in hours at your plant or facility. The number of break-ins is the average amount of times your team must account for emergency, corrective repairs that occur in your plant. Break-in hours account for the time needed to complete the repair. Another example is scheduling capacity. If you schedule your high priority work at 100% capacity, when a break-in inevitably occurs, you will have to make the choice of which high priority task to move. That’s why many organizations schedule at ~85% capacity leaving the remaining ~15% for anticipated break-ins. Having a firm understanding of your plant’s scheduling metrics and its impact on asset prioritization is key to building a successful schedule. No method is perfect, but this provides a certain degree of flexibility when unplanned events happen.

For most organizations, equipment criticality and prioritization of asset repair comes down to cost and safety. Even if you have a break down that is costing the plant a lot of money, it is still important to ensure your technicians know how to handle it safely. When it comes to saving costs on critical tasks and assets, it’s critical that your assets are functioning correctly, and that preventative maintenance is being performed on your most critical assets at pre-determined intervals. This means the scheduler should be in constant communication with the reliability team in order to determine what equipment requires preventive maintenance before critical assets break down in the first place.

3.) Having a Separate Team That is Dedicated to Break-Ins

Another scheduling option for teams is to schedule resources to 100% capacity and still dedicate a “break-in” team of technicians to handling break-in work orders. When there’s a break-in, the scheduler would assign those tasks to the designated team. As you create your schedule and priority lists, set aside some lower priority tasks for this team to complete if there are no break-ins. However, that will rarely be the case in most industries, and there will most likely be enough break-in work orders to keep them working at capacity.

Delegating your break-in tasks to alternate teams allows your other technicians to complete their work without having to change their priorities because of a break-in helping to maintain their focus on the schedule. If there is a high priority PM maintenance task you must complete and a critical break-in occurs, your teams won’t have to choose between which job to postpone. In the end, it comes down to the scheduler and how they want to distribute tasks between these teams.

4.) Get Out of Firefighting Mode

Another area organizations struggle is in how they respond to maintenance repairs. They become reactionary and fall into the trap of “firefighting mode”. They’re constantly dealing with break-ins and break outs, which means having to continually decide what work they can drop from their schedule to take care of these reactive maintenance tasks. Often, they will choose to drop preventive maintenance tasks to get a break-in taken care of. But here’s the catch: that preventive maintenance they should have done turns into a break-in the following week or month. You can see how quickly this can turn into a vicious cycle.

By correctly prioritizing your maintenance tasks and having a dedicated team(s) to handle break-ins, organizations can finally break this cycle and get their processes back on track.


By integrating these best practices into your planning and scheduling processes, your organization will be well on its way to boosting productivity and gaining a competitive edge. Prioritization, visibility, and communication are vitally important to transforming your processes and ensuring that your plant runs at its full potential. However, without a robust digital solution that helps you truly integrate these best practices into your workflow, many organizations will continue to face solvable planning and scheduling challenges.

Article Provided By: Prometheus Group
If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.
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Risk Assessment Matrix Used to Improve Maintenance

Plant maintenance needs to be done in a calculated manner. With the scarce resources that businesses are restricted to using during maintenance, it is essential to ensure that every resource is optimized. Ideally, embracing a risk-based approach to plant maintenance is among the best ways to improve the performance of your plant.

With risk-based maintenance, the frequency and type of maintenance can be picked based on the risk of failure of specific equipment. If an asset has a high risk of failure, it should be monitored and maintained more frequently. For assets with a lower risk of failure, subjecting them to less stringent monitoring and maintenance programs is wise. Risk-based maintenance ensures that you can use the most economical way to minimize the risk of failure across your entire facility.

Here is how to use risk-based maintenance:

Start With Data Collection

Before proceeding with risk-based maintenance, you need to create a list of the risks that might affect the different parts of your plant. This list should include information about the consequences of the risks happening. For instance, if a particular machine fails, you might have to undergo a downtime of three hours.

The list should also include detail on the triggers of such risks, as well as how to identify these risks. Ideally, with a well-outlined list, it becomes easy to predict the chances that a threat will occur and identify common mitigation options.

Risk Evaluation And Ranking

Next, you will need to identify the probability that a risk will occur, as well as the impact that it will have on the performance of your facility. Both the effect and the likelihood need to be quantified to give you a bird’s-eye view of your risk landscape. Furthermore, both figures will be required when it comes to forming the  risk assessment matrix.


You can then consider the risk as acceptable or unacceptable. For unacceptable risks, looking for ways to mitigate them is essential. In the case of maintenance, you also need to formulate an inspection plan to keep an eye on such risks.

Propose Mitigation Measures

There are four ways to deal with maintenance risks. You can avoid them, transfer them, and work on how to reduce them in-house, or completely ignore them. If a maintenance risk is too significant to deal with in-house, but it affects an essential part of the production, the best option is to transfer it to another party. This can be a business or individual that can help with maintenance needs.

For risks that are too huge for you to handle, even after outsourcing them, it might be ideal to avoid them. This includes letting go of anything that can lead to this risk. In case a risk can be handled by your in-house staff, work on ways to do so. Lastly, risks whose impact and/or probability is trivial should be ignored.

Create An Inspection Plan

Once you have an idea of the mitigation path to take, proceed to make an inspection plan. Risks that have a high occurrence chance should be monitored often. For some risks, however, preventative maintenance will be essential to dealing with them.

The inspection plan should also include details on the person  in charge of monitoring the risk . This creates the perfect atmosphere for building a culture of accountability in your workforce. Ideally, those in charge of the risk should key in any inspection they have done in a specific document to create a paper trail for the inspection.

Reassess The Inspection Plan

Risk landscapes are bound to change from time to time. The maintenance risk you are dealing with today might not be the same risk you get to deal with tomorrow. For instance, you might change the machinery you use or use the facility to produce a completely different product. In other cases, external factors such as the change of regulation can warrant a separate risk management approach.

When such changes happen, you need to update your risk management plan for the facility. Commit to constant evaluation of your existing plan, and identify any new loopholes that may exist. You can also use this period to determine whether the previous plans you had chosen were effective enough in optimizing the use of resources in facility maintenance.

The Power Of Record Keeping

Leaving a document trail of your risk management decisions is essential for your entire risk posture. First, the trail ensures continuity in your risk management plans even after the person in charge changes roles in the business, is fired, or gets sick.

Second,  document trails make auditing easy . Instead of the auditor having to collect data on their own, you can present them with evidence of your maintenance plans. Other than reducing the time audits take, it also reduces their cost.

Lastly, document trails help to identify issues quickly. In case a piece of equipment fails, it will be quite easy to know who is at fault, not to mention, identify ways of preventing this from happening in the future.

Ample facility maintenance is necessary to reduce the costs of running a facility and the chances of injuries happening. By taking a risk-based approach to maintenance, your business can have more control over your risk landscape. Consider embracing this approach to improve the overall productivity of your business.

Article Provided By: MaintWorld

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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Why PM is Not a Silver Bullet Solution

Predictive maintenance aims to minimize unplanned downtime by using artificial intelligence (AI) and machine learning to identify impending problems, so they can be addressed before they take effect.

While it’s a very attractive proposition, it’s crucial that manufacturers understand the dangers of getting it wrong. A false alarm triggered by inaccurate data can lead to incorrect actions and additional costs.

How to build a predictive maintenance model

In order to build a predictive maintenance model, manufacturers need clarity on the specific variables they are collecting and how frequently these variable behaviors occur on the factory floor.

It is absolutely key to have accurate domain knowledge about each specific machine and a strong data set of previous failures to draw from. Manufacturers also have to make decisions around lead time, and it’s worth noting that the closer to failure the machine is allowed to go, the more accurate the prediction.

Understanding that some data sets are harder to collect than others will be a huge asset in the decision-making process.

For example, if a machine only breaks once a year and a hundred observations of the break are needed in order to build an effective predictive model, it is clear that observation will not be feasible.

Manufacturers can get around this type of problem by choosing a partner who works with machine makers who have already created a large and relevant data set.

Alternatively, manufacturers can work with a partner who can create a digital model of their machine. Known as a simulation engine, a virtual model of a machine can be analyzed in order to collect the data sets required.

Predictive maintenance: A silver bullet solution?

Understanding the quality of each data set and the depth of expertise it takes to collect and process data explains why predictive maintenance—and the decision making around it—is so complex.

It also explains why predictive maintenance is not a silver bullet solution.

Unexpected problems can still occur, because if a problem has not been anticipated, it’s unlikely that any relevant data sets have been discussed and collected. Manufacturers should keep this in mind, because the most expensive failures are often the unexpected ones.

Predictive operations and predictive quality: A faster route into Industry 4.0?

Manufacturers looking for the fastest route into Industry 4.0, could also explore predictive operations and predictive quality.

A predictive quality system uses quality management information, analytics, machine learning and AI to provide actionable insights. Using these insights, manufacturers can improve quality in real time, minimizing issues such as scrapping, recalls and quality-related machine downtime. They can also look at A/B testing to check different scenarios.

Quality issues typically occur more frequently than machine failures, so data is more easily collected. In fact, it’s fair to say that for many manufacturers, predictive operations and predictive quality are the ideal entry point into smart manufacturing. Due to the availability of quality-related data sets, companies can typically begin benefiting from AI in a much shorter time frame.

In addition to predictive quality, there are other activities manufacturers can use, such as machine learning models that know the best way to make a specific product.

Manufacturers can also look at certain conditions within a machine, such as unusual temperatures or vibrations. As you’d expect, using this data set alone is unlikely to identify the specific machine components that need maintenance, but it provides useful insight and is a good choice for many companies.

To conclude, manufacturers can benefit hugely from predictive maintenance, but must make sure the platform they are working from is right from the outset. Start with some simple data sets and scale up, or explore predictive operations and quality with an experienced partner first.

That way, manufacturers can be assured that they are on the right path to benefit from Industry 4.0, transforming operations, lowering the cost of production and–ultimately–improving profitability.

Article Provided By: Forbes

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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Technologies Solve Industrial Cybersecurity Needs

Emerging condition monitoring software and devices have ingrained cybersecurity that helps bring legacy systems and processes into the modern era.

As the Industrial Internet of Things (IIoT) expands, facilities are looking to include legacy processes or systems into modern infrastructures. However, older assets may be unable to connect to the internet or other industrial technologies, making it difficult to keep them up and running. While the IIoT is triggering this connectivity barrier, it’s also solving it by helping ensuring these old assets have the same security features modern technologies provide.

Many older industrial systems are siloed. This mean they’re not allowed to go outside of defined networks, which are locked down. Users, constrained by limited connectivity, have relied on outside vendors to set up the system with updates, which require expensive support contracts. Systems that are not upgraded end up receiving ad hoc improvements that don’t easily integrate with other software. Unupgraded systems add risk.

However, emerging technologies help take care of integration and security problems by allowing teams to include legacy systems without the need for updates, upgrades, or new networks.

Legacy systems, network connectivity

Network connectivity on shop floors is not a given. Many locations either have sub-optimal internet connections or none at all. Furthermore, information technology (IT) departments are reluctant to let third-party servicers use the internal network. To address this, industrial sensors must be able to connect to more than a local area network (LAN) or Wi-Fi connection.

“A lot of factories don’t want you on their networks,” said John Baker, B3 founder and owner. “So, we take our own Wi-Fi hot spots, plug in our meter, and hook it all up. It takes two minutes to get it all set up, and it’s off and running.”

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Emerging technologies are solving the problem of unconnectable assets, providing data from legacy systems to people on the shop floor. Courtesy: Fluke

Connecting assets to a one program may seem difficult, but it’s not. Third-party IIoT-enhanced software and sensors can help mitigate integration problems by sidestepping the issue outright. Installing a third-party, internet-enabled sensor onto equipment also allows legacy systems to be integrated into modern programs without updating or upgrading existing equipment or networks.

Legacy systems and cybersecurity

By definition, cybersecurity protects against criminal or unauthorized use of electronic data or systems. When legacy systems were installed, administrators were more concerned with internal users accessing sections they shouldn’t have been in, such as hacker Marcus Hess infiltrating a Berkeley server in 1986 and gaining access to ARPANET and 400 military computers.

As time went on, external hacking efforts became more prevalent. The first computer worm, The Morris Worm, spread across networks in 1988, and the Dyn attack of 2016 took down much of the United States’ networks.

Industrial, Service, Industrial Solutions, Industrial Contractors, Maintenance, Mechanical, Renfrow Industrial, Charleston, Spartanburg South Carolina

Wireless devices allow teams to monitor aging assets that do not have innate connectivity. Courtesy: Fluke

The ability to stop attacks from the outside and protect information from unauthorized internal access is paramount. Many maintenance devices teams install on older assets have innate cybersecurity protocols to protect from attacks.

Cloud-stored data is secured against intrusion via active monitoring, security protocols, and layers of firewalls. Sensors and other handheld devices that send information to the cloud are encrypted with the latest transport layer security (TLS) security protocols.

Legacy systems, AWS security

Many industrial products use the Amazon Web Services (AWS) suite of applications. AWS includes security features, such as security bulletins. AWS safeguards data stored on associated servers with firewalls and encrypting information transmitted to and from it over networks. It provides identity and access control features so individual users can only access what is required for their jobs.

Three benefits of using AWS are:

  1. The provider manages the more complex infrastructure needed for services
  2. Offerings are scalable (in or out)
  3. Data center expenses are not incurred by users.

AWS protects against distributed denial of service (DDoS) assaults that overload servers, encryption ransomware that locks data, and virus or malware attacks.

Legacy system integration

Modern industrial asset monitoring products have integrated security features, whether they are based on web services or cloud solutions. Communication between devices or systems and the cloud, where data is stored, uses the latest encryption protocols to secure data.

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Stay connected to your assets by installing internet-enabled devices that stream data to the cloud. Courtesy: Fluke

Integrated security and connectivity are the new standards; the new industrial norm by which all new products should adhere. Any IIoT device should come ingrained with high-level cybersecurity for protection against internal or external intrusion.

While legacy system integration with modern IIoT technologies has been a frequent problem, emerging technologies can help solve this issue. They provide connectivity to industrial data sources and teams along with built-in security features. Newer software and devices allow users to benefit from these tools to help lower cybersecurity risks.

Article Provided By: Plant Engineering

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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10 Tips for a Successful PM Strategy

A carefully considered preventive maintenance plan can make a positive impact on several areas of your business — improved machinery operation, more efficient output, higher production quality, longer equipment life, increased uptime and more.

The most successful preventive maintenance procedures take a multipronged approach to address potential machinery quality and performance issues before they even occur. Below, you’ll find 10 preventive maintenance tips that can help you tailor a plan to avoid unplanned downtime caused by a machine breakdown.

  1. Assess the current state — Before starting any new maintenance plan, it’s always a good idea to conduct a baseline assessment of your facility and equipment. This will provide a big-picture view of your facility that will help guide your actions and decisions. Consider the number, age and types of machines, the OEMs of your machinery, the overall production chain within your facility, the level of automation and any notable historical maintenance issues — either one-time or recurring. Once you’ve taken the big picture into account, think about the strengths and weaknesses of your facility’s current state and decide where you’d like to focus on improving maintenance and performance.
  2. Review manufacturer specs — Once you’ve assessed your facility and machinery, it’s time to start doing research. This is certainly not the most satisfying part of the process (which would be seeing the results of your preventive maintenance plan), but it’s a major step in getting there. Manufacturer documentation and specs can help you identify the recommended best maintenance practices for your machinery, which will be a major piece of the plan you develop.
  3. Documentation is key — Documentation is an important component of any good maintenance plan. In general, you’ll want to document as much as you can, including the current state of your facility, your newly created maintenance plan and the actions you take in pursuit of that plan. As you develop your preventive maintenance plan, comprehensive documentation serves several purposes: it helps you keep track of the work you’ve done so far, creates a single dependable resource for the maintenance practices you enact, and allows employees to easily access and review the plan. All these factors will contribute to positive results.
  4. Maintenance, Industrial Solutions, Industrial Contractors, Service, Industrial, Contractor, Renfrow Industrial, South CarolinaTrain and retrain — Your preventive maintenance plan won’t go anywhere without your employees. It’s essential that they’re able to be active, effective participants in your facility’s preventive maintenance plan. Give them the tools to do so by creating a dedicated training program to communicate established PM practices. And make sure it’s not a one-time deal — retraining should occur whenever new practices are enacted, as well as at regular intervals.
  5. Pursue buy-in — Preventive maintenance sometimes has a bad reputation among facility owners and employees. It can be seen as a practice that lowers productivity, increases unnecessary downtime and creates extra work. Communicate to your employees that preventive maintenance is a key aspect of their jobs and an important factor in the success of the facility because it actually improves overall productivity and reduces the need for more costly reactive maintenance down the line.
  6. Set goals and benchmarks — Working from the big-picture assessment you undertook in the first tip, it can be very useful to set facility goals for uptime, productivity, quality and more. Periodically comparing incremental improvements with your starting point can help motivate you and your employees as you see the results of your efforts.
  7. Take a “before picture” — In pursuit of the above, it can be useful to take a metaphorical snapshot of the initial performance metrics of your facility when you begin your new maintenance plan. Making it easy for you and your employees to review this data will put a strong focus on your results.
  8. Track progress — While your goals and benchmarks may be in the form of one-year, three-year and five-year plans, you should also ensure that you and your employees can see more incremental results. Using the machine data you have on hand, be sure to check in regularly celebrate improvements that are occurring on a monthly or even weekly basis.
  9. Investigate technology options — Automation and performance data tracking tools are more powerful and commonplace than ever. Even if you aren’t interested in sourcing and installing new sensors and other equipment, you can take steps such as using existing CMMS data in a more deeply analytical way.
  10. Review and modify as necessary — Your first (or second or third) preventive maintenance plan shouldn’t be your last one. If something in the plan doesn’t work, don’t hesitate to change it. If you have data to back up your modifications, continuous improvement can never be frowned upon. A good preventive maintenance plan and practice should be able to easily adapt and respond to the needs of your facility.

With these tips in mind, you’re now ready to create — or modify — an effective preventive maintenance plan for your operation.

Article Provided By: ATS

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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Preventive vs Predictive Maintenance

Taking Your Facility to the Next Step

When it comes to maintenance, your organization is either taking a proactive approach or a reactive one.

It’s been proven that proactive strategies, like preventive and predictive maintenance, can improve a facility’s bottom line in many ways: lowering maintenance costs, improving asset reliability and giving a 360-degree view of performance, just to name a few.

However, a majority (66%) of organizations still rely on reactive maintenance practices to keep their operations going.

Here, we’ll discuss each strategy to show the long-term benefits of proactive maintenance approaches.


What is Reactive Maintenance?  Reactive maintenance is when maintenance is only conducted when equipment needs fixed or attention or a Run-to-Failure mode.  This approach can cause more labor, time and money to be spent to keep equipment working correctly.

Up front, run-to-failure seems like the easiest, most cost-effective strategy to implement. It requires little planning and minimal staff, and there’s no need to train technicians on prevention methods or asset tracking software.

Over time, though, the inconsistency of the run-to-failure method will take a toll on your maintenance budget and overall productivity. This approach makes it hard to anticipate the required labor and parts needed for a repair, resulting in unexpected expenses associated with product loss, overtime labor and spare part storage/purchasing.

Preventive Maintenance

What is Preventative Maintenance?  Preventive maintenance involves planned, regularly performed tasks to check, clean and maintain assets to reduce failure and downtime.

With this maintenance method, you want 80% of your maintenance initiatives to be planned and only 20% to be unexpected. Doing so will make your assets more reliable, which can positively impact productivity and profitability.

However, getting to an 80/20 ratio does take a significant amount of effort to schedule, prepare and delegate tasks. Preventive maintenance also doesn’t consider an asset’s wear, which can cause facilities to conduct excessive maintenance when a piece of equipment might need replacing.

Predictive Maintenance

Instead of following a schedule of planned maintenance tasks, predictive maintenance is a strategy where tasks are conducted based on trends within asset data.

This method requires expert observation and specialized tools, such as vibration analysis and thermal imaging, to collect data on the equipment’s efficiency and wear, making the upfront costs of implementation expensive.

However, this data-driven approach to predictive maintenance gives facility managers and technicians full visibility of an asset’s performance. The strategy also uses algorithms to predict when an asset may fail, ensuring downtime only occurs before an unavoidable failure. This can reduce product loss and increase efficiency, directly improving a company’s profitability.

Which Proactive Strategy Should I Choose?

There’s no right or wrong approach to proactive maintenance. Choosing between a preventive or predictive strategy is up to the needs of your facility and your resources for purchasing and maintaining the required technology.

The major difference between the two methods is the amount of downtime involved.

Preventive maintenance relies on planned tasks that are scheduled based on time passed or sensory triggers. Typically, these tasks involve shutting down and disassembling equipment, like for an oil change or applying lubrication.

Predictive maintenance, though, uses algorithms to identify trends in asset data and predict when failure will occur, allowing maintenance teams to prepare spare parts, productivity alternatives and labor required ahead of time. This method typically collects real-time data through sensors and specialized tools, meaning predictive maintenance usually occurs while equipment is operating.

Although the execution tactics are extremely different, both methods can be used together for a well-rounded maintenance strategy that expands your asset’s life-cycle to its fullest potential.


Preventive and predictive maintenance have similar objectives: to make regular maintenance more routine and allow facilities to improve their bottom line.

Whether you mix both methods for a fully developed maintenance routine, or use one as your budget allows, implementing a proactive maintenance strategy can lead to continuous improvement, increased asset uptime and reduced spending on unexpected repairs.

Article Provided By: Maintenance Connection

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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The Evolution of Industrial Maintenance

Several factors—from automation and big data to staffing shortages and culture shifts—will help guide the evolution of industrial maintenance.

These along with artificial intelligence, sensor technology, and budget constraints will become the standard in factories, pushing age-old reactive practices to the wayside. But how and why did equipment maintenance practices evolve from reactive to proactive? And what changes and trends can we expect to see in the future?

The History of Industrial Maintenance

Much has happened in manufacturing since the industrial revolution, but the most dramatic of those changes have occurred in the last fifty years. These changes affected how industry plants have been maintained. Before the Second World War, machinery was generally large, rugged and relatively slow running, with basic control systems and instrumentation. The demands of production were not as severe as they are today, so downtime was not as critical of an issue. When downtime did occur, it was addressed—but generally, these machines were reliable. In some older factories, machines manufactured in that period are still as good today as the day they were made.

After the war, the rebuilding of industry began. A much more competitive marketplace developed, forcing manufacturers to increase production. The overwork of machines lead to an increase in downtime and a rise in costs to fix machines. This increase in production demanded better maintenance practices, which lead to the development of preventive maintenance.

Since the 1980’s plants and systems have become even more complex. The demands of the competitive marketplace and intolerance of downtime have increased, while maintenance costs have risen. Along with the demands for greater reliability, new awareness of failure processes, improved management techniques and new technologies allowed for a broader understanding of machine and component health. The understanding of risk has become essential. Environmental and safety issues are paramount. New concepts like condition monitoring, just in time manufacturing, quality standards, expert systems, and reliability centered maintenance have also emerged on the scene.

Maintenance Programs of Today

In 2019, Advanced Technology Services conducted a survey through a third-party source to collect data about current maintenance practices at over 200 manufacturing facilities. Below are a few of the findings that produce a snapshot of what a typical maintenance program looks like today:

Maintenance strategies: 78% of manufacturing facilities follow a preventive maintenance strategy; 61% have a computerized maintenance management system (CMMS), and 56% use a run-to-failure method.


Scheduled maintenance: 53% of facilities allocate up to 10% of their annual operating costs to maintenance processes; 30% devote more than 10% of this budget on maintenance. The average facility spends 20 hours each week on scheduled maintenance.

Attention to systems: Rotating equipment (motors, power transmission, etc.), fluid power systems (air, hydraulic, etc.), and plant automation systems are the three areas where facilities dedicate the most maintenance support, followed by internal electrical distribution systems and material handling equipment.

Unscheduled downtime: The leading cause of unscheduled downtime within respondents’ facilities remains aging equipment (40%), followed by mechanical failure (24%) and operator error (12%). Four in 10 facilities plan to upgrade their equipment and improve/increase training.

Training: Maintenance teams are mostly trained on basic mechanical (73%) and electrical skills (72%), as well as safety (72%). Other types of training include lubrication (55%) and motors, gearboxes, and bearings (52%).

Technologies: The most common technologies facilities use to monitor/manage maintenance are CMMS (58%), in-house spreadsheets/schedules (45%), and paper records of maintenance rounds (39%).

Outsourcing: The average facility outsources 19% of their industrial maintenance operations, and the leading causes are lack of skills among current staff and too many specialized skills being required.


What the Future Looks Like

Future implementation of maintenance systems will see greater integration of business and technical systems, with more intelligent use of collected data. They will protect users against change of personnel, with the inherent loss of their learning, and allow better informed choices for decision makers. The use of such wide-ranging systems and sensors will allow for vast data collection, which will enviably cause challenges with data management. This will require exceptionally trained people to run, maintain, and manage these systems and data, which may continue to be a problem if there is a lack of technical talent available. The capture of those with this specialized knowledge and the training of new people will continue to be essential for the exploitation of advanced maintenance.

Maintenance has always had the same definition. It is the management, control, and execution which will ensure that design levels of availability and performance of assets are achieved in order to meet the business objectives. The issue that is driving the evolution of maintenance is that the business objectives are variable over time. They have continually changed and will continue well into the future. Only by understanding the underlying issues driving this change will we be better suited to speculate on the future of the maintenance industry.

Article Provided By: ATS

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Service, Industrial Solutions, Industrial Contractors, Contractor, Industrial, Mechanical, Renfrow Industrial, South Carolina, PM, Enterprise Maintenance, Maintenance Strategy

3 Techniques for Optimizing Preventive Maintenance

When Benjamin Franklin wrote, “An ounce of prevention is worth a pound of cure,” he was referring to fire safety. But, as you may know from experience, this saying holds true with regard to preventive maintenance (PM). Simply stated, preventive maintenance is an activity performed at a set interval to maintain an asset, regardless of its current condition. It’s a properly planned activity, where materials and parts are on hand and labor is scheduled ahead of time.The goal of any PM program is not only to extend the life of an asset or maintain it to its existing capabilities, but to also identify potential failures that could cause an unexpected event in the future. Properly planned corrective maintenance is typically several times less expensive than performing unplanned work.But, are the typical frequencies that PMs occur actually correct?

The Society for Maintenance and Reliability Professionals (SMRP) Best Practices Committee recommends approximately 15 percent of total maintenance labor hours be associated with PM work. This, of course, could fluctuate depending on the type of plant, its location, age and complexity, among other things. However, having a line in the sand can provide a starting point to begin the preventive maintenance optimization (PMO) process.

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Though there are many potential starting points, one option is to begin by evaluating the amount of emergency work that occurs on equipment that regularly gets PM performed on it. Start by identifying a critical plant process and its associated assets. Once these assets are identified, choose one piece of equipment that gets a regular PM. Do not pick the most complex PM, as you may be immediately overwhelmed with the amount of information available. Instead, choose something basic. Begin by tallying the amount of PM labor hours and emergency labor hours charged to this specific piece of equipment over a period of time, for example, three to five years to start with. If the site has properly utilized a computerized maintenance management system (CMMS), this task should be relatively easy, as all maintenance work performed should be available. If a CMMS has not been adequately utilized, this task may be more time consuming.

Once this informationhas been gathered, compare the amount of labor hours charged to PM work versus the amount charged for emergency work. If the emergency labor value is greater than that spent on completing PMs, something is probably wrong. It could be that the work within the PM is focused on the wrong item and missing something that’s having a frequent impact to production. Root cause analysis on failures must be performed to completely understand this. Generally speaking, if the numbers are adverse and emergency labor is consistently minimal or nonexistent, the frequency of PMs might be adequate.

Maintenance, Service, Industrial Solutions, Industrial Contractors, Industrial, Renfrow Industrial, Spartanburg, Charleston, South Carolina

There is also the possibility that PMs are being performed at the wrong interval even though hours are aligned as previously explained. John Day, Jr., manager of engineering and maintenance at South Carolina’s Alumax, the first organization in the world to be certified compliant with world-class standards, developed another method to evaluate PM effectiveness. Day’s theory assumes that for every six PMs performed, one corrective work order that cannot be completed within the required PM time frame should be written. If the ratio is greater than 6:1, say 10:1, then it’s likely the PM is being completed too frequently. If the ratio is less, at 2:1 for instance, then the interval between PMs is likely too great, resulting in corrective work orders being written once for every two times the PM is performed. Again, utilizing a CMMS will make gathering and evaluating this information a much easier task.

Considering every corrective work order could eventually lead to unplanned downtime, should the frequency of the PM in the last example be increased? Maybe, but not without some additional research. It is important to understand the work that was identified, the threat it poses and the asset that it’s on. If the corrective work poses a higher risk, the frequency should be increased. However, a conservative approach should be used to begin with. Also consider instead of every year, changing the frequency to every nine months. However, if the work poses no threat to safety or production loss, it is quite possible it’s an acceptable risk that can be addressed as it is identified. The bottom line is that without a consistent evaluation process, the optimal frequency cannot be accurately determined. And, as mentioned earlier, variations in plant operating conditions also can impact the decision.

Maintenance, Service, Industrial Solutions, Industrial Contractors, Industrial, Renfrow Industrial, Spartanburg, Charleston, South Carolina

A third way to begin the PMO process is perhaps the easiest and the most overlooked. It’s that of the human factor – the craftspeople performing the work. As plants, and the workforce itself, are aging, it is highly likely that the PMs being performed were established many years ago and never revalidated. It’s also likely that the same people perform the same PM every time it’s scheduled. An engaged maintenance workforce can be a tremendous help in the PMO process. They’re the ones who can provide input on its quality and contents. Since they routinely perform the work, they can help identify process improvements and validate that the PM includes all relevant work. They can make suggestions on items that should be added to or removed from the PM and could even provide input as to the right frequency.

Each of these three techniques provide a starting point to PM optimization. Additional methods, such as utilizing metrics like mean time between failures and mean time between maintenance, also can provide benefits. Whichever path you choose, remember that PM optimization is a facet of the continuous improvement process. The SMRP’s 15 percent guideline and Day’s 6:1 ratio may not be a perfect fit for every plant or asset, but they’ll provide a starting point for a PMO initiative.

A vision and a long-term commitment from every level of the organization are required for PMO to succeed. Achieving small gains early on can provide momentum and increase buy in to the process over the long haul. And although the process may initially seem costly, PM optimization can reap benefits for years to come.

Article Provided By: Maintenance World

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Predictive Maintenance, Mechanical, Fabrication Electrical, Maintenance, Industrial, Service, Mechanical, Contractor, Renfrow Industrial, Spartanburg, Charleston, South Carolina

Overcoming Predictive Maintenance Obstacles

There are several key barriers to the implementation of predictive maintenance technologies, but they can be alleviated.

Predictive maintenance makes a lot of promises including reduced downtime and eliminating unnecessary maintenance. However it is important to keep engineering and business challenges from getting in the way. Common obstacles and arguments to implementation include:

  • We do not have enough data to create a predictive maintenance system.
  • Many predictive maintenance approaches rely on machine learning algorithms, so there needs to be enough data to create an accurate model.
  • For predictive maintenance, this data usually originates from sensors on machinery.
  • If the sensors are new or the way readings are logged limits the information, you will need to think about the best way to access enough data to build your models.

Take a close look at the list of data sources. Users might find their department does not collect enough data to power a predictive maintenance system. Consider whether other departments collect data, as well. Depending on where the user is in the supply chain, it is also worth looking at agreements with suppliers or customers. Cooperating to prolong the health and efficiency of equipment components may put the company in a win-win situation that fosters data access between business entities.

Some systems operate in a feast or famine mode where data isn’t collected until a fault occurs. Others only log event codes and time stamps: engineers are notified that an event occurred, but not the sensor values at the time of the failure. Although this data may be useful for diagnostics, it is likely insufficient for developing models that can predict failures.

Consider changing the data logging options to record more data, perhaps on a test fleet if production data is not available. Depending on the load on existing embedded devices, it may be possible to reconfigure them to collect and transmit sensor data, or external data loggers may be necessary to get started.

Use simulation tools to synthesize data – Generate test data using simulation tools and combine that data with what sensor data is available to build and validate predictive maintenance algorithms. This is done by creating models that cover the mechanical, electrical, or other physical system to be monitored. Synthesize sample data and validate this against measured data to ensure the model is well-calibrated. This can be done at the component level first, then later at the system level for complex systems.

When considering data for a predictive maintenance system, begin to analyze it early to understand which features are important and which may be redundant. It can be costly to keep data that is not going to be used.

Failure data is a crucial part of teaching algorithms to recognize the warning signs to trigger just-in-time maintenance. Failure data may not exist if maintenance is performed so often that no failures have occurred, or the system is safety critical and cannot be left to fail. To stop this from becoming a fatal deficiency, users can simulate failure data and learn how to recognize warning signs.

An engineer with deep system knowledge of how the physical components work will be able to generate sample failure data with the right tools. Tools such as failure mode effects analysis (FMEA) provide useful starting points for determining which failures to simulate. An engineer with sufficient domain knowledge can incorporate these behaviors into the model in a variety of scenarios, which simulate failures by adjusting temperatures, flow rates, or vibrations or adding a sudden fault. These scenarios can then be simulated, and the resulting failure data is labeled and stored for further analysis.

While failure data might not be present, operations data might show trends about how a machine degrades over time. Statistical techniques such as principal component analysis (PCA) can provide valuable insight into how equipment operates over time, transforming raw sensor data into something which can be visualized and analyzed more easily.

Understanding the cause of a failure is important, but there is a significant difference between identifying what went wrong and knowing how to predict it. Root cause analysis (RCA) is an integral part of domain knowledge that, paired with predictive maintenance algorithms, creates an effective predictive maintenance program. Users can take these steps to reduce the learning curve if the algorithm part of the equation is new and intimidating.

It is important to define goals upfront. Users should then think about how the predictive maintenance algorithm will affect these goals. Building a framework that can test an algorithm and estimate its performance relative to the stated goals will enable faster design iterations.

Start small. If the user already knows the causes behind failures, then the domain knowledge is there. Choose a project using a deeply understood system to practice on. Users should understand the features and factors that affect the performance of the system, and build a predictive maintenance algorithm. As the simplest starting point, consider if thresholding a feature is a significant maintenance indicator (typically done via control charts). Once the team is comfortable with building the algorithms for a simple problem, they can apply that knowledge to more complex systems.

When predictive maintenance algorithms begin to show promising domain knowledge to tune models to predict different outcomes based on the cost/severity of those outcomes. To further validate models, add generated failure data similar to known historical conditions and test the system. This will build confidence that the process is working.

Every new technology requires investment that must be justified. If machine learning has only recently been introduced, it is only natural to see what might be considered an advanced application of it as a risk. However, users can take steps to minimize that risk and get up and running with a working predictive maintenance model as quickly as possible.

Instead of trying to introduce a new technology and technique, take advantage of new capabilities in the software already in place and focus on the new techniques. Some tools already have specific predictive maintenance capabilities, enabling engineers to continue working in an environment they know.

Data can be gathered from multiple sources, such as databases, spreadsheets, or web archives. Make sure the data is in the right format including date and time stamps. Pain points are often around how to organize the data for analysis. If the user doesn’t have enough data, they can generate this from a physical model of the machine to supplement normal usage, varying parameter values, different system dynamics, or signal faults.

If data has come from different sources, it will also need to be combined. If anomalies are removed, think about whether to replace them with approximate values or work with a smaller data set.

Instead of feeding sensor data directly into machine learning models, it is common to extract features from the sensor data. These features capture higher-level information in the sensor data, for example moving averages or frequency content. The use of familiar tools to perform feature extraction techniques simplifies this step. An iterative approach—in which features are added, new models are trained, and their performance is compared—can work well to determine the effectiveness of different features on the results.

To train the model data must be classified as healthy/faulty, set thresholds and estimate remaining useful life for components. Users will need to create a list of failure scenarios to predict, choose classification methods, and simulate models. Apps provide graphical interfaces for applying machine learning that make it easy to get started and compare the results of training many different types of models.

Models may be deployed to embedded devices by converting them to a low-level language such as C, or they may be integrated with other applications in an IT environment. The pain here is often around lack of familiarity with code generation and IT integration. There are tools that can automatically package models to run in a production environment.

Predictive maintenance is an achievable goal with the right tools, guidance, and motivation. Find the features, models, and methods that work for the business and iterate until you get it right.

Article Provided By: plantengineering

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Electrical, Maintenance, Service, Industrial Solutions, Industrial Contractors, Industrial, Renfrow Industrial, South Carolina, Electrical Industry

Changing Times in the Electrical Industry

The industrial sector is changing rapidly: Autonomous robots are filling manufacturing floors, Internet of Things connectivity is increasingly pervasive and artificial intelligence is driving the rise of the smart factory. In 2018, industrial organizations have more motivation than ever to ensure facility functionality and uptime. Across a broad swath of industries, the cost of downtime can be significant—from $30,000 to more than $12 million an hour by some estimates.

This needed reliability hinges on healthy, modern electrical systems. Ensuring aging electrical equipment can keep up with modern demands can be an intimidating process, but it needn’t be.

There are new innovations that can provide a more reliable experience, increase energy efficiency and meet modern infrastructure needs—all while keeping costs and complexity in check.


Connected technology platforms, which gather real-time data on infrastructure performance and share it via the cloud, can act like a heart monitor for your electrical system. Similar to a human body, there are many potential and unforeseen issues that could affect an electrical system’s holistic health and performance. Similar to a heart monitor, a platform can provide a level of reassurance that things are running as intended.

Through smart sensors, cloud-enablement and artificial intelligence, connected platforms for electrical infrastructure can collect and analyze performance data across multiple systems, bringing peace of mind to management by providing early failure notification and actionable insights for intelligent operations.

With a full view of what’s going on with power systems “behind the wall,” managers and engineers can reap numerous benefits, including the following.

  • Operational Performance: Connected technology platforms can significantly improve operational performance by reducing unscheduled downtime, increasing asset life and offering a more consistent experience with an optimized maintenance plan.
  • Financial Efficiency: A connected technology platform offers insights into the parts of the system that are at risk of failing. By receiving a warning from the platform, managers can avoid downtime and make repairs ahead of time. In turn, this reduces failure risk, cost of ownership and maintenance.
  • Safety: Employees will experience reduced personal risk. A connected technology platform will provide alerts to early equipment failure warnings, as well as provide expertise from the data gleaned.


Traditional, older medium voltage switchgear has an average maintenance lifecycle of one to three years, and frequent maintenance of electrical systems means frequent downtime for critical equipment. New metal-enclosed medium voltage switchgear technologies feature sealed-for-life compartments, meaning the internal components will be unaffected by the environment. As a result, maintenance lifecycle can be dramatically reduced to between 10 and 30 years, lowering the total cost of ownership and increasing system reliability. Importantly, this significantly reduces exposure for electrical workers.


Another option to consider—one that is fairly new to the U.S. market—is shielded solid insulated switchgear technology. Shielded Solid Insulated Switchgear (2SIS) uses solid insulation made from silicon, resin or elastomer and is coated by a grounded conductive layer that wraps around the switchgear’s live parts to eliminate the risk of arcing.

Since each bus bar is separated by the insulation, 2SIS inherently prevents them from interacting. The Shielded Solid material in updated medium voltage switchgear solutions reduces the likelihood of the conductors faulting due to a poor environment, eliminating arc flash risk and limiting system outages.


It’s no secret the electrical industry is transforming quickly, and adapting to modern infrastructure needs is becoming a necessity. There are many different options on the market today that can reduce costs, simplify maintenance and increase lifespan.

It’s time for industry professionals to adjust—aging systems were not built to handle this bandwidth. Without changes, repairs and failure will continue. Whether a contractor is considering a connected technology platform or an upgrade in electrical equipment to address maintenance and safety, the time has come to take steps toward the future of the electrical industry.

Article Provided By: Construction Executive

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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