Asset Reliability – infinite uptime

Decoding plant reliability in manufacturing and a process to reach there.

Process manufacturers typically operate in data-rich environments and know their plants inside out. While they know their assets and how their resources are deployed, they are often unaware of factors contributing to optimal asset performance. Even if this information exists within the manufacturing ecosystem, the plant maintenance and operational heads don’t really know how to use it to achieve optimum plant productivity.

Studies reveal that frequent downtimes at process manufacturing plants can result in nearly $15000 per hour of lost revenue. Digitalization of the maintenance process and proactive asset performance management directly contribute to saving this cost. Prioritizing plant reliability becomes the only way to improve overall operations and mitigate unplanned downtimes.

But what is plant reliability, and what processes need to be institutionalized to achieve it? In this article, we will discuss what reliability means for manufacturing and lay out a six-step process to devise a plant reliability strategy for a manufacturing plant effectively.

What is plant reliability, and how can you measure it?

Any reliable system accounts for its safety and trustworthiness while ensuring minimal maintenance costs. For plant assets, reliability can depend on performance, condition, maintenance needs, and availability. An asset reliability check can be done based on factors like frequency of maintenance or repair costs, number of malfunctions, unexpected downtimes, and more.

Keeping plant’s maintenance up to the mark ensures that assets run 24/7 with fewer interruptions or unexpected delays due to frequent maintenance incidents. This leads to faster go-to-market, better output quality, employee productivity, and significantly lower operational costs or costs per unit.

Plant reliability in production can be quantitatively measured using the Overall Equipment Effectiveness (OEE). A popular metric for measuring manufacturing productivity, OEE factors in a product of availability (number of downtimes/uptime), performance (speed or run time of your processes), and quality (number of defects).

An OEE score of 100% indicates a completely reliable, dependable, and high-quality plant with maximum productivity. Therefore, calculating OEE and securing a top score should be part of your asset management’s best practices.

Six steps to creating an effective reliability plan at your plant.

Every successful plan consists of a set of clear and executable steps. And the same principle applies to achieving top-notch plant reliability as well. Without a clear, planned route, it can be hard for you to envision your end goal – optimum plant and asset reliability management. Here are the six actionable steps that are essential to executing your plan successfully:

1. Building the right team.

The right team can make or break reliability goals- from top to bottom.

Effective leadership, skilled personnel, and onsite-plant operations team must be aligned with accomplishing plant reliability goals.

Achieving reliability is a team effort and a continuous improvement process. Designated team champions have to be distributed within Operations, Maintenance, and Engineering along with sufficient alignment around their common goals & individual targets. This way, every individual is well aware of their role in constantly improving the plant and understands their dependencies on the other teams. There has to be also a Reliability Leader who helps drive this initiative

2. Creating the right mindset for reliability

For a successful plan, having the right mindset for asset reliability is as important as relevant skills, processes & technical understanding.

Since achieving reliability requires continuous effort, you can try to define your target numerically & align every department’s target accordingly. This target & its deadline needs to be agreed upon by each department-operations, maintenance, engineering, and the subsequent KPIs that befall them individually.

It is critical that all teams uphold this goal as their guiding principle and implement it through individual responsibilities every day.

3. Adapting Predictive Maintenance (PdM) approach

Plant reliability is also heavily dependent on asset health & reliability. The approach towards asset reliability is centered around the plant maintenance methodology chosen.

An advanced framework like predictive maintenance alongside numerous assets and operations can speed up the process of obtaining plant reliability. By proactively anticipating flaws or anomalies within the plant and addressing them, reliability objectives can be progressively achieved.

And when you proactively work towards fixing them, you can see your maintenance costs and the dreaded plant downtimes plummet instantly. Also, by understanding what caused these failures, your teams can work towards optimizing their maintenance strategy in the future.

4. Having a best practices checklist for assured equipment reliability

It is not enough to be proactive at one time; it has to become a process for excellence in reliability. For this, rigorous observation of what worked needs to be executed..

Best practices for different plant segments and units can be documented for standardized records and accessibility. Mainly for equipment reliability which requires defined steps, having a list of executables becomes highly essential. These practices are initially set for a particular type of machine in a plant -like a gearbox which can be applied for gearboxes across multiple plants for the same organization to benefit from learnings.

This can help your team focus on improving reliability at the plant, bit by bit, and avoid recurring mistakes alongside employing PdM.

5. Prioritizing critical assets first

Your plant might have critical equipment that causes the most impact – both financially and operationally when down, like a kiln in a cement plant.

So to reduce this unpredictable impact, you can prioritize these assets and implement a model like Predictive Maintenance to fix issues on priority beforehand. Predictive Maintenance can also improve equipment reliability as it works toward assuring asset performance and health around-the-clock through continuous evaluation.

6. Assessing your Reliability plan’s progress

Using your goals & best practices checklist, perform regular audits to check for any shortcomings. Organize audits as frequently as monthly or quarterly, based on your process durations. Check if your charted reliability program progress is aligned with your monthly/quarterly goals.

Continuous improvement requires continuous learning too. Make a detailed implementation plan with clear-cut steps for each task for every department involved and skill improvement and tool usage training at periodic intervals. Skills like vibration monitoring.

This practice keeps your plant and teams’ performance in constant check. Also, assessing highlights hidden improvement areas that may be hindering your plant’s reliability.

Conclusion:

A well-articulated plant reliability plan and set targets can be the driving force towards rapidly fulfilling your reliability goals. Since consistency is key to realizing this goal, it requires a combined proactive effort from leadership, stakeholders, and staff.

At Infinite Uptime, we provide cutting-edge solutions to implement predictive maintenance programs, seamlessly improving your plant’s reliability. With plant reliability, we help manufacturers across the globe see faster results by significantly improving plant efficiency, fewer downtimes, and better quality using predictive maintenance.

Want to achieve faster plant reliability? Get in touch with us today to schedule a free demo!

Understanding Asset Optimization in Manufacturing

Mission-critical assets in manufacturing setups can make or break an entire value chain. An unmitigated asset breakdown or productivity decline can halt production for hours, or even days, resulting in huge revenue losses and unsafe work environments. The situation becomes even more complex for industries with distributed assets.

To maintain assets in optimal condition and run the production process without disruptions, dedicated maintenance teams have to be deployed in various locations. Furthermore, investments are required in carrying spare-parts inventory and establishing strategic service contracts with Original Equipment Manufacturers (OEMs). While conventionally, these practices have been considered inevitable, a marked shift is happening towards predictive analytics and responsive maintenance solutions that can optimize asset performance.

This article will cover what exactly asset optimization is, why is it important for manufacturing industries, and how predictive maintenance solutions empower maintenance and operation teams to achieve it.

What is Asset Optimization?

Optimization essentially means making something as effective, functional, reliable, and productive as possible. Asset optimization means optimizing the way that an asset is utilized and deriving maximum value from it. It also entails driving efficiency and reliability objectives by improving the Remaining Useful Life (RUL) of an asset and enhancing the Overall Equipment Effectiveness (OEE).

Asset Optimization depends on leveraging data-driven intelligence and predictive analytics to achieve business objectives and add to the bottom line. IoT-enabled technologies can be deployed to monitor asset conditions and analyze real-time data to determine maintenance needs.

Benefits of Asset Optimization

Optimal asset performance and availability have a dramatic effect on the overall productivity and throughput of a production plant. When assets are operating in optimal conditions, the following benefits are derived in discreet and process manufacturing industries:

In addition to these, safer and accident-free production environments can be created, with reduced risk of catastrophic events that could cost life and property. An indirect, yet palpable effect is also observed on revenue, margins, customer satisfaction, Return On Assets (ROA), and Work-In-Progress (WIP) inventory.

Challenges in Asset Optimization Despite the incredible benefits that asset optimization offers, it is quite challenging to manage asset performance towards optimization. Major roadblocks in asset optimization are:

Maintenance frequency: When manufacturing plants adopt breakdown maintenance (till failure) or scheduled (preventive) maintenance strategies, asset conditions often remain less than optimal. Either maintenance is performed when an asset breaks down or is performed periodically, irrespective of what the asset condition is. In both scenarios, it is impossible to extract the maximum use of an asset.
Lack of data: Real-time information about asset conditions is rarely available, especially if manufacturing plants rely on offline asset inspections. Even when regular equipment inspections are performed manually, gaps remain in the data and many alarming signs about deteriorating equipment conditions may go unnoticed.
Costly unplanned maintenance: For industries with distributed assets, unplanned maintenance in the event of machine breakdown proves to be very costly. A larger maintenance team needs to be maintained to cover the geographic distribution of assets. Spare parts and sub-assemblies need to be sourced at higher prices to fulfill urgent requirements. Not to mention, on-floor conditions are highly unsafe and hazardous for maintenance workers.
Poor flow of information: Offline machine inspections and decentralized maintenance events create silos of information within the manufacturing organization. Critical information about asset conditions is not shared in real-time with all concerned stakeholders, and maintenance teams operate independently as per their capabilities.
Ineffective utilization of resources: Both human and physical resources are utilized with limited visibility of the machine health and asset availability. Thus, maintenance activities are organized even when they are not needed and machine parts are replaced before their useful life is over.

Asset Optimization through Predictive Maintenance Predictive Maintenance solutions can help plant maintenance teams overcome the various challenges in asset performance management and ensure asset optimization across the plant. With a predictive approach, maintenance teams monitor asset conditions remotely with the help of cloud-enabled technologies. Vibration analysis, acoustics, thermography, oil analysis, and other remote condition monitoring techniques are deployed to track asset conditions while they operate as per schedule.

The machine health data is centrally collected and analyzed with the help of Industrial IoT (IIoT) technologies and accessible through responsive dashboards to concerned stakeholders. Since maintenance has to be strategized based on predictive insights, edge diagnostics, and advanced analytics are used to determine which asset is performing non-optimally and in need of attention. Such a focused approach to asset performance management has several benefits:

Conclusion In sum, asset optimization ensures that all available assets are utilized optimally in a manufacturing environment. By tracking asset conditions in real-time and performing predictive analytics, maintenance activities can be scheduled to optimize asset performance. Improved flow of information within the manufacturing organization and data-backed planning of asset maintenance can improve net return on assets (ROA) and overall plant productivity.

Infinite Uptime offers responsively designed predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in optimizing asset performance, explore the plant reliability solutions of Infinite Uptime.