Insurance is an interesting business. Just about everyone has one or more insurance products and most of us hope to never use them, or at best, use them sparingly. We are either mandated to have them or subscribe to them to hedge against risk, a structure fire, an accident, a medical issue. All pose significant financial burdens when left unprotected. Many types of insurance create incentives to help you lower your regular contributions; alarm systems, discounts for safe driving, or lower premiums for not smoking. While these are certainly beneficial to us as consumers, they’re offered to lower risk for the insurer, decreasing the likelihood of paying claims. In many cases we bear the cost of these programs that help them, and us, lower risk.

It’s curious then that so many in the industrial space expose themselves continue to incur the risks of not putting these systems in place that can detect or prevent failures. Even more troubling is that a significant failure is a collection stretching across numerous insurance types when in the context of personal products. Poor equipment health leads to lower production standards and higher costs. Failures may take equipment out of service for an extended period like a fire or accident would with our home or vehicle. Why do we take such risks in the workplace that we would be far less likely to take at home?

When insurance is being underwritten several factors are considered. Higher risk either incurs a higher premium or is not offered coverage because they know the probability of an incident requiring a claim or payment is higher than those with lower risk factors. Applying proper data models and maintenance procedures is the same concept. If you know where the risks exist and you take mitigating action to identify and lower their ability to occur, you increase the likelihood of preventing problems. Having a comprehensive set of knowledge that shows how defects or failures present themselves, identifying the appropriate technology to monitor for that pattern, and applying it across various equipment will help improve your underlying OEE metrics.

The path is there for you to take. It does require work and does not fully remove risk. However, the knowledge to guide you through the journey and lessen the possibility of significant failures or wasted resources on ineffective methods is at your fingertips.

A key misconception in industrial transformation and three ways to help avoid the trap 

For more than a decade the industrial landscape has been inundated with grandiose promises to see massive improvement in operations via Digital Transformation (Industry 4.0, Smart Manufacturing, or any other litany of buzzwords). Connect your devices, collect your data, pump it to the cloud, hand the data to the data scientists and through the power of Machine Learning (ML) and Artificial Intelligence (AI), we get transformation. It’s an alluring story, but it’s misguided and many that are piloting these solutions are not seeing the value in moving to larger implementations. Vendors call it “pilot purgatory” but analysts such as LNS Research have extensively surveyed manufacturing organizations do not indicate they’re stuck in purgatory. They suggest that while the technology is interesting, it’s not sufficient to deliver the improvements they were promised. 

There are three keys that differentiate those who succeed and those who stagnate. Clear vision and purpose from the top that cascades down into an executable plan, inspiring a culture of quality and change, and using a validated set of data to build improved operational processes. 

While we will go into much deeper analysis on each topic, for now, let’s look at these three pillars of transformation success quickly and discuss how you can bring them to the forefront in your organization.  

C-suites and boards are justifiable enamored with transformation. Executed properly, these endeavors move all the executive levers in the proper direction. The challenge is vision translating into execution. Ultimately, the vision needs to be turned into an actionable plan that engage multiple stakeholders and layers in an organization, specifically including those on the front line of operations, quality, maintenance and reliability, and supply chain, to name a few. Having a top-down and bottom-up view is essential to success. 

Nobody truly likes to change, but it’s happening all around us. Recognizing that it’s going to happen with or without you, having input into the process creates the opportunity to feel better about the outcome. Engaging with management and front-line colleagues, gathering feedback, and engaging that feedback to help eliminate outdated methods and tasks helps create a more engaged environment. Removing the stigma that change is going to be pushed on you and will lead to pain requires a full commitment from the team impacted. Starting small to gain trust can go a long way to building a sustainable program and momentum. 

A third key element to success is having the right data. We hear a lot about ML and AI being able to infer, and frankly, picturing some data scientists recalls images of a hoarder with magazines, newspapers, and books everywhere. Try finding what you’re looking for in that clutter. All that data also leads to a lot of potential bad findings, particularly because many data feeds are incomplete or not properly understood. Leveraging content that is validated and tested can help shorten time to value because you know what you’re referencing is accurate. No more trying to find a needle in a haystack, but instead taking information that has been researched and collected by subject matter experts over years of practical use. 

While having all three pillars is ideal, it will likely take effort to achieve. Ensuring the proper data is present can be a potentially easy win allowing you to use the proper analytics and decision trees to help drive improvement. This can lead to getting buy in for change and for mid and senior managers to throw more plans for execution against the C-suite’s vision. Follow us on LinkedIn to learn more about actionable ways to transform successfully. 

Workloads are growing, talent is retiring, replacements are often not as skilled as those they replace. Expectations from senior management are growing, yet the resources required to maintain the status quo, let alone improve, are harder to obtain. Prices across the landscape continue to rise. We are asked to do even more with less. Many leadership teams urge Digital Transformation, yet not enough are leading from the front to identify ways to truly improve and transform operations.

Attempting to throw technology at a problem rarely solves the problem, at best it simply shifts the focus to the next weakest link in the chain. Eli Goldratt introduced us to his “Theory of Constraints” in the bestselling manufacturing novel, “The Goal” decades ago. While some have fully embraced the vision, many more continue to struggle, moving from one fire to the next. When times are good, although not ideal, businesses can survive. In our current state of “Fear, Uncertainty, and Doubt” (FUD) it seems like the burning fire will consume us.

There is a path forward. It will impact your organization from front line operations and maintenance all the way to the C-suite. While it is simple to start, it does require discipline and a commitment to focus beyond the quarterly results. Knowing precisely what is wrong with equipment creates a level of empowerment that opens these possibilities. In a recent article, we discussed five benefits of data-centered reliability. Now let’s focus on the impact at different levels of the organization.

Maintenance and operations receive a critical mutual benefit, an accurate picture of equipment performance, issues that require resolution, and a clearer picture of when equipment can be taken offline for maintenance to maintain optimum performance. Given a past where this has been difficult to envision, let alone execute, will help make decisions more transparent for all personnel in the plant. An additional benefit for the plant manager and operations staff is they’ll be able to better plan and execute their production schedules. Greater visibility into uptime and performance also enables a reduced need for last minute overtime or bringing in additional staff or contractors to catch up after either unplanned downtime or a planned outage that took too long because maintenance didn’t have a clear picture of the issues they would face when repairing the equipment.

Sales and marketing have the confidence to position products with greater confidence that plants will be able to deliver on time and in the right quantities more often. Also, what sales and marketing team wouldn’t like to have more to sell in certain markets? The newfound availability means they can seek out additional new customers or offer greater quantities of products to existing customers because their maintenance and operations colleagues are keeping the lines rolling with high quality goods. Being able to keep your word on timing, price, and quantity are all great benefits for sales and marketing teams to foster trust in their client relationships.

Human Resources, Human Capital, or People organizations are always looking for opportunities to reduce turnover, retain knowledge, and create a competitive advantage to recruit the best talent available. In certain manufacturing verticals turnover at the plant level can be very high. We’ve worked with some companies with upwards of 40% year-over-year turnover on the floor, which means constant recruiting and training costs. It also means fostering institutional knowledge is more challenging. Having plants that are functional and not fire drills helps promote retention. Being able to let incoming personnel know that you have an efficient plant which means they can work their shift and go home with less chance of an incident can be a hug recruiting tool.

Sustainability is top of mind for many companies. The ability to produce goods faster and better helps drive those goals. We don’t have to give up the third side of the triangle here, because producing better and faster ultimately is cheaper. For sustainability professionals, having equipment that runs properly means it will consume less electricity, which means it has the potential to lower your carbon footprint. Also, with increased quality you’ll almost assuredly see decreases in scrap, re-work, or other areas that impact run times, raw materials, and inefficient shipping due to product delays. These impacts to Scope 2 and 3 emissions will help any sustainability leader move the needle toward stated goals.

While each of the areas described has a positive financial impact for the organization in their own regard, within the C-suite the impacts create significant hard and soft savings. Manufacturing plants contain millions in assets and inventory, yet they’re often not treated as well as they should be given the direct impact they present with revenue and profit. When you factor in capital expansion projects that are often undertaken because existing capacity is maxed out, when in many instances there’s a significant hidden plant, these benefits run deep. With publicly traded companies, given the current climate of many institutional investors, showing a commitment to ESG, net-zero, etc., there’s demonstrated ways to show a commitment to a purpose driven organization for your colleagues, and improved financial returns for shareholders.

The “Power of Knowing” your assets and the roadmap to safeguard them to improve across maintenance, operations, HR, sustainability, sales, marketing, and finance is available and attainable. Hopefully, seeing the impact will drive a desire to embrace proven methods that will improve a variety of aspects in your organization. Treating it as a journey allows you the opportunity to consistently improve, delivering added value daily to your internal and external stakeholders.

 Good decisions are based on good data. The most intelligent people and the most sophisticated computers systems in the world get it wrong from time to time. People like to use the term "Facts" and "Supporting Data". But what if your truth isn't based on reality? We all want to make good decisions and seek out relevant data in order to obtain "Truth" and derive a sincere opinion. However, if our data isn't proper, or granular enough to make logical decisions, we find ourselves being sincerely wrong, and supported by what we believe to be data.

The question becomes, how do we obtain the proper information and enough of it, in order to make good decisions? Ideally, we would search out an expert in the field and question them. Learn from others experiences in similar situations. Look at historical data that has known outcomes and study it. 

Predictive maintenance has been touted as a “killer use case” for IIoT, Digital Transformation, Industry 4.0, or any other buzzword or marketing jargon that’s been used in the last decade or more. For folks that have been around the space for longer than we care to admit, glad to be invited to the party. Opting for a data-centric approach you unlock a whole new level of identification and accuracy. Here are five that create a strong business justification for most organizations.

Many pieces of equipment that are not properly maintained consume additional electricity

Something as simple as imbalance in an electric motor can lead to 20-30% increases in electrical consumption. In an assessment of about 500 motors that could result in excess spend of $1-2M per year. The same can be found for conditions like belt slip, again, resulting in far greater consumption just to meet base demand. Also, with the growing nature of electrical components in facilities, load can increase causing harmonics in your systems, again leading to an unclean electrical signal and additional consumption and spend.

Quality of production is directly tied to asset health

Even in instances where equipment is still functioning, if it’s not operating at proper levels, you’re likely to see decreases in your quality numbers. That could mean longer times to process a batch, rework, or excess scrap or consumption of materials. Small improvements in reliability can yield significant results. Just think, for every 1,000 units you need, a 10% decrease in productivity means you need 1,110 units produced to meet your goal. In long run batch and continuous environments that adds up to significant costs on an annual basis.

Increased capital to meet capacity

While not explicitly tied to quality, one expansion method is adding new equipment, lines, or even plants. If you’re operating to a high OEE or TEEP, you may need to invest, as there’s not a ton you can squeeze from the equipment. However, looking at many facilities, that’s simply not the case. With data centric reliability we can improve availability, performance, and quality. Using data and monitoring, optimizing the line speed to your desired quality and uptime becomes more predictable, because you can see the parameters in real time, catch anomalies before they impact function, leading to potential for significantly better overall productivity.

Failing to meet production goals and schedules

Unless you’re already running 24 x 7 a way to fulfill orders and production is to run additional shifts, whether with additional staff or overtime. As discussed above, if you’re not working to improve or maximize your OEE, you’re likely to spend more money meeting those targets. If you are running 24 x 7 and falling behind, you’re once again thinking about capital expansion to close the gap. Being able to see and manage your equipment and processes with better data will help weed out these inefficiencies and improve productivity to lower COGS.

Overspending on ineffective maintenance strategies

There’s no arguing that preventive maintenance (PM) is more cost effective than reactive or corrective maintenance. However, many PM strategies continue to rely on time based work orders, often dictated by manufacturer’s recommendations. Those PMs are not designed to optimize spend and performance, they’re meant for preventing failure during the warranty and selling spare parts. Great for them, not the best strategy for you. Moving to data-centric and condition-based work allows you to truly optimize spend and performance. This easily leads to removing 1 of every 3 PM work orders and in many cases over 2 out of every 3. The impact on your work order backlog and spares budget is tremendous.

Leveraging data has a massive impact on the operation of your facility. Actionable knowledge of your equipment can be established quickly and cost effectively. All of this is demonstrable, allowing you to truly experience the impact before committing additional time to expand and capture more of the benefits.