Every manufacturer wants to improve Overall Equipment Effectiveness (OEE) and associated metrics, but achieving world-class OEE is impossible without real visibility into machine health. In this article, we outline how a predictive approach to maintenance can significantly improve a manufacturing operation’s OEE by maximizing machine availability and performance, as well as output quality.

This article was originally published on October 27, 2020 and updated on November 18th, 2024.

As emerging technologies transform manufacturing, traditional approaches to machine maintenance are less effective. Maintenance and reliability teams that utilize a reactive approach to addressing equipment issues and breakdowns lack real insights into Machine Health. As a result, they can’t make meaningful improvements to OEE.

In the increasingly competitive world of manufacturing, this is a major problem.

What Is OEE And How Is It Calculated?

As the premier metric for measuring a manufacturing operation’s productivity, OEE considers equipment availability, performance, and quality of output to assign a numerical value to the overall effectiveness of a machine or production line. Essentially, that numerical value represents a machine’s actual productivity in relation to its full potential over the course of planned operations. It’s expressed as a percentage.

What Is A Good OEE Score?

A low OEE value means that a production line or individual piece of equipment is severely underperforming, whereas an OEE of, say, 100% indicates that an operation is producing only the highest-quality parts, as fast as possible, for the entire duration of planned production time.

Every manufacturer wants to improve OEE and its associated metrics, but achieving world-class OEE is virtually impossible without real visibility into machine health.

Time For A Change

The typical manufacturer allocates approximately 40% of total operating expenditure to maintenance — a substantial amount for any company. Yet when maintenance and reliability teams operate within a traditional reactive or preventive framework for identifying and prioritizing equipment repair, that investment is often insufficient.

Factory walk-throughs, random equipment checks, and annual shutdowns don’t just consume valuable time; they’re also prone to human error and leave opportunities for machine malfunctions to cause unplanned downtime. Furthermore, the data generated by these methods generally isn’t useful for manufacturers trying to forecast future risk and production output.

As a result, manufacturers employing conventional maintenance practices experience more unplanned downtime, lower-quality output, and more maintenance-related costs throughout the year. Eventually, these negative outcomes add up, putting a manufacturing operation at a severe disadvantage in relation to more forward-thinking competitors.

So what’s the solution?

“Not only does condition-based maintenance allow manufacturers to reduce machine downtime and avoid temporary unplanned outages (as well as potentially catastrophic equipment failure), but it also allows them to accurately forecast future risk of failure based on a machine’s current condition.”

How to Improve OEE

Given the increasingly complex nature of modern factory equipment, manufacturers can’t afford to not continuously monitor Machine Health. Real-time insight into equipment performance generated by sensors mounted onto or embedded within individual machines allows teams to perform condition-based and even predictive maintenance. The advantages of this approach over outdated maintenance and monitoring techniques are clear and substantial.

By using vibration analysis and similar data-collection methods in conjunction with advanced analytics, factory operators can anticipate a machine failure before it happens. When a bearing starts to show signs of deterioration, for example, teams utilizing a condition-based maintenance approach can replace the part before it negatively impacts a machine’s effectiveness.

Not only does condition-based maintenance allow manufacturers to reduce machine downtime and avoid temporary unplanned outages (as well as potentially catastrophic equipment failure), but it also allows them to accurately forecast future risk of failure based on a machine’s current condition. By extension, they’re able to more confidently set production targets and plan future maintenance operations around production goals.

To better understand how a predictive approach to maintenance can lead to significant improvements in a manufacturing operation’s OEE, let’s take a deeper dive into the metric’s three primary components.

“In high-level sports, it’s often said that the best ability an athlete can possess is availability. The same is true of machines in the world of manufacturing.”

The Three Components Of OEE

1. Machine Availability

In high-level sports, it’s often said that the best ability an athlete can possess is availability. The same is true of machines in the world of manufacturing. A piece of equipment that’s not functioning at capacity (or at all) when you need it to be operating at 100% is a financial drain on your business. Unscheduled downtime as a result of machine failure is one of the largest obstacles manufacturers face when it comes to improving OEE. A reactive approach to maintenance virtually guarantees that unexpected equipment failure will be a persistent problem.

Manufacturers that rely on a preventive maintenance approach, with scheduled downtime to address equipment issues that may or may not impact OEE, face similar inefficiencies. During a plant shutdown or other scheduled maintenance periods, mechanics and technicians are typically presented with a task checklist and a target completion rate. In order to meet this target within the time allotted for maintenance, they’ll often mark unfinished repairs as complete. This is why a facility with a preventive maintenance checklist that is close to 100% complete can find itself operating at just 50% efficiency.

Moreover, even the best technicians are prone to human error. During scheduled downtime, these experts will often be tasked with inspecting equipment that doesn’t need repair, resulting in further time and cost inefficiencies.

2. Machine Performance

In order to meet the demands of modern consumers, manufacturers must produce as much as possible, as fast as possible. The problem is that an outsize focus on production speed can actually lead to more instances of equipment failure, ultimately hindering productivity rather than improving it. Despite this reality, manufacturers must maintain certain performance standards if they hope to stay in operation.

Idling equipment and minor stoppages due to operational inefficiencies can become major problems if not addressed promptly. However, factories that rely on manual machine inspection processes often don’t know that a machine isn’t performing at full capacity until it fails completely. As a result, they suffer a loss of production that’s exacerbated by the need to divert human and capital resources to diagnostic and maintenance activities.

3. Output Quality

Large manufacturing operations have little room for error when it comes to production quality — they have to get it right the first time. If they don’t, they stand to lose potentially significant amounts of time and money and could suffer severe damage to their reputation as well.

Excess waste — scrap or output that can’t be sent to customers due to structural or cosmetic defects — often occurs when machines aren’t operating at optimal conditions. Whether this results from normal depreciation, overuse, or some other issue, it’s a loss for a manufacturer.

‘But understanding the actual state of your machines with a Machine Health platform is only the first step toward improving OEE.”

Maximizing OEE With Machine Health

Each of these components can be improved manually over time, but if your goal is to achieve world-class OEE, reactive and preventive maintenance approaches won’t get you there — and they certainly won’t get you there ahead of your competitors. Instead, you’ll need a Machine Health monitoring solution that gives you real-time visibility into equipment status, condition, and performance.

But understanding the actual state of your machines with a Machine Health platform is only the first step toward improving OEE. Being able to use those insights to prioritize maintenance schedules and take direct, targeted action to reduce machine downtime, failures, and waste represents the next stage of facility sophistication.

A Holistic Approach to OEE

Within every manufacturing operation, there is a production and a maintenance unit, and it’s logical to assume that the two might work together to pursue common goals. Historically, however, that hasn’t been the case. In fact, their operational mandates have often put these two teams directly at odds with one another.

Maintenance is typically charged with maximizing OEE, while production teams’ objectives tend to shift based on ongoing revisions to quotas or product specifications. Both units are indispensable, but the emphasis on speed and the immense pressure to meet market demands mean production goals are usually prioritized over machine health and maintenance. Inevitably, this leads to losses in productivity and excessive spending on maintenance.

“While industry leaders have understood and implemented TPM’s principles for decades, relatively recent advances in machine learning and data analytics have allowed manufacturers to truly bridge the gap between maintenance and production truly.”

In the latter half of the 20th century, Japanese manufacturing leaders seeking to remedy this situation developed a methodology designed to create synergy between the two teams: Total Productive Maintenance (TPM). The focus of TPM is on reducing operational costs through the continual improvement of equipment effectiveness, and its creators established OEE as a critical metric in manufacturing.

While industry leaders have understood and implemented TPM’s principles for decades, relatively recent advances in machine learning and data analytics have allowed manufacturers to truly bridge the gap between maintenance and production — in a way that the creators of TPM could only imagine.

The Next Evolution of Manufacturing

TPM makes everyone responsible for OEE and calls for a holistic approach to production and maintenance. When manufacturers have access to real-time Machine Health data, such an approach becomes possible.

A Machine Health data platform gives factory operators insight into key equipment metrics (e.g., speed, vibration, heat levels), enabling them to decide whether a machine requires intervention with pinpoint accuracy. These capabilities allow operations to move from preventive maintenance to predictive maintenance, opening up countless opportunities to improve overall manufacturing productivity and efficiency.

With this data, for example, operators don’t just know whether they have the equipment necessary to meet production goals on schedule; they also know whether their machines can support faster or longer production runs. They can then adjust production line speeds or switch production lines altogether to sidestep equipment problems. Without machine health data, however, operators often find themselves gambling on the unknown.

“On a macro level, Machine Health data can be combined with operational data, allowing manufacturing leaders to optimize facility design to maximize the OEE of individual machines.”

When Machine Health metrics decline, a Machine Health platform will alert managers that maintenance is needed before issues arise. The technicians performing repairs then have access to a vast array of data that allow them to work more effectively and efficiently.

On a macro level, Machine Health data can be combined with operational data, allowing manufacturing leaders to optimize facility design in a way to maximize the OEE of individual machines. While it may seem counterintuitive, the reality is that a single optimized machine can actually cause downstream problems and reduce the efficiency of a production line if other equipment hasn’t been similarly optimized.

The Right Data At The Right Time

For example, consider a bottle-filling machine configured to fill 1,000 bottles every minute. A two-minute accumulation time between the filler and downstream labeler means that the labeler will be shut down for two minutes when label rolls need to be replaced. Suppose the filler is “optimized” to fill bottles at a clip that exceeds 1,000 units per minute but the label roll replacement process hasn’t also been improved. In that case, bottles will be filled faster than they can be labeled, resulting in excess waste.

Without a Machine Health platform, operators facing the above scenario might shut down the entire production line for equipment inspection and conclude that the problem is a faulty labeler. However, with the right data, an operator can avoid unplanned downtime and simply reduce the filler’s speed to reset the line and continue production, without having to spot-check each machine individually.

“In the competitive world of 21st-century manufacturing, these operations will quickly become customer favorites, while those that persist with a reactive or preventive approach to maintenance will fall behind and eventually disappear.”

On The Path Of Continuous Improvement

The benefits of a predictive maintenance approach paired with real-time Machine Health monitoring are both instantaneous and cumulative over time. Manufacturers that adopt such an approach will be able to continuously improve OEE, allowing them to set increasingly aggressive production goals and deadlines.

In the competitive world of 21st-century manufacturing, these operations will quickly become customer favorites, while those that persist with a reactive or preventive approach to maintenance will fall behind and eventually disappear. With early adopters of sophisticated machine health monitoring and data analytics solutions already separating themselves from their peers, the window of opportunity for latent adopters to close the gap is shrinking fast.

Want to talk more about pumping up your OEE? Reach out!

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Over at Automation World, Augury’s VP of Strategy Artem Kroupenev is featured in an article that envisions the nature of future AI co-pilots. “The addition of AI co-pilots will help reduce production downtime, meet sustainability goals, upskill workers, enable decision-making, and more,” says Artem.

I had a great interview with David Greenfield from Automation World for his recent article, ‘The AI Co-Pilots Are Coming. What Are They?’. While I think we’re a few years off before every worker will use an AI copilot, with time, these copilots will be able to effectively collaborate with humans at any level of task planning and execution and complete large portions of work independently. 

3 Core Components For Effective AI Co-Pilots

However, these co-pilots will first require three key components for them to achieve maximum impact on the factory floor: 

1)  Collaboration: AI co-pilots should be designed to augment and empower human work, making it more efficient and insightful through ongoing collaboration and interaction.  

2)  Broad insight and applicability: AI co-pilots should be useful at multiple levels of work, including strategy, planning, and execution, and they should be customizable for multiple applications. 

3)  Agency: AI co-pilots should evolve to have agency and become more like skillful team members than tools to assist, collaborate, and independently perform a wide range of tasks.

What’s Needed To Bring AI Co-Pilots To The Factory Floor

As I observed during the interview: “Companies like Cognite, Siemens, and C3 all have come out with co-pilot concepts early in the hype cycle. But there is still some way to go to make these truly useful and reliable. The industrial world is heavily biased towards trust and reliability for a good reason, and making a co-pilot that can be fully trusted to provide accurate information and perform well in a manufacturing setting is currently one of the biggest obstacles to wider development and adoption. Reliable and unbiased data is what determines the accuracy of models, and manufacturing data is often neither reliable nor standardized.”

Besides the need for a reliable data source that Augury’s Machine Health Solution can provide, it’s also essential for manufacturers to follow another universal truth when applying technology: identifying those very specific problems in their operations they want to solve with that technology. 

As I concluded: “AI has the potential to solve a lot of organizational obstacles, but each solution is unique in the way it works to solve the biggest challenges. When shopping around for AI solutions, asking questions is key. Understanding the quality of data, the types of insights generated, and how the value is measured are critical before signing an agreement with an AI vendor. For the best outcomes, start with proven AI technologies that provide near-immediate value to gain buy-in for larger initiatives.”

Read the full Automation World article, ‘The AI Co-Pilots Are Coming. What Are They?’

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In the article ‘AI Disrupts the Workforce – But Not How You Think’ for Manufacturing.net, Augury’s VP of Strategy Artem Kroupenev argues, “Workers should come to terms with anxieties surrounding AI because it’s not going anywhere.”

This article was originally published on Manufacturing.Net on February 4, 2024.

“As companies everywhere implement AI into their tech stacks, the fear of becoming obsolete (FOBO) is a growing concern for U.S. workers. According to a new Gallup report, nearly 1 in 4 U.S. workers worry technology will make their jobs obsolete. However, for industries with an aging workforce, like manufacturing, AI helps major players maintain decades of experience and institutional knowledge,” according to ‘AI Disrupts the Workforce – But Not How You Think’.

Meanwhile: “The opportunity to work alongside tech also attracts the next generation of workers who see the industry in a new light – innovative, safe, more sustainable, and critical. The future of how we leverage technology in the workplace is bright, but only if we’re all working towards the same goal.”

The article then details that despite AI being the understandable cause of workplace anxieties, it is in fact our friend. 

Good For Business, Good For Workers

“It’s essential that workers in manufacturing and beyond come to terms with their anxieties surrounding AI because it’s not going anywhere. According to Augury’s recent State of Production Health 2023 report, 63 percent of manufacturing leaders plan to increase their AI budgets within the next year. This should come as good news to leaders in manufacturing currently grappling with widespread labor shortages. AI can augment their workers’ abilities and fill gaps. For workers interacting with the technology on factory floors, this brings endless opportunities for advancement.”

In the end: “The FOBO is not a fear that will disappear overnight, but while the public’s worries around AI continue to grow as technology advances, technology leaders must take action to remove these feelings of unease. This pioneering technology can up-level all industries in a way we never imagined. As we continue to see increased advancements and innovation, it’s an exciting time for the workforce. The time is now to learn, embrace, and let technology do its job: making yours more manageable.”

Read the full article.

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Augury’s VP of Strategy, Artem Kroupenev, wrote an article for Manufacturing Tomorrow about how thanks to digitalization, “once-massive tasks could be executed with improved quality and unprecedented speed.” And today, with AI, we are now beginning another – equally dramatic – transition.

The full version of this article was first published at Manufacturing Tomorrow on 14 October 2022.

“Between the third and fourth industrial revolutions, the application of knowledge experienced a near-seismic change. Almost overnight, it felt like companies jumped from an analog world to a digitized realm,” according to Artem. And now: “manufacturing companies are experiencing another transition, moving past the static digitized world to one marked by artificial intelligence.”

The Power of AI To Drive Solutions

AI can bring a lot to the table, says Artem. “With AI, manufacturing workers can identify and untangle problems more easily and creatively and, as a result, work more autonomously.”

But yes, as with any new technology, adoption is always challenging. And if it’s not adopted, it won’t be able to help manufacturers solve their most pressing challenges. However, applying Machine Health Solutions, could be an effective foundation to properly enable Industry 4.0.

“Machine health monitoring enables predictive maintenance by leveraging AI insights to alert manufacturers to not only when machines will break down, but also what can be done to minimize mechanical degradation. In fact, predictive maintenance software has the potential to define the outcomes needed for true Industry 4.0.”

Empowerment Through Predictive Maintenance

In the article, Artem goes on to sketch out the various advantages of having a predictive maintenance platform, including:

• Improved cost and production predictions means teams can go beyond routine maintenance to also perform preemptive repairs – thereby avoiding unnecessary downtime.
• Increased collaboration between operation and maintenance teams – thanks to the increased insights and expertise on the factory floor.
• Better insights for the leadership team to help them decide on how to move forward in instilling more innovative manufacturing initiatives – to further reduce downtime and enact process changes to improve yield, quality and efficiency.

Onward And Upward

“It only stands to reason that more insights would lead to greater capabilities,” according to Artem. “Employees have the information necessary to solve problems by looking at multiple solutions that can improve the bottom line and minimize the environmental impact of operations.”

And one success leads to another: “which could prove to be a driver of a fully realized Industry 4.0 and onward toward the next revolution.”

Read the full article.

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Innovation is often driven by a singular use case — one invention so revolutionary that widespread adoption is inevitable. Production Health is here to drive that change. It combines AI-driven insights across machines, processes, and operations to create a more holistic view of production lines than ever before. 

The first American Industrial Revolution was defined by the invention of the cotton gin in the late 18th century. It was a tipping point in manufacturing as cotton mills began to spring up throughout the Northeast and farther south along rivers and streams. Infrastructure followed, creating transportation systems like railways and canals as commerce and trade proliferated. Then we had the Edison light bulb, which drove electrification, and then the electric motor pushed industrial adoption. With each industrial revolution iteration, a singular use case — the gin, the lightbulb, the motor — forever changed the landscape of manufacturing.

What’s starting the next industrial revolution?

So, what is the metaphorical cotton gin of today?

The answer is in artificial intelligence (AI) — more specifically, the Production Health that AI enables. AI in manufacturing has led the way through Industry 4.0, but reaching the horizon, where Industry 5.0 awaits, hinges upon using predictive maintenance technology to continuously monitor machine health, both historically and in real time. These provide both holistic and in-depth views across the entire production line to optimize the manufacturing process.

Leveraging these insights, manufacturing leadership can reduce overhead and increase profitability. Then, they can work on Process Health: reducing waste, increasing throughput and yield, and improving the sustainability of the production line. Combined with Machine Health, we call this process Production Health — an innovative combination of AI-driven insights into the interdependence of machines, processes, and operations.

Related: Learn more about driving digital transformation in manufacturing.

Why is Production Health so essential to reach Industry 5.0? Consider the environment today’s manufacturers operate in. The industry also suffers from worker shortage, which is overall holding things back as manufacturing surpasses pre-pandemic levels with an expected 4.1% GDP growth for 2022. On top of that, there are more people in the world than ever before — meaning manufacturers must meet greater demand. Unfortunately, this also means more waste, both in terms of defective products and pollution.

Production Health can drive major improvements to solve these problems. With a holistic view into multiple aspects of your production, process-driven objectives can be met while also cutting down downtime and maintenance costs. And all that optimization leads to higher quality and yield and reduced waste and emissions.

How Production Health Leads to Industry 5.0

Manufacturing processes 20–30 years from now will be so refined that it’ll be unthinkable not to have the process sub-optimized to report exactly what’s needed based on raw materials, demand, equipment reliability, and other variables. But the greatest change is that unlike 4.0, which is marked by efficiency and automation, 5.0 adds another pillar: the human touch. As repetitive tasks are increasingly left to automation, people have more time to focus on higher-level work, personalization, and thinking outside the box.

This collaboration between human expertise and machines — hybrid intelligence — will completely change the industry. Processes will become more intelligent, and our ability to optimize machine life and resource usage will minimize the environmental impact of what we do while maximizing profitability. And processes will adjust with more ease to changing business targets.

How AI Insights Unlock Unheard-Of Manufacturing Optimization

Today, there are still manufacturers who think they need to rip out old machines and build new production plants to meet consumer demand and produce new iterations of products. That is neither efficient nor cost-effective, and the application of prescriptive maintenance technology can resolve that problem as well.

Related: Learn more about production health in our on-demand webinar.

AI-enabled machines run by a small team of highly sophisticated workers can unlock shadow capacity in existing plants. Companies have specific production problems, and when AI is targeted at those particular issues, teams can gain the valuable data and insights needed to improve them and then move on to address their next business concerns.

This is the future, today: We can combine the AI insights from process health and machine health, enabling a level of manufacturing optimization previously unheard of. Production Health means $1 trillion in added productivity and 16 billion tons of reduced emissions. Once hybrid intelligence achieves that, who knows what else we will change.

The Augury Way of Production Health

Augury provides end-to-end solutions that help eliminate production downtime, improve process efficiency, maximize yield, and reduce waste and emissions. We empower our customers with prescriptive insights into their processes and machines through AI-driven technology and industry-specific expertise that helps transform how they work.

For more information on how we can help you reach toward Industry 5.0, contact us today.

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Over at Manufacturing.net, Augury’s VP of Strategy Artem Kroupenev discusses four ways to attract and retain team members. In short: “The breakthroughs of today are what the future industry will be built upon. Invite job seekers to be a part of it.”

This article first appeared on Manufacturing.net on Aug 3, 2022 

No one needs to tell you that there’s a significant labor shortage in manufacturing. You need only look to your open positions to find proof of that. With the Great Resignation, job openings in manufacturing are at record highs: 800,000 each month.

Although great efforts are being made to bring manufacturing back to the U.S. and recruit the next generation of talent, manufacturing labor shortages are quite the hurdle to surmount. All is not lost, however. One study found that perceptions are changing around manufacturing, largely due to the industry’s response to the pandemic. But more work needs to be done to attract and retain top talent.

Money is often a good start, as are time-off benefits and greater schedule flexibilities, but companies need to think more about rewriting the cultural narrative of what manufacturing is to better reflect its current state. Technology in manufacturing has been reshaping the industry, but our recruiting tactics don’t reflect that. We show how working for a manufacturer is an attractive proposition for talent with the skills and background to succeed in the industry.

How to Attract Manufacturing Employees and Retain Team Members 

With such a labor shortage, it may be tempting to focus on training and recruiting the next generation of people to work in the manufacturing industry. But that’s not enough. We can’t just wait for a whole generation to grow up and enter the workforce. Instead, turn your attention toward getting the right systems in place to make the proposition of job opportunities more appealing to talent of all ages. The following are often the best places to start:

1. Create Higher Value With Manufacturing Positions
2. Increase Diversity Through Company Culture
3. Change the Perception of Manufacturing
4.  Institute Reskilling Efforts

Read more about these four approaches in the full article on Manufacturing.net.

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How manufacturers can assess and identify their assets’ relative criticality to improve equipment reliability and minimize wasted capital.

This article first appeared on Supply & Demand Chain Executive.

Companies around the world are increasing spending on supply chain innovation to effectively navigate pandemic-era disruptions from shortages to the Great Resignation. For the manufacturing industry, clogged supply chains restrict the flow of essential spare parts and equipment into manufacturing facilities. With longer lead times and limited resources coupled with record consumer demand, manufacturers must navigate the pressure to push more products out while getting fewer resources in by optimizing their maintenance and reliability programs to maintain the highest level of productivity possible.

But many companies today still tend to respond to supply chain disruption by panic-buying, stockpiling inventory they may or may not need to keep production lines running in the future. While it might supply some peace of mind to anxious manufacturing professionals, this approach also results in inventories filled with unnecessary stock, increased overhead costs, and even higher tax prices due to retaining tangible assets.

Instead, manufacturers must assess and identify the relative criticality of their assets and take every possible step to protect and maintain the most essential equipment. With a thorough understanding of asset criticality, they can minimize wasted capital in two key ways: by not overbuying spare parts and by having the right parts on hand to prevent critical failures and long downtimes.

Asset criticality assessments for optimizing inventory

The goal of the asset criticality assessment is to uncover data to inform strategic decisions for manufacturing maintenance and inventory. With a criticality score tied to each machine, manufacturers gain a better idea of which machines they should invest in monitoring continuously.

By monitoring the machine health of critical assets in real time, manufacturers will know in advance when machines are likely to fail and what parts they’ll need on hand to prevent downtime. Equally important, they’ll identify non-critical assets for which they don’t need spare parts inventory, enabling lower overhead costs.

When determining the criticality of machines, manufacturers must look for the assets that would shut down the production line in the event of failure. These are the critical pieces of equipment and where organizations will want to spend strategy and money. Equipment that isn’t vital for running the line or that manufacturers could find easy workarounds to continue operations is non-critical.

Adapting manufacturing maintenance and reliability practices

After distinguishing critical from non-critical equipment, manufacturers must adjust maintenance and reliability practices to ensure sustained productivity. Start with three key steps:

1) Monitor the right equipment continuously.

When machine health sensors collect and report continuously on the machines to which they are assigned, maintenance technicians know which machines need attention and when long before machine failure. But performing maintenance on non-critical assets will be an inefficient use of labor that would be more valuable spent on critical equipment.

In addition, constant data collection can be a pricy investment with low ROI if sensors aren’t strategically placed. Once you’ve determined your most critical assets, adjust your monitoring approach accordingly to allocate expensive sensors only to production-vital equipment.

2) Stop stockpiling unnecessary inventory.

Many organizations are currently overbuying parts to circumvent machine downtime due to parts shortages, but this is an expensive and inefficient strategy. For one, it exacerbates the shortages as companies buy out resource stock, potentially inflating prices for future purchases.

Another direct issue is that keeping extra, unused parts in storage just adds to a list of taxable assets, which can become a significant expense for manufacturing companies. Instead of panic-buying parts, use data from continuous monitoring of critical assets to predict when machines will need repairs with plenty of lead time, and prioritize your parts inventory based on criticality scores.

3) Avoid extreme preventive maintenance.

Technicians tend to perform maintenance on set schedules. For example, if the failure rate of certain parts shows bearings usually fail after six months, technicians replace those parts in advance to prevent the failure — every five months, in this example.

This might be a more targeted method than monitoring all equipment at all times or stocking up on replacement parts for every asset, but it can still be wasteful if organizations put a lot of capital into unnecessary repairs. Instead, insights from the continuous monitoring of critical equipment can help companies optimize inventory and labor by determining not only when machines are likely to fail, but also what maintenance teams must do to prevent it.

With proper asset criticality assessment and strategic continuous monitoring, manufacturers can predict necessary repairs, enabling longer lead times for securing parts and making data-driven decisions for inventory and labor allocation. It’s the smart way to keep operations resilient, keep production lines running, and limit costs well into the future, no matter what unforeseen disruptions may come.

If you’d like to learn more about our services, watch Augury’s Solutions Architect Brian Richmond discuss the business case for performing a criticality assessment and gain valuable information on how to create your own matrix in our on-demand webinar.

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With an effective digital transformation strategy, organizations can transform their production ecosystem into something more insight-driven, streamlined, and responsive to specific customer demands. Digital transformation in manufacturing can provide businesses with a better way to handle supply chain disruptions and give them a considerable competitive advantage.

Since early on in the pandemic, manufacturers have been dealing with supply chain disruptions. Shipment delays and a lack of supplies became the top COVID-19 worries of supply managers in 2021, and 2022 hasn’t done much to allay those concerns.

What first looked like a pandemic-induced hiccup is actually a long-term problem that organizations can no longer hope to wait out. Even if today’s shortages bounce back, manufacturers must be proactive to develop resiliency over the shortages to come. Implementing a strong digital transformation strategy will provide organizations with the control they need, both over their manufacturing operations and over their supply chains.

This type of transformation is not an easy fix. You’ll need people willing to shake up existing structures and employees ready to learn new skills and face new challenges. It will require a willingness to take a completely new approach to operations to truly bring manufacturing into the digital age. But by the end, you will have a company that is not only ahead of the pack, but also ready for whatever bumps in the road may come next.

How to Ensure Your Digital Transformation Strategy Will Produce a Competitive Advantage

Digital transformation in manufacturing has the power to significantly improve efficiency, giving you the edge over the competition. However, not every transformation is a success. While organizations that hit their transformation ROI goals surpassed those goals by an average of 50%, there were also many who failed to hit the mark, underperforming by an average of 30%. With that in mind, here are three strategies to help you overcome the challenges of digital transformation in manufacturing and, ultimately, reap its rewards.

1) Use Remote Management to Make Space for Major Strategic Initiatives

With fewer people on-site than ever, now is the time to switch to a machine health monitoring platform with a robust suite of remote management tools. Not only will this enable smarter predictive maintenance, but it will also free up a notable amount of your managers’ time. This will allow them to focus instead on important issues such as deployment and root cause analysis. It will also make it possible for them to plan out and enact innovative manufacturing initiatives designed to address deeper, portfolio-level problems rather than spending all their time on routine maintenance issues.

2) Prepare Your Employees for the Digital Future

One of the major challenges of digital transformation in manufacturing is making sure reliability and maintenance teams have the skills necessary to excel in a more modern environment — there’s simply not enough time in the day with all the routine (and often unnecessary) maintenance. Luckily, AI-driven machine health monitoring doesn’t just free up management’s time — it also lightens the load of employees and technicians, providing them with enough time necessary to focus on higher-level work.

At Augury, we’ve seen this firsthand. Companies that implemented our machine health platform saw employees across plants able to quickly start learning the precision maintenance skills necessary for their new roles while also advancing to a higher level of collaboration between operation and maintenance. Many employees even have enough time to be trained in new capabilities off-site, bringing new expertise to the manufacturing floor and strengthening their company’s digital transformation strategy.

3) Create a Culture of Collaboration and Accountability Around Machine Health

To effectively implement a digital transformation strategy, you need to get your whole staff on board. Collaboration between floor workers, technicians, reliability experts, and management is crucial if you’re serious about creating an efficient and productive business. Luckily, modern machine health monitoring platforms make cross-departmental communication easy. Even better, collaboration through this platform is built on a foundation of AI-driven insights, prioritizing verifiable facts over personal opinions.

Regular conversations around machine health should be encouraged and records of these communications captured and kept within the platform. This will provide you with a clear chain of accountability and build up a database of AI-backed insights from an ever-growing record of machine performance. These insights will inform future decisions and can be used to improve operations on both the site and portfolio levels.

It’s not certain that supply chains will ever get back to where they were pre-pandemic. Instead, it’s time to embrace innovative manufacturing strategies and digital transformation. This can increase the resiliency of your operations, allowing you to optimize production lines, increase output, and put you firmly ahead of the competition.

Interested in seeing how digital transformation can accelerate your growth? Reach out to Augury today.

The post How to Stay Competitive in Manufacturing Through Digital Transformation appeared first on Augury.

Although lean manufacturing is highly efficient in the best of times, it’s also highly susceptible to disruption in the worst. Fortunately, efficiency and resiliency aren’t diametrically opposed.

This article was originally published in Supply & Demand Chain Executive.

Companies continue to face supply chain disruptions worldwide and these disruptions and product shortages will likely continue throughout the year. While there are multiple factors contributing to these supply chain issues, one major factor can be attributed to the lean manufacturing methodology of companies keeping inventories intentionally lean to reduce costs. Under this system, new shipments and raw materials arrive just in time to fill a demand for new products.

The pandemic proved that existing methods for efficiency don’t necessarily translate into resilient manufacturing. Although lean manufacturing is highly efficient in the best of times, it’s also highly susceptible to disruption in the worst. During a global pandemic, for instance, the need for more resilient manufacturing is stark. Fortunately, efficiency and resiliency aren’t diametrically opposed. With the right technology, companies can enjoy the best of both worlds.

Operational flexibility is the missing link

In 2019, McKinsey conducted a study that examined the performance of companies following the financial crisis of 2008. It found that the most resilient companies did something different than the others. In addition to efficiency, they built their business models around financial and operational flexibility. By quarter one of 2008, they had cut costs by 1%, and by the time the crisis reached its trough in 2009, they’d boosted earnings by 10%.

Cutting costs and focusing on growth are the traditional pillars of resilience. Today, however, a new wave of resilient manufacturing has emerged in response to restrictions that were put in place at the beginning of the pandemic. The manufacturing workforce, supply chains, and product demand have all been affected by COVID-19. 

In this new environment, being operationally and financially flexible is not enough. Before the pandemic, digitization was a huge accelerator to efficiency and resilience. Companies that digitized early could cut costs and increase revenues; now, they’re much further ahead than companies that are still struggling with resilience.

Being digital is a critical component of resilient manufacturing, which is why some newer manufacturers have been slower to feel the supply chain drag. In fact, many have grown significantly faster due to their heavy reliance on digitization. Tesla, for example, has been super resilient from the start, because the entire workflow is digitized, and its people know how to communicate with customers digitally — including taking, fulfilling, and delivering orders. In the industrial space, efficiency and resiliency are built by companies that invest in these same elements.

Why resilient manufacturing isn’t a lost cause

Resilient manufacturing is essential in the current environment, and building it will provide manufacturing facilities with significant flexibility beyond the pandemic. To create a complex lean manufacturing supply chain, things need to combine and work together in a highly efficient manner. Companies have to cut out the fat by optimizing every system for efficiency.

The assumption is that each component of a product will be delivered just in time for it to be assembled on the factory floor. When a local, regional, or global crisis introduces systemic risk to any part of the supply chain, that link in the chain is lost. The component may never reach the factory floor because optionality and buffers have been optimized out.

Manufacturers must combine lean manufacturing with resilient manufacturing methods. That requires a digital thread running through the entire operation, with end-to-end operational visibility across the whole supply chain. With greater visibility, companies can predict shifts in demand with much more flexibility.

Operational visibility also creates a wider base of optionality and suppliers. Digital visibility shows, in real time, what components or raw materials manufacturers need, how their supply chain is providing them, and what they’re manufacturing with them. Companies can efficiently switch suppliers or products in response to, or even before, major disruptions.

Operational flexibility combines efficiency and resiliency

For manufacturers to effectively combine efficiency and resilience, they need enough data, knowledge, and understanding of what their supply chains look like at any given moment. They can predict major events to reduce or completely avoid disruptions to their manufacturing processes.

To achieve this combination, companies need three important digital threads:

1) Total visibility into financial and operational flexibility: The most important digital thread is a digital space where key people in the supply chain can see it all. They can understand demand and sale smetrics, as well as analyze production, physical assets, delivery networks, logistics, and supplier bases. Ensure that sales, marketing, production, and all other departments can see the same numbers in real-time and make decisions together based on that single truth. This will be the supply chain control tower.

2) Real-time machine health monitoring in all factories: In a lean manufacturing model, efficiency is based on meeting production needs. As the primary source of that production, machines are a company’s biggest assets, and a company needs full visibility into its machines’ health and performance at all times. That includes predictive knowledge about how they’ll perform under different conditions. Machine health monitoring platforms can gather data from IoT networks and use AI algorithms to deliver predictive knowledge based on that data, creating greater visibility in every factory.

3) Remote collaboration through a connected worker platform: How machines will perform under different conditions is an important aspect of operational flexibility, but most operations rely as much on people as they do machines. It’s just as important to know how employees will work together when they can’t be near one another. Connected worker platforms allow them to work remotely, collaborate, and share operational data and insight so they can become more autonomous in the face of disruption.

Lean manufacturing methodologies have traditionally lacked this level of visibility, insight, and flexibility, which hindered many supply chains amid the pandemic. By introducing technology to boost real-time visibility and remote collaboration, however, companies can combine lean manufacturing efficiencies with an unprecedented level of operational resilience.

Do want to learn more about taking lean manufacturing methods to the next level? Connect with our team.

The post How to Combine Resilience with Lean Manufacturing Methodologies appeared first on Augury.

Supply chain disruptions have now become commonplace, and the Manufacturing Leadership Council highlights supply chain improvement in 2022 and beyond as essential to the health of manufacturing. More than ever, manufacturers need resilient and agile supply chains to anticipate and overcome crises. According to the council, creating collaborative supply chain network strategies is key. Quickly sharing key data, insights, and material needs among key partners will foster agility and innovation.

This article originally appeared on 28 February 2022 at Global Trade Magazine.

But we need to update our collaboration strategies because the U.S., and much of the rest of the world, last truly focused on supply chain resilience more than 70 years ago. During World War II, manufacturers saw industry collaboration at unprecedented levels as the Allies needed a dependable supply chain for the war front. Consequently, the American government forced collaboration on a top-down, streamlined supply chain with a singular focus. Every company produced a different part, but their common goals superseded their desire to compete and spurred efficiencies.

We’re no longer facing these stark geopolitical challenges, but we are at a supply chain crossroads. The knowledge and agility needed to meet today’s challenges have reached a similar point where no company, regardless of size, can adjust individually to meet demand. The demands of the modern market necessitate collaboration.

Want to learn more about how Augury’s machine health solutions help you keep up to demand? Reach out.

Overcoming Reluctance Toward Cooperation Between Manufacturers

Companies hesitate to engage in collaboration, and that makes sense: If you can move faster, you have a tremendous advantage. Why bother to share? The answer lies at the intersection of philosophical and practical justifications. From a philosophical side, manufacturers that pride themselves on innovation shouldn’t be afraid of imitation.

This leads to the practical side: If you hold back on sharing innovative ideas, tools, and frameworks, you slow your whole industry. A leading company may gain a short-term advantage, but down the line, it won’t be able to gain anything from others. In the modern world, there’s no such thing as the “smartest person in the room.” It’s a global room. If you aren’t willing to share some of your insights, you could cause long-range setbacks for your business and your industry.

One globally recognized consumer product goods company gave competitors an insider look at how it made recyclable tubes. Being collaborative didn’t lower the company’s credibility. It illustrated the company’s leadership and cemented it as being true to its mission toward developing more sustainable manufacturing practices.

Moving Toward an Ideology of Supply Chain Collaboration

What will it take to make manufacturers feel comfortable establishing a two-way street when it comes to sharing their supply chain data or innovations? The following strategies will help:

1. Develop universal rules and terminology around collaborative efforts.

Right now, there’s no single language or rulebook that allows manufacturers to communicate confidently among themselves. We just aren’t sure what to share, so we think we must share everything. This makes collaboration feel overwhelming and unrealistic. Having a single language that all manufacturers use to communicate across industries and regions would reduce the latency around collaboration.

For example, we know that sharing asset-level information like makes and models can be useful. But how about the deeper metadata that involves how the item works or the best practices to maintain it? Which metadata is useful enough to send out? And how can it be shared in a commonly understood and recognized format? These are all important questions that can be answered by universal guidelines, which would allow for better machine servicing and create more efficient and sustainable production lines.

Clearer language also helps identify what information should be protected to prevent others from stealing core IP by reverse-engineering processes.

2. Share use cases regarding successes, failures, and best practices.

A lot of manufacturers struggle to use digital transformation (DX) principles to improve their supply chains. They’re stuck in the pilot phase, according to McKinsey research. Understanding how others adopted and scaled their DX initiatives could be extraordinarily helpful.

The World Economic Forum’s Global Lighthouse initiative is already facilitating the sharing of DX use cases across industry silos. There are also peer-level customer advisory boards and industry-level groups sharing implementation practices.

Make no mistake: DX is essential to unraveling knots in the supply chain. The right DX applications can improve the entire global manufacturing “organism.” The more manufacturers learn from one another’s mistakes, the faster the industry can evolve. Not participating in these forums or groups means losing out on valuable information. 

3. Upskill and reskill manufacturing workers.

The Great Resignation is making it harder to source and hire talented people, especially with older workers retiring and taking key institutional knowledge with them. This is a huge challenge: Companies need to onboard new workers, and there’s intense competition for the new generation of technical talent who will drive future innovation. Even current workers may need upskilling and reskilling, too, especially in the latest digital tools to make their roles more effective.

These are significant challenges, and manufacturers need to quickly gather insights, data, and best practices around workforce development. The industry, however, lacks the tooling needed to share data efficiently like in the software industry, which has a tremendous amount of tools, academies, and online capabilities that have enabled people to learn to code and allowed collaborative employment models with apprenticeships. We need this same level of collaboration among upskilling employees.

Allowing the people themselves to collaborate helps. There are forums for VPs or management roles to share insights but few, if any, forums for technicians across different industries to collaborate.

4. Find solutions around sustainable manufacturing.

Corporate leaders constantly say, “We need to be more sustainable.” But how many are taking steps toward sustainability? The whole industry needs to become more effective, efficient, and sustainable, and the more collaboration we create there — sharing data and insights on implementing sustainable practices — the faster it’ll be to move forward.

Even if sustainability weren’t the right focus ecologically, it’s right operationally. An organization that’s not sustainable has little supply chain resilience and will need to change tactics as resources run out. If you don’t have real initiatives in place to make the supply chain more sustainable over time, resilience won’t even matter.

Ultimately, we need data-driven standards around improving sustainability. Technology allows us more real-time data than ever, but we need to improve how our initiatives use that manufacturing data. Sharing a digital roadmap of best practices and insights or utilizing cross-company supply chain initiatives makes it quicker and easier to make supply chain improvements.

Plenty has changed since WWII’s collaboration among manufacturers, but the benefits of cooperation haven’t. Let’s respond to today’s supply chain concerns by revisiting the advantages that come from coming together.

Looking for new partners? Reach out! 

The post Want a More Resilient Supply Chain? Collaboration Is Key. appeared first on Augury.