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Insights that convert into actions can improve equipment reliability, span the enterprise, and boost the bottom line.  

TAKE AWAYS:
● Using data and analytics for condition monitoring can eliminate unplanned downtime and allow for improved equipment reliability.
● Centralizing maintenance roles and utilizing remote support can help manufacturers alleviate labor scarcity and maximize their technical teams.
● Machine health monitoring can trigger corrective actions to be scaled across multiple production lines or multiple sites. 

Manufacturers are acutely aware of how machine health affects production throughput, particularly plants operating in a throughput-constrained mode. Without sustainable equipment uptime, schedules are missed, orders go unfulfilled, revenue is lost, and unplanned labor and repair costs are incurred.

A significant factor impeding the achievement of operational goals is the widespread, protracted shortage of asset reliability and maintenance talent. Fortunately, technology can alleviate this challenge.

The burgeoning depth and breadth of condition monitoring analytics technologies offered by countless solution providers aims to eliminate unplanned downtime. The core value of machine condition monitoring is twofold: (1) drive best practices in reliability and maintenance; and (2) mitigate the skills gap now and into the future. Its primary goal is harvesting analytical insights directly from machines to identify the opportune time to service degrading critical equipment and components — not too early, nor too late.

“Gaining maximum value requires putting the data and analytics to good use.”

Mastering how the condition data is harnessed, analyzed, and operationalized is key. With digitalization boosted by the industrial IoT, such as wireless condition monitoring sensors, plants can collect and centralize for analysis unprecedented quantities of real-time, streaming asset condition and performance data, along with batches of intermittently connected or locally captured data. Gaining maximum value from this approach requires putting the data and analytics to good use.

Operationalizing Data Analytics

Many plants lack the internal resources to leverage machine condition monitoring for troubleshooting and repairs, let alone navigate the complexities of developing scalable analytics solutions. The prior model of staffing qualified, dedicated at every manufacturing facility is no longer sustainable.

Industrial service providers are responding to this reality by adapting and optimizing their own technology and methods, while developing new ways to provide actionable remote support to the factory floor. Over the past 12-15 years, the aging manufacturing talent pool and increasing demand for domestic manufacturing compelled leading service companies to refocus on centralizing core roles, such as predictive maintenance specialists, reliability engineers, and high-level positions.

This development is driving a cultural shift in manufacturing. Internal plant maintenance teams are not inherently inclined to ask for help, but once they begin accepting virtual support, they can transition from their singular plant focus and leverage their skills out to other locations.

Machine health monitoring is also a paradigm shift for the remote support providers, who now actively pull condition data for analytical insights and translate it into action on the factory floor. For instance, when an alert is received at a centralized technology center that an asset condition threshold has been breached at a certain plant, an expert can reach out to the appropriate individual on the factory floor to provide crucial guidance remotely, in layman’s terms.

“The core value of machine condition monitoring is twofold: (1) drive best practices in reliability and maintenance; and (2) mitigate the skills gap now and into the future.”

Rather than details such as the frequency, hertz, and amplitudes of the waveform, what a technician really needs to understand is, for example, that it looks like a specific coupling is coming loose. In this example, that would mean they need to take that machine out of service, validate that the motor is meeting its alignment specifications and everything is in safe working order, and then torque the coupling to its proper specification. Additional details or step-by-step instructions can be provided by the remote service expert to the asset-facing technician via a mobile device.

Moreover, this new approach to monitoring and analytics is driving significant innovation in maintenance automation processes. A simple example is instead of regularly pumping grease into an asset, data analytics from that piece of equipment can trigger a command to an auto-lubricating device bolted onto the machine to automatically inject the correct amount and type of grease into the equipment. This optimizes the process while also eliminating the need to involve a maintenance technician.

Figure 1 – Accelerating Maintenance Maturity

The Evolution of Maintenance Philosophies

Maintenance philosophies have changed markedly over the years (Figure 1). For a long time, maintenance meant running equipment until it breaks, and then fixing it, though this caused excessive unplanned downtime. When preventive maintenance practices emerged, time- and usage-based plans and routes were developed to keep the equipment in better condition, though the risk of downtime from over- or under-maintenance was still a concern.

Condition-based maintenance practices flourished when skills gaps intensified the need for labor efficiency. Having sensors continuously monitor and measure machine condition parameters and trigger alerts when a given threshold is met allows scarce labor to be applied to situations that actually need attention.

“Internal plant maintenance teams can utilize virtual support to transition from a singular plant focus and leverage their skills out to other locations.”

The latest realm of maintenance maturity is predictive maintenance — applying condition analytics and prognostics to predict when an asset is likely to fail, forecast the asset’s remaining useful life, and plan maintenance processes, people, and parts with precision to get the maximum life out of the equipment and components. Ideally, this approach leverages not only real-time condition monitoring data, but also historical trends and contextual information such as environmental conditions, process output, and maintenance histories.

Continuous condition monitoring analytics are central to the highest level of maintenance maturity, known as precision maintenance. It allows the work to be predicted and stretched out for as long as possible, maximizing the utilization of time and labor while maintaining the highest asset availability and throughput opportunity.

Extending Maintenance Optimization

Condition monitoring analytics enable plants to optimize their asset maintenance practices and migrate to an enterprise-wide predictive maintenance philosophy. Even non-critical assets that remain on route-based preventive maintenance schedules can benefit from predictive analytics. This is because manufacturing facilities frequently operate identical or similar types of equipment across multiple lines within a plant, and across any number of sister plant locations.

Specifically, any machine health finding that triggers a corrective action can be leveraged across other assets that have, or are developing, a comparable condition. Additionally, the knowledge gained from condition analytics and root cause identification can help to refine maintenance plans and intervals for all equivalent equipment. Extrapolating the findings to additional equipment in this manner optimizes both preventive maintenance schedules and predictive maintenance activity, further reducing unplanned downtime and extending the mean time between failures.

“The knowledge gained from condition analytics and root cause identification can help to refine maintenance plans and intervals for all equivalent equipment.”

A good case in point is when a specific lubrication issue at a plant was identified as the root cause of unplanned downtime. A little digging into their maintenance processes revealed that the preventive maintenance plans did not include the appropriate tasks to prevent the lubrication condition. That deficiency affected not just that single piece of equipment, but all equipment of that type, across all facilities. The results of that initial finding led to adjusting all the maintenance plans for 26 pieces of equipment across 12 facilities, increasing efficiency and preventing similar failures.

Reaping Strategic Benefits

The advantages of mastering this strategy are manifold. Consider a plant that purposely overheats aggregate to make sure it is completely dry. It may consume twice as much natural gas than is really needed to properly manufacture the product. From a financial perspective, passing that extra cost onto the consumer creates some competitive challenges for the manufacturer. From a sustainability perspective, excess emissions and gas transport requirements create a larger carbon footprint. With proper temperature monitoring, such challenges can be avoided.

Here are three of the primary benefits of operationalizing condition monitoring analytics:

• Planning and scheduling: Having access to machine health data analytics enables data-driven prioritization of work planning and scheduling for consequential labor and cost efficiencies. Online condition monitoring allows issues to be addressed as they arise, based on detectable early warning signs, with well-planned and strategically timely corrective actions. Since time- and cycle-based maintenance plans are conducted regardless of an asset’s actual condition, changes go undetected between routes, and random failures are routinely missed.
• Equipment reliability: Continuous machine health monitoring allows plants to move quickly on resolving predicted failures, maximizing uptime rather than reactively repairing failed equipment or components. Monitoring can reveal sudden large changes such as those attributed to a process change or machine crash, or gradual changes over time, including micro trends. By implementing AI and machine learning algorithms based in statistical process control, the degree of change can be tracked on an ongoing basis, and insights can be gleaned to determine reliability engineering needs and root causes.
• ESG and sustainability: Actively monitored and well-maintained equipment tends to operate in a highly efficient and effective manner. Likewise, when environmental conditions such as humidity, temperature, and cleanliness of the air are continuously monitored and proactively maintained, the benefits to human and machine health are great. Conversely, improperly maintained equipment running under production load is more prone to energy waste, raw material waste, scrap, quality defects, minor stops, equipment outages, or catastrophic failure.

Consider Monitoring as a Service

Many plants want to move to a condition-based mindset but lack the engineering and development capabilities in maintenance and IT to pull together a robust package on their own. Choosing remote condition monitoring as a service may be the answer for plants that are short on time and internal technical talent.

Industrial service providers with extensive experience in predictive technologies are adept at designing, implementing, and managing fully integrated condition monitoring analytics solutions. Those with the added capability of providing centralized, remote support for hundreds of manufacturers in multiple industries are uniquely well versed in operationalizing analytics in plants and across plant sites.  M

About the author:
Micah Statler is the Director of Operations at Advanced Technology Services

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What does it take to be a digital transformation champion? Anheuser-Busch InBev can tell you.

The world’s largest brewer won four Manufacturing Leadership Awards in 2022, including the highly coveted Manufacturer of the Year. (The honors are given annually by the Manufacturing Leadership Council, the NAM’s digital transformation arm.) The MLC chatted with AB InBev Global Vice President Marcelo Ribeiro recently to get his insights on the processes, technologies and strategies driving the company’s success.

Business transformation drivers: “We have a dream at ABI, which is ‘to create a future with more cheers,’” said Ribeiro. “[That means] a clear strategy to lead and grow, to digitize and monetize our ecosystem and to optimize our business.” Here are a few ways AB InBev is pursuing that dream:

• Developing and delivering products that give consumers what they want, when they want it
• Making sure the supply chain can adapt quickly to consumer needs
• Increasing capacity without compromising safety, quality or sustainability

Rising to challenges: “The future is becoming less predictable,” Ribeiro said. “We need to prepare for that, so we have to build a more resilient, flexible supply chain.” Additional opportunities include:

• Moving from transactional relationships with vendors and suppliers to partnerships
• Looking beyond operations and across the entire supply chain to meet sustainability goals
• Creating a collaborative manufacturing ecosystem that fosters the sharing of ideas

Meeting the digital future: Ribeiro says that ABI’s digital strategy has three key aspects:

• Making data more accessible and available to frontline workers
• Creating a template for digital technology that can be easily tailored to the unique needs of each business
• Using advanced analytics to contextualize data and discover where it can best be applied to aid decision making

Leaders required: Ribeiro noted that leadership is essential for making this vision a reality.

• “It is critical to empower the front line,” he said. “Leaders should be focused on providing the resources to allow people to do the work and achieve excellence themselves. In the end, people are key for any business transformation.”

Find additional insights into AB InBev’s digital transformation in DIALOGUE: AB InBev’s Award-Winning Dream, or make plans to attend Rethink, where Ribeiro will present a keynote address on “Building Your Enterprise into a Digital Transformation Champion.”

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Electronic waste is a big problem.

In 2019, the world generated a record 53.6 million metric tons of discarded electronic and electrical devices, according to a Global E-waste Monitor report. That’s an increase of 21% in just five years. But there’s more: The figure is expected to double by 2050, hitting 120 million tons annually.

The good news is that manufacturers can be an active part of the solution. Though their bread and butter has typically been bringing new products to market, manufacturers are now also developing end-of-life processes for goods to mitigate environmental impact, according to Bright Machines Vice President of Industrial Solutions Adam Montoya, writing in the Manufacturing Leadership Council’s Manufacturing Leadership Journal. (The MLC is the digital transformation division of the NAM).

The challenge: complex components. Disassembling a product is not nearly as straightforward as assembling it, according to Montoya. Take a server, for example. A company might know what’s inside it based on its original configuration, but memory or processor upgrades could have changed over the course of its life.

• When a lot of change has taken place, the dismantling process is unique to each server, making it complex and difficult to automate.

The solution: intelligent disassembly. Improving the end-of-life process for electronics requires intelligent disassembly, a combination of smart technology and a different way of thinking, says Montoya. Here’s how it works:

• Automation technology that uses AI and advanced vision systems interprets the contents of a particular component and compares it against the original blueprint.
• Next, the system assesses the presence and location of components within the unit.
• It then sorts, separates and removes components so they can be reclaimed or recycled.

The bottom line: Manufacturers stand to realize many benefits from intelligent disassembly. Components with sensitive data can have machine-driven proof of destruction. Systems with usable parts can be repurposed rapidly.

• Ultimately, it’s an important way for manufacturers to collectively reduce carbon footprints and electronic waste while delivering business value, says Montoya.

For more on this topic, read Rethinking End-of-Life Technology Value in the Manufacturing Leadership Journal. And to learn more about how manufacturing leaders are undertaking digital transformations, join the MLC at its Rethink conference in Marco Island, Florida, on June 26–28.

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A personal perspective by Augury CEO Saar Yoskovitz on his most important takeaways for manufacturers from the World Economic Forum’s annual meeting in Davos last month.

This year’s World Economic Forum’s (WEF) Annual Meeting at the Swiss resort of Davos was both intense and inspiring. Among the many attendees, world leaders in government, business, and social institutions were present. Speaking to them and hearing their thoughts is a unique opportunity as those conversations can shed light on global problems and innovative solutions. And since the previous WEF Annual Meeting last May, the mood has shifted.

Last year, during Davos 2022, the world was in the midst of a recession, the Russia-Ukraine War had just started, supply chains were at risk, and “de-globalization” was one of the most used words at the event. Any discussion about the environment or financial support for sustainability seemed hollow in those conditions.

This year, things appear to be considerably more stable: Ukraine is holding up, the energy crisis has been avoided in most countries, the world’s inflation appears to be under control, and supply networks are expanding. All these together made it possible to have a far more fruitful discussion on how to address the main problems manufacturers are currently facing.

In It Together

The first key takeaway was that manufacturers are increasingly coming together as the challenges smaller and mid-size companies are experiencing look very similar to those that some of the world’s largest companies are battling with at the moment. Everyone is looking for signs of economic certainty over the next couple of years. Leaders of all types of companies are trying to balance the need for expansion with wise resource management.

Simultaneously, global issues such as sustainability, workforce change, efficiency, and the effectiveness of a global industrial and manufacturing base are all being considered seriously by manufacturers big and small, with workforce and skills-related topics being top of mind. Businesses of all sizes now understand that progress and growth still depend on the fundamentals: people coming together to work out the details, agree on a course of action, and put in a genuine effort to bring about change.

Technology’s Coming of Age

Each annual meeting brings a rush of studies on a range of topics. This year these included  how the circular transformation of industries is unlocking new value, how over a hundred WEF Lighthouse Factories are showing the way forward to a more sustainable future, and how various industrial clusters are using technology to move towards Net Zero. What’s good about these reports is how they’re starting to focus more on what’s actually happening, rather than what should be done.

The next stage of this involves scaling those world-leading Lighthouses, highlighting the unique cases of transformational success that shine brightly across manufacturing. In many ways, this represents the next step in building a global society in which the combined efforts of humans and machines improve the quality of life in all respects.

Enter Glocalization and Friendshoring

While “de-globalization” was last year’s big word, in 2023 it was replaced by “glocalization”. This captures the idea that while our supply chains are becoming more localized, they still need to be internationally connected. Glocalization strategies can help manufacturers become more resilient, a trend that is also happening naturally as part of the global energy transition. Alternative energy sources are typically closer to home and more difficult to move. For example, solar energy is produced during the day and wind energy during windy conditions in multiple locations but, at the moment, neither the bulk storage nor transportation methods exist for these two types of energy to be easily transferred any significant distance, which naturally leads to more decentralization. Factories will tend to be located where the cheapest and cleanest energy is available, as previously happened with data centres.

Friendshoring was another emerging term at the WEF this year. It is swiftly replacing the traditional strategy of offshoring and reflects how manufacturers are now turning towards countries that share similar values and have more compatible trading approaches. Friendshoring can also help reduce reliance on a single source. This is especially evident in the semiconductor sector where approaches like the US/EU IRA and CHIPS laws represent a significant step forward.

Circular Supply Chains

Larger organisations are also giving more sustainable and circular supply chains top priority as a result of impending regulations around carbon reporting, like Scope 3. This reporting looks at a product’s whole footprint by taking into account the upstream and downstream environmental impacts of its supply chain.

Some people have already called such developments “a nightmare.” It can definitely be hard. Many companies are doing their best to track supply chain emissions effectively, but there is a severe lack of the necessary frameworks or tools to do so. To make matters worse, Scope 3 also varies depending on the manufactured product. For example, Scope 3 only accounts for 6% of the emissions from a cement factory, but it might account for 80% of emissions in the automobile or food sectors. As a result, manufacturers need to come together as an industry to make the reductions needed to their overall carbon emissions.

Talent and Talent Again

Access to talent is still a hot topic, but with a new twist. Now, companies are shifting their focus from Labour Cost Arbitrage to Skills Arbitrage when making investments in new geographies. In other words, people are asking themselves about the locations of the best talent that can make better use of automation and digital tools to increase productivity. This shift towards skills is expanding the talent pool.

A Way Forward

In short, this year’s WEF leaves space for optimism. Manufacturers have more clarity on the challenges that lie ahead, they understand more about how to overcome them, and they have the right tools to do so. It certainly won’t be easy, but by working together, manufacturers, governments, and wider industries, have a better chance than ever to make a difference and chart a clearer path forward for the future.

Saar Yoskovitz is Co-Founder and CEO of machine health company Augury.

Manufacturing Leadership Journal content and MLC resources are exclusively available to MLC members. Please sign up for an account or log in to view this content.

Manufacturing Leadership Journal content and MLC resources are exclusively available to MLC members. Please sign up for an account or log in to view this content.

AI promises to foster a mutually augmented human-machine relationship, making the workforce more productive and strengthening manufacturing capability.

Artificial intelligence is already becoming a commodity tool that is permeating our lives. If you have used Google maps, Apple maps, or shopped on Amazon, then you have already been touched by AI.

Driven by Data

AI is essentially the simulation of human intelligence using computers. Machine learning (ML) is a subset of AI that is used to automatically learn from data without being explicitly programmed. The two key words here are simulation and data. While we’ve all heard about the great promise of AI, it is still a simulation of human intelligence. Right now, human intelligence is superior to artificial intelligence in many sectors, and certainly in manufacturing. Part of the reasoning here, is that AI has yet to be trained in the vast field of manufacturing. Such training requires data. One thing that we do know from manufacturing is that we have significant levels of automation, and that automation has serious amounts of sensors and sensing capabilities on board. So, while there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.

 Understanding the Opportunity

Many initial forays in using AI in manufacturing have been tried. One approach is to take all of the data coming from manufacturing operations and feed them into commercially available AI algorithms to find patterns linking various elements of the manufacturing process to the outcomes (e.g., quality, process health, and the tuning of operations). Many of these attempts are what one would call “black box”, in that they are basically just throwing the data at AI systems to see what happens. This approach has had some limited success. However, more successful approaches have used data with models that are well established but augmented by AI. Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.

For example, new generation design (CAD) tools employ AI to help design components based on variables such as the load requirements on a part, while minimizing its weight. The approach is generally called generative design, and it creates some very complex structures. One might think that such complex parts can easily be 3D printed – just hit the print button! – as we can now 3D print almost any geometry. However, a person with significant experience in 3D printing will tell you that this kind of print button does not exist and there remain numerous limitations on 3D printed parts. A part generated by an AI design alone may not, in fact, be printable. However, an expert who understands both the goal and the limitations can work with the AI system to generate a lightweight part that can effectively support the required load while still being printable. This is where artificial intelligence works together with real intelligence. The AI is augmenting the human designer, and the human designer is augmenting the AI in this instance. The key is that both are working together.

 Improving the Models

Today, we are just starting to take our data and augment our models to help us better understand our processes. Within a few years, we will have improved models that better utilize sensor data to help us predict the future health of our processes. For example, higher end machine tool manufacturers are starting to place more sensors, such as accelerometers, on their spindles. They train their AI algorithms to understand the sensor output when the spindle is healthy, and then have the algorithms use that knowledge to determine if the spindle has developed a problem. Furthermore, the algorithms can often determine a pending problem, and when it is going to happen. So now, a plant might shut a machine down for maintenance in advance of a breakdown. Thus, repair might happen during the weekend, when the machine is scheduled for maintenance, as opposed to waiting for it to break at an inopportune time such as in the middle of a shift.

“AI has yet to be trained in the vast field of manufacturing. Such training requires data. While there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.”

Such applications of AI will not be a silver bullet. The interpolation/extrapolation limitations of AI will still be in place. That is to say, there is a good chance that AI will not do well outside the scenarios that generated its original training data. So, if a completely new problem or situation is presented to AI, its response can be highly unpredictable. Therefore, we will have to understand its limits. For example, there is no algorithm that can accurately predict when a spindle will fail, until the spindle starts to fail. Statistics can be used to estimate failure (for example, based on experience the spindle may be expected to last so many hours of operation), but until the actual failure starts to occur, one cannot use AI, or any other techniques, to precisely predict that failure. For example, statistically speaking, the spindle might be expected to last for 10,000 hours of operation. However, if a severe crash occurs, that life expectancy might be substantially shortened. That being said, if crash data are fed into AI models, we might get a better feel for how much time was taken off the spindle life due to the severity of the crash, so there is another instance where we can augment our spindle life model.

Augmenting the Human Workforce

Just as with automation, people often express concerns about AI replacing humans in manufacturing jobs. Certainly, we will not see the Skynet AI scenario from the Terminator movie series that is in total control. What we will see is that the AI will augment the human workforce making it more valuable and effective. So once again, rather than artificial intelligence, we will see augmented intelligence. Just as with the earlier generative design example, we can use AI to help the human workforce, and the human workforce can be used to train AI.

Mixed Reality AI

One great technology advancement that is presently coming on-line and will be fully integrated into manufacturing operations by 2030, is mixed reality, which is a blending of the digital and physical worlds. For example, in a plant many workers may have to wear safety glasses, so why not make them augmented reality (AR) goggles? Such goggles could ensure that an operator follows the right procedures and adheres to all the required safety protocols. It could also ensure the correct insertion of all of parts into an assembly. Such an approach is the best of both the human and automation worlds.

Automated assembly makes sense in a highly controlled environment when thousands or tens of thousands of assemblies are being made and the components of the assembly are always in the same location and orientation so that automated units such as robots know exactly where and how to put up these parts. But if a part is backwards or falls over for some reason, the robot may have great difficulty picking it up. A human, however, is very adept at determining a part’s location and orientation and picking it up to put into an assembly correctly. Furthermore, AR goggles could ensure that the person picks up the right components and puts them into the right locations. An added benefit to this, is that a digital passport can then be generated for the assembly that records the correct assembly procedure documenting every process step ensuring the assembly’s quality.

“Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.”

Such a capability can also be used to help to train new workers in a facility and reduce their errors substantially, especially if the AR is used to train the AI by learning directly from the actions and decisions of experienced workers. This does not eliminate the human from the job, as a human is still necessary to physically execute the task. But AI could learn the most effective manner to execute a task and then guide a novice through a similar procedure using AR. All this can be done while building the product’s digital passport, conducting time and motion studies, and ensuring that all safety protocols are followed. It is truly an approach that augments and adds value to the workforce.

Privacy Implications

If we are recording every move and motion of the workforce, this does raise some privacy concerns which will need to be addressed in the years ahead. We will need to understand what we are learning and recording from the workforce and ensure that their data are protected accordingly. This is not unlike Google maps. We share our location with Google, and in turn, Google provides us with directions to where we want to go. Of course one major concern here is that Google might use our location data to track us and provide that information to third parties. The same is true in manufacturing. How do we ensure privacy among workers and businesses? This does open up a can of worms as, right now, different geographic regions (e.g., EU, U.S., China) have different regulations regarding personal data and its privacy. So, we will certainly have to address these issues which could become increasingly complex for multinational companies over the next few years.

Furthermore, the ability to share production data from one production facility to other facilities, or from one machine tool to another, is going to become important as lessons learned in one location could be equally as valuable to others at different locations. This highlights the value of data, but it begs the question, why would factory owners open their cyber doors to others and share their data? Clearly, new business and privacy models and rules must be developed to make it both worthwhile and safe for companies, and industries, to share some of their data.

Looking Ahead

So, I believe that by 2030 we are going to see AI capabilities permeate throughout manufacturing. We are also going to see it really take off in mixed reality settings that will increasingly look like something out of a science fiction movie. This will result in a safer, more efficient, and higher value-added manufacturing ecosystem. But it will also result in some very different business and socio-economic models that will change the way manufactures operate, think, and profit in the years ahead.  M

About the author:
Professor Thomas R. Kurfess is Executive Director at the Georgia Tech Manufacturing Institute, Professor and HUSCO/Ramirez Distinguished Chair in Fluid Power and Motion Control at the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, and a member of the MLC’s Board of Governors.