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The key to dealing with disruption is agility. The key to agility is having a strategic approach to harnessing data flows in real time using today’s digital tools.

TAKEAWAYS:
● Analytics must be as adaptable and agile as our businesses need to be.
● First define the needed outcomes — then map the data needed to get those outcomes.
● Digital tools, including AI, are essential to creating an agile Industry 4.0 operation.

Agility is essential for manufacturers to deliver a solution that suits consumers and brands. Indeed, Industry 4.0 describes an agile manufacturing ecosystem that works with a lot size of one, a digital thread that defines the entire supply chain and manufacturing process.

The numerous disruptions of the last five years (trade war, pandemic, components shortages, geopolitics, etc.) have proven just how important it is that the industry achieve this agility. And while many companies, particularly those in Electronic Manufacturing Services ( EMS ), have talked a good game, few have built agility into their business or operational strategy.

In fact, the opposite could be true. Over decades, the EMS industry sought to drive economies through scale and an increasing dependence on low-cost labor, all while moving inventory out of the supply chain. This has created rigid manufacturing footprints that thrive on little change and have little ability to adapt at speed. These just-in-time supply chains have proved brittle, not allowing for the just-in-case scenarios we’ve seen recently.

“Undoubtedly, AI has a massive role to play in our future, whether doing our children’s homework or figuring out how to optimize a factory — or even an entire manufacturing ecosystem.”

We’ve also had a decade of talking the talk around Industry 4.0, while very few are walking the walk! In short, few have seen a measurable digital dividend as yet. We need to use digital tools to create a more agile work environment that is less dependent on labor and hence more efficient, adaptable, and reliable.

What has that got to do with operational analytics? A lot, it turns out. The role of operational analytics is to gain intelligence that drives insights, leading to better and faster decisions. And that eventually drives better outcomes, which means better quality and reliability, greater efficiency and profitability, and a more robust and agile operational model. The process is simple data-insight-value.

Data Everywhere

One thing we have done well in the first decade of the fourth industrial revolution is to figure out how to connect machines and harvest the massive amounts of data available — now some might say we now have too much data.

If our process starts with data, it ends with value:

Data (contextualized) produces intelligence
Intelligence drives insights
Insight drives value through better decision making

An example might be the closed loop between an SPI (Solder Paste Inspection) machine and a solder paste printer on a typical SMT line. In the past, many errors occurred because of the print quality of the solder paste printer. These would result in poor quality after reflow, low first-pass yield, and inefficiencies. Introducing SPI to the process allowed us to stop the line if there was an issue, but it only acted as a stop signal.

By creating a closed loop, the SPI can use images of the board with the printed solder paste (data) to determine if the right amount of solder paste is present in the right place (insight). It then uses that insight to decide how much to adjust the printer (speed, alignment, and pressure) to get the best result. Hence reducing waste and increasing yield and reliability to drive incremental value.

“As a rule of thumb, any data being transmitted should be encrypted. Part of any data management security system is the management of access.”

In this case, we know the outcome or value we are trying to achieve and can work our way back to understand the dataset we need to gain insight and make a good decision promptly. We could get numerous parameters from the SMT line; some deliver value, and some may not. Hence the first phase of any operational analytics strategy is to understand what data we need and how to use it to deliver value.

The quality of the data is also critical in this example. Not all SPI systems are created equal, and if the image is not accurate or accurately processed, errors can slip through, and false calls can slow the entire process.

Analyzing Data With AI Just Got Easier

Like Industry 4.0, we’ve been talking up artificial intelligence (AI) for some time. Right now, everyone’s being dazzled, and occasionally disappointed, by the skills of OpenAI’s ChatGPT and other chatbots from Google and Microsoft. Undoubtedly, AI has a massive role to play in our future, whether doing our children’s homework or figuring out how to optimize a factory — or even an entire manufacturing ecosystem.

What these AI systems show us, often vividly, is the importance of the learning derived from datasets. If you use unreliable data, you’ll get unpredictable insights, driving flawed decisions.

Let’s return to the example of the inspection system used to adjust the line. AI could be used to manage the enormous amount of data being derived from the system, but we need to be especially careful in what datasets we use to train that AI. Hence, we need to ensure those developing these systems have the domain knowledge associated with the manufacturing system and the deep domain knowledge required to understand what is good data and what is not. We must also ensure we use the best possible inspection solution with the highest definition and most accurate image.

“The ability to drill down into each data field can be extremely valuable in helping you to understand an issue’s root cause and find the right solution.”

There is no doubt that AI will be a game-changer for the use of data, particularly on the factory floor. Like many, we are working hard in this area. The factory floor can give us hundreds of signals at any moment. AI will help us process, prioritize, and manage those signals to generate better insight, outcomes, and value. We are on a fast ramp in the performance of AI and its application, but we will need to be careful about how we train our AI systems and how we monitor and manage their performance.

Data Management Best Practices

It’s worth thinking about the best practices for the management of data. With the volume of data generated in a factory, it is easy to see how data volumes can quickly become unwieldy and expensive in terms of storage. Here are a few best practices and things to consider in terms of data management:

Backup and recovery — it is essential to have a regular backup plan and backups in multiple locations to ensure that if and when a data breach or failure occurs, recovery can happen quickly and seamlessly.
Data locations — consider if you plan to store data on premise, in the cloud, or perhaps both. Within this decision will be considerations around data security and access. Multiple locations should add protection against loss but will also add cost.
Security and access — encryption is vital as much of the data stored may be confidential or include your or your customer’s IP. The same is true concerning access. Ensure that access is restricted to those needing the data, and that they have the appropriate security clearance. As a rule of thumb, any data being transmitted should be encrypted. Part of any data management security system is the management of access.
Data management — using the correct tools and systems can help optimize the data storage needed while creating more efficient workflows.
Compliance and regulation — over the last decade, various laws and regulations have been implemented to protect privacy and ensure data is properly collected, stored, and shared. Ensure you are current and compliant with the rules and legislation of the regions where you operate and store or transfer data.
Stay up to date — regular audits of your data process should allow you to stay on top of what is happening regarding technology and regulation, especially that which is specific to your data. It’s essential to know what data is being accessed and used and what is accumulating without providing insight.

Whose Data Is It Anyway?

Now that we can use big data, we must consider in whose hands the power should be and who needs which data. In a typical brand/EMS relationship, the data required by the brand will differ from what the contract manufacturer needs. Typically, product data affecting traceability, reliability, recalls, and supply chain transparency is necessary for the brand. On the other hand, data concerning manufacturing performance is more normally leveraged by the manufacturing company. But sometimes, these lines are blurred.

Hence, sharing data in an open and safe environment is important. Trusted data must be available to drive custom dashboards, reports, and notifications for every stakeholder. In some cases, data access must be gated so only those needing sensitive information can view it. Lastly, the ability to drill down into each data field can be extremely valuable in helping you to understand an issue’s root cause and find the right solution.

“If we can design and plan the outcomes we need, such as a more efficient and sustainable supply chain and manufacturing ecosystem, we can map them to the data we need to collect to drive those outcomes.”

Five years ago, everyone was talking about the glass factory concept, where customers and the operational team could see exactly what was happening on each line and for each product. Now, thanks to recent component shortages and supply chain disruptions, people are more excited about the idea of a glass pipeline, which provides real-time transparency into each part of the supply chain.

Focus on the End Game

The bottom line is that data needs to serve the business’s strategy rather than the other way around! If we can design and plan the outcomes we need, such as a more efficient and sustainable supply chain and manufacturing ecosystem, we can map them to the data we need to collect to drive those outcomes.

And if we take a more open-minded approach in that design, we can create analytics that are as adaptable and agile as our businesses need to be. M

About the author:
Adam Montoya is VP of Industrial Solutions at Bright Machines.

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.”

Accelerating the use of AI can result in substantial benefits, but manufacturers must proceed with a practical understanding of the best applications.

TAKEAWAYS:
● The main challenges to increased AI adoption and deployment largely revolve around data and the need to capture, organize, and analyze growing volumes in a timely manner.
● Organizationally, manufacturing companies should stand up an internal team with expertise in AI, data science, and data engineering to handle all AI-related activities and investments.
● AI’s single biggest impact in manufacturing could be helping companies address the future workforce gap.

Artificial Intelligence adoption and deployment seem to be less extensive and mature in industrial manufacturing than in most other industries. So far, there have been fewer big AI success stories in manufacturing and thus less competitive pressure to take immediate action. Although most manufacturing companies generally acknowledge the importance of AI—and see it is an essential and disruptive capability that could greatly affect their ability to operate and compete in the future—most efforts to date have been limited to small-scale pilots and proofs-of-concept projects focused on narrow parts of the business.

The main challenges to increased AI adoption and deployment largely revolve around data. Unlike many other industries where digital data plays a central role, manufacturing still revolves around physical work and physical assets, with many of those assets geographically scattered and disconnected from digital networks. Widespread deployment of IoT-related technologies is starting to fill this data void. However, in order to be useful, the resulting data needs to be organized, captured, and analyzed in a timely manner. Also, edge computing and edge AI technologies should be harnessed to enable timely processing and analysis of data in dispersed locations at the edge of the network.

“The main challenge to increased AI adoption revolves around data.”

For most manufacturing companies, the immediate and important next step is to establish an internal team with expertise in AI, data science, and data engineering to serve as a focal point for all AI-related activities and investments. This team would coordinate AI activities across the company’s business ecosystem, while providing a core set of internal AI resources and capabilities that can be supplemented from the outside as needed. Also, the team would provide a broad, balanced, and informed perspective on using AI across the enterprise.¹

Emerging AI Use Cases

Using sensor data and AI to create and analyze digital models of real-world machines and factories can enable operations to be improved without disrupting production. Trying to optimize a manufacturing operation without disrupting production can be like trying to change the tires on a race car while it’s zooming around the track at 200 miles per hour. The solution? An AI-enabled digital twin.

A digital twin is a virtual representation of a physical device or system that mirrors its exact elements and behavior in real time. Sensor data from numerous sources—along with historical data—is combined with machine learning and advanced analytics to create digital models and spatial graphs that constantly match the status, position, and working condition of their physical counterparts. These exact digital simulations enable a company to conduct extensive analysis and optimization experiments without disrupting day-to-day operations. It’s a virtual process that can deliver real-world benefits.¹ Figure 1 shows examples of digital twins in manufacturing settings.

Figure 1: Facility and Production Digital twins must be enabled to replicate real-time behavior, run analytics, and optimize manufacturing processes and throughput.

In today’s highly competitive world, companies may rush to manufacture goods on a large scale to leverage economies of scale, while still trying to offer customers the ability to customize the products to meet their individual needs and preferences. AI could help organizations minimize manufacturing costs and offer a wider variety of desirable products to customers. AI can be leveraged in real-time to help achieve the following ²:

Predictive and Prescriptive Maintenance: AI predictive models may be able to predict potential equipment failure points, allowing maintenance to be performed before the actual failure occurs. This could minimize equipment downtime, improve overall equipment efficiency (OEE), and increase productivity.
Safer Operating Conditions: AI-enabled robots could replace humans in hazardous working conditions, potentially reducing the risk of casualties in the workplace. AI predictive models could also alert of potential failures in advance, helping to avoid any mishaps and improve working conditions for workers.
Better Quality Control: AI algorithms may allow organizations to monitor production processes, conduct continuous quality inspections, and detect deviations from desired outputs, which could allow for the identification and correction of defects in real-time. AI algorithms could increase consistency and improve quality assurance.
Supply Chain Optimization: AI algorithms could be leveraged to monitor the use of raw materials from procurement to the delivery of the final product, possibly allowing organizations to optimize material flow, reduce material waste, and minimize inventory costs in real-time.
Material Procurement Optimization: AI algorithms could forecast the cost of raw materials based on historical data analysis and current market trends, potentially helping organizations cope with fluctuating material prices, procure raw materials at optimal prices, and build inventory based on macroeconomic forecasts.
Production Planning Optimization: AI could optimize production processes through real-time process monitoring, identifying potential bottlenecks in advance, and optimizing routings and resource allocation. This may increase productivity, reduce production costs, and improve delivery timelines.
Process Optimization through Augmented (Virtual) Reality: AI algorithms could simulate different manufacturing scenarios, introducing unknown events and predicting the outcomes of production processes and equipment behavior, thereby helping to improve the production processes. Furthermore, augmented reality could help workers access real-time product data and assembly instructions, possibly reducing defects.
Optimal Workforce Utilization: AI algorithms could optimize workforce/staffing, manufacturing shifts schedules, and workforce training programs, potentially improving employee satisfaction, and reducing labor costs.
Improved Product Development: AI-enabled sensors could monitor production yield in real-time and provide closed-loop feedback to the R&D team to help improve product design for increased production yield and minimum production defects, likely reducing the cost of manufacturing.
Enabled Proximity Search: AI could translate natural language processing (NLP) into search parameters to search for appropriate assembly parts in proximity, possibly reducing time to assemble complex products.
Dynamic Production Line Testing: AI algorithms could perform unit and system integration testing of software-hardware integration as the product progresses through the assembly line. This may help organizations detect and rectify defects early in the production, rather than waiting for end-of-line (EOL) testing.

Figure 2: Best in class manufacturers are utilizing AI based analytics to drive benefits across asset efficiency, quality, cost, and safety.

Figure 3: Organizations must prioritize the AI use cases (mentioned above) to meet their business requirements and plan for implementation to maximize AI benefits in manufacturing.

Take a Practical Approach

Too many AI initiatives in manufacturing are either overly tactical and technical (too narrowly focused, and often highlighting technical capabilities that are exciting but not very useful), or overly strategic and ambitious (too difficult and expensive to implement, requiring data and advanced capabilities that don’t currently exist). To succeed with AI, manufacturing companies should have strategies and roadmaps based on a practical understanding of what parts of the business are best suited for AI.

One early and ongoing focus area for AI in manufacturing is making machine maintenance more predictive and less reactive. Another key focus area that is getting a lot of traction these days is using AI to improve interactions with customers and field workers. Also, some manufacturing companies are starting to explore the use of AI to help them handle extreme weather and other hard-to-predict events. By harnessing the power of AI vision and other advanced AI technologies, companies can monitor and analyze vast amounts of information— including data from field sensors, drone video, and weather radar—with a level of timeliness, accuracy, and thoroughness that humans alone simply cannot achieve.

A key focus area for AI is making machine maintenance more predictive.

Expanding on the idea of machines helping humans be more efficient and effective, AI’s single biggest impact in manufacturing could be helping companies address the future workforce gap.

The Biden administration’s multi trillion-dollar commitment to infrastructure is expected to dramatically increase business activity throughout manufacturing, but could also create a significant shortage of workers and expertise. AI can help address this gap by augmenting the work done by humans—doing much of the preparatory analysis and heavy lifting so human workers can focus on activities that require skills and expertise that are uniquely human. M

Footnotes
1. Source: The Energy, Resources & Industrials AI Dossier | By Deloitte AI Institute
2. Source: Deloitte Analysis

This article contains general information only, does not constitute professional advice or services, and should not be used as a basis for any decision or action that may affect your business. The authors shall not be responsible for any loss sustained by any person who relies on this article.

About the authors:

Stavros Stefanis is Principal, Product Engineering & Development at Deloitte. Stefanis is a leader in Deloitte’s Product Engineering & Development market offering with a focus on hardware and software development transformation using digitally integrated model-based capabilities.

Mohit Kapoor is a Manager at Deloitte Consulting LLP. Kapoor has 18 years of experience in Product Strategy & Lifecycle Management with a focus on defining end-to-end global product development processes, system design and implementation, PLM integration with ERP, strategic planning and execution, and enterprise digital transformation.

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.

Introducing the latest new members to the Manufacturing Leadership Council.

Patrice Baumann

Senior Vice President, Global Operations
EnerSys

www.enersys.com

https://www.linkedin.com/in/patrice-baumann-b8b02aa0/

Rob Bilger

Executive Director, Operational Technology
MSA Safety

www.msasafety.com

https://www.linkedin.com/in/robert-bilger-8a544018/

Roger Brecht

Vice President, Digital Manufacturing
Nestlé Purina

www.purina.com

https://www.linkedin.com/in/roger-brecht-2b93245/

Sven Neusken

Director, Global Operations Development
Emerson Automation Solutions

www.emerson.com

www.linkedin.com/in/sven-nuesken-p-e-5662042/

Steve Sukup

President
Sukup Manufacturing

www.sukup.com

https://www.linkedin.com/in/steve-sukup-638a1113/

MLC Members can view the full MLC Member Directory here.

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.

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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.

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