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The Manufacturing Leadership Council’s 2024 Future of Manufacturing Project event drew forward-thinking manufacturing leaders to Orlando, Fla., for a deep dive into AI-centered factories of the future. The Future of Manufacturing Project is an on-going initiative focused on key trends manufacturers must understand today to help them realize a better future.

David R. Brousell, MLC’s Founder, Vice President and Executive Director, opened the event with a sweeping overview of AI’s transformative role in the manufacturing sector and beyond. He began by highlighting the explosive growth of generative AI tools like ChatGPT, which became a global phenomenon almost overnight. Brousell explained how this rapid adoption of AI has served as a critical inflection point, fueling innovation, competition and investment at unprecedented levels across industries and nations. He underscored the staggering economic potential of AI, citing projections that it could contribute nearly $20 trillion to the global economy by 2030.

“A worldwide competition for dominance in AI is underway — and it appears to be intensifying,” Brousell said.

Major investments and initiatives by the U.S., China and Saudi Arabia highlight the AI race. He illustrated how these efforts reflect the high stakes of AI’s development, with nations and corporations alike vying for leadership. However, he cautioned against overhyped expectations, acknowledging the historical challenges of AI adoption and the – unlikely as it may seem – potential for a third AI winter. To navigate these uncertainties, he emphasized the importance of understanding both the opportunities and the risks that AI presents.

Drawing from MLC research and recent industry studies, the opening address included key findings on how manufacturers perceive and are implementing AI. While most manufacturers recognize AI as a game-changing technology, only a small percentage rate their current AI maturity as advanced. Despite this, many companies are ramping up their investments and seeing early signs of positive returns, particularly in areas like operational efficiency and productivity. Brousell also identified significant challenges, such as data quality, skill gaps and the need for metrics to measure AI’s impact.

To guide manufacturers in their AI journeys, Brousell offered practical recommendations: think big and envision bold possibilities, stay informed on technological advancements, collaborate with industry communities, establish ROI metrics and prioritize a human-centered approach to AI adoption.

He closed by reflecting on the manufacturing sector’s historic role as a driver of societal progress, calling on leaders to define their partnership with AI responsibly. His message was clear: the AI train is leaving the station, and manufacturers must be ready to board.

Access full text and a video of David R. Brousell’s opening address.

The Future of Manufacturing Project has become the premier platform for exploring the transformative forces shaping the manufacturing industry. At the 2024 event, David R. Brousell, MLC’s Founder, Vice President and Executive Director, delivered a powerful opening address that set the tone for the discussions and presentations to come. In his speech, Brousell examined the meteoric rise of AI technologies like ChatGPT, the growing global competition for AI leadership, and the profound implications for manufacturers. He offered data-driven insights, candid reflections on challenges, and practical guidance for navigating the AI-driven future. Below is a recording and the full text of his address — an essential read for anyone seeking to understand the opportunities and responsibilities that lie ahead for manufacturing and beyond.

Good morning, everyone, and welcome to the fourth annual Future of Manufacturing Project event.

Two years ago in Nashville, when MLC first devoted this event to examine the potential as well as challenges with artificial intelligence in manufacturing operations, OpenAI had just released the generative AI tool ChatGPT.

I had mentioned OpenAI in my talk that day, but little did we know what would soon be coming. ChatGPT took off like a rocket ship, capturing the imagination of the public, businesses, and governments, raising both aspirations as well as fears about the technology.

ChatGPT reached 100 million users in just two months after its launch, becoming the fastest growing consumer application in history at that time. Today, ChatGPT has a reported 200 million users.

ChatGPT became an inflection point for AI in general. The energy and momentum around AI in its many forms – IBM alone has identified seven types of AI — have not only continued but appear to be picking up even more steam. In the generative AI market alone, a flood of competitors has joined ChatGPT including Midjourney, Stable Diffusion, Bard, and, of course, Copilot, and others.

The amount of literature being produced about AI – and I use the term “literature” with an asterisk attached – is almost impossible to keep up with. Every day, it seems, there are multiple newspaper and magazine articles, research reports, case studies, government reports, product and service announcements, and conferences extolling both the virtues and warning of the challenges with the technology.

Huge sums of money are flowing into the AI market as nations earmark funds, announce national strategies, and set up organizations to capitalize on the technology; as technology and service providers produce new AI and augmented products and offer implementation methods; and as businesses assess the effect of AI on their competitive posture and develop their own goals and objectives with the technology.

As I said in Nashville two years ago, a worldwide competition for dominance in AI is underway – and it appears to be intensifying.

Here are a few datapoints to consider:

• In a report published in September, International Data Corp. predicted that AI would have a cumulative global economic impact of $19.9 trillion through 2030, representing 3.5% of global GDP by that year. Every new dollar spent on business-related AI solutions and services will generate, IDC estimated, $4.60 into the global economy, in terms of indirect and induced effects.
• Meanwhile, spending on AI is growing at double-digit rates. IDC estimated that worldwide spending on AI, including AI-enabled applications, infrastructure, and related IT and business services, will grow at a 29% compound annual growth rate between this year and 2028, reaching $632 billion in 2028. In terms of use cases, Smart Factory floor investments are expected to grow at a 32.5% CAGR in that time period.
• The number of AI patents granted worldwide rose 62.7% between 2021 and 2023, according to the 2024 Stanford AI Index Report. In 2022, China led global AI patent origins with a 61.1% share, significantly outpacing the U.S., which accounted for 20.9% of AI patent origins. In that same year, however, the U.S. far exceeded China in terms of financial investments. In the U.S., AI investments reached $67.2 billion in 2022, 8.7 times more than China.
• Announcing AI strategies has become commonplace among nation states. The U.S. has its National AI Initiative Act, a Presidential Executive Order on AI, and is in the process of standing up an AI Institute under the America Makes program.
• In China, well, we know there is much activity but getting reliable data is a challenge. For example, International Data Corp. estimates that China’s investment in AI will reach $38 billion in 2027. Meanwhile, a report in Yahoo Finance in September said that China’s AI industry could invest $1.4 trillion into developing the technology in the next six years. Pass the salt!
• On the other side of the world, the European Union has its AI Innovation Strategy, which calls for the creation of AI Factories across the European Union. Back in Asia, India has its AI Mission initiative; Japan’s Ministry of Economy, Trade and Industry has allocated $740 million to subsidize the AI computing industry in Japan; and South Korea has a plan to spend $6.9 billion by 2027 on artificial neural processing units and high bandwidth memory chips.

But one of the more notable announcements this year came from Saudi Arabia, which created a $100 billion fund to invest in AI and other technologies.

***

Now, cautious observers, particularly those with a historical context, might say that all the posturing and the financial projections and investment numbers merely reflect an inflated state of expectations, a euphoria born of excitement about the shiny new car. We are in what Gartner calls a hype cycle that will, in time, deflate as the challenges of implementing and optimizing use of the technology become better understood and experienced.

No doubt there is some truth in this. As we know from the experiences we’ve had with other technologies, strategic, organizational, cultural, and human behavioral challenges govern whether a technology project will ultimately be successful. I myself have talked for many years about what I call The Absorption Gap – the time and effort required to go from implementation to benefit. It often takes much longer to get to benefit than we think at the outset.

Could history repeat itself with AI? Could we yet experience a third AI winter, after the two that occurred last century when expectations were disappointed?

Anything is possible, of course, but I wouldn’t bet on it. The foundational conditions that were lacking years ago to enable AI to take off – compute power, storage, communications bandwidth, sufficiently large data sets – have all been addressed. Moore’s Law will ensure that price/performance will improve, perhaps significantly, over time. And the unrelenting need to find new competitive advantage will compel companies to go forward into the known and, perhaps more importantly, the unknown.

My friends, the AI train is leaving the station – and you must be on it.

***

The encouraging news is that aspirations with AI among manufacturers are strong and there is already some evidence that AI is, as they say, worth the squeeze.

Recent MLC research shines a light on what manufacturers are thinking about the use of AI in their operations.

Among the many findings of our new AI study, results of which were published in the Manufacturing Leadership Journal in August, perhaps the most important is that a majority of manufacturers, 55%, see AI as a “game-changer” in the industry by 2030, even though only 5% at this point in time assess their maturity level with AI in operations as “advanced”.

Perhaps propelled by this perception, 78% said they plan to increase spending overall on AI tools over the next 12 to 24 months, with

one-fifth expecting to increase their spending by more than 30%. Already, more than half are using GenAI tools such as ChatGPT or Microsoft’s Copilot, and more than 80% said they expect to increase the use of such tools in the next two years.

As manufacturers ramp up their AI investments, early indications about the impact and return on these investments are looking positive.

In July, EY, a member of the Manufacturing Leadership Council and a partner in the Future of Manufacturing Project, published the results of its first AI Pulse survey, which asked 500 senior leaders across industries in the U.S. about AI investments, impacts, and challenges.

The study found that senior leaders whose organizations are investing in AI have seen tangible results across business functions, including about three-quarters who are already experiencing positive ROI in operational efficiencies, employee productivity, and customer satisfaction.

Moreover, the EY study found that organizations that are committing 5% or more of their budgets on AI are seeing “outsized returns.”

And all of this is happening as AI continues to advance. This year’s Stanford report assessed that AI has now surpassed human performance on several benchmarks, including in image classification, visual reasoning, and English understanding. However, the technology still lags human capabilities in competition-level mathematics, visual common-sense reasoning, and planning, Stanford said.

I suppose that we can all take some comfort in those findings. We humans will continue to dominate – at least for the moment. We don’t know how fast AI will evolve and how capable and powerful it will become in the years ahead, but we know one thing for sure – with the amount of capital flowing into the AI market and the amount of effort being applied to its development, AI will not sit still.

As it continues to move, manufacturers have a host of issues to deal with to ensure that AI is used effectively to advance their organizations’ business and operational goals, and in a responsible manner.

Once again, MLC research spotlights some of the challenges.

The top concern, say 68%, is around data – quality, contextualization, and validation. More than 40% also identify AI skills and AI business case development as issues. Sixty-six percent say they do not have a specific set of metrics to measure the effectiveness and impact of AI implementations at this time.

This all goes back to that single digit finding I mentioned earlier about maturity levels with AI. The question is: what will it take to accelerate adoption and use of AI in operations and enable companies to climb up the maturity curve?

The easy answer is time, money, and persistence. But there’s a bit more to it than these three foundational elements. Please allow me to offer a few recommendations:

First, I can’t emphasize strongly enough about having a vision for what you want to do with AI. That may sound obvious, and it is on one level, but it takes a lot of thinking, strategizing and even soul-searching to craft an idea of what your company could be in 10 or 15 or 20 years.

So, think big about AI and what your company might be able to do with it. Think about the art of the possible. Think about growth and not just in what you are doing now but what you could be doing that’s new, innovative, and exciting.

At first, you will turn your attention to using AI to become more efficient, more cost-effective, faster. These are all important objectives, but don’t stop there. Imagine a better future.

And once you craft a business vision for the future – a vision that should be flexible enough to change when needed – make sure your AI strategy “functions as the fuel,” as Council member Deloitte says, for the business strategy.

Second, keep up with the evolution of the technology. There is a lot of energy and momentum around AI, as I have detailed, and new developments are occurring frequently. The technology must be monitored and understood, as well as the rules, laws, and regulations that will govern its use. Designate someone or a specific business unit in your company to keep track of developments to understand their potential to help your business.

Third, don’t try to go it alone. Plug into the community. MLC is here to bring together your fellow manufacturers, technology and services providers, academics and research organizations, and sister groups such as the IRI, CESMII, and MxD so you have access to a robust community for help.

Fourth, work on the metrics. Experimentation with AI is necessary and important, but the laws of business must be served. Find, adopt, and use ROI models to prove return so that investment continues.

Fifth, it is essential to take a human-centered approach to AI. Bring people along by showing them the benefits and being candid with them about the challenges. Change management and training are key.

Taken together, these simple, common-sense tenets can help you navigate your journey with AI.

***

What we do here today, and what you do after this conference when you go back to your companies, will help propel the manufacturing industry forward.

But our responsibility is greater than just our own industry. AI has not only the potential to reshape manufacturing but society itself. As it has many times throughout history, this industry is playing a vital role in deciding what the future might look like. Once again, the mantle of change lies at our feet.

I would like to close with a quote from a book called “The Age of AI And Our Human Future,” authored by Henry Kissinger, Eric Schmidt and Daniel Huttenlocher:

“The advent of AI, with its capacity to learn and process information in ways that human reason alone cannot, may yield progress on questions that have proven beyond our capacity to answer. But success will produce new questions … Human intelligence and artificial intelligence are meeting, being applied to pursuits on national, continental, and even global scales.

“Understanding this transition, and developing a guiding ethic for it, will require commitment and insight from many elements of society: scientists and strategists, statesmen and philosophers, clerics and CEOs. This commitment must be made within nations and among them. Now is the time to define both our partnership with AI and the reality that will result.”  

Thank you and have a great conference.

About the author:
David R. Brousell is the Founder, Vice President and Executive Director, Manufacturing Leadership Council

Photos by David Bohrer / National Assoc. of Manufacturers

The 20th anniversary of Rethink brought together thought leaders and manufacturers to share digital transformation best practices and lessons learned. 

MLC members celebrated Rethink’s 20th anniversary by doing what they’ve done at the past 19 summits: learning about the technology, leadership, and organizations driving Manufacturing 4.0 forward. Over the course of three days, more than 60 speakers shared their insights during keynotes, case studies, panel discussions, (Re)Think Tanks, fireside chats, lightning rounds, and executive interviews.

Here are a few of the exceptional sessions Rethink participants experienced.

Access to the MLC Member Portal is required to view the videos.

Opening Address: What’s Next?

David R. Brousell, Founder, Vice President and Executive Director, Manufacturing Leadership Council

Over the past 20 years, the manufacturing industry has worked hard to understand and adopt the digital model of production, learning much along the way. But as new technologies emerge, the proverbial finish line continues to move, leading to new possibilities, aspirations, and opportunities as well as challenges. MLC’s founder shares his views on what manufacturing might look like by mid-century.

Watch Now

Case Study: 25 Years of Transformation—Triumphs and Pitfalls from a Manufacturing Digital Native

Robert Bodor, CEO and President, Protolabs, Winner of the 2022 ML Awards Small/Medium Enterprise Manufacturer of the Year

Protolabs began as a company aimed at filling a specific manufacturing need: injection molded parts really fast. As one of manufacturing’s original “digital natives,” that speed was a result of automation and digitizing a once-traditional process. Since those early days, the company has expanded its offerings and experienced significant growth. The ongoing digital transformation that followed over the next 25 years as a custom components manufacturer has provided a case study in digital evolution comprised of technology advancements, key acquisitions, difficult trade-offs, and even a few hard lessons learned along the way. Learn more about the company’s ongoing Manufacturing 4.0 journey that has driven its mission to serve customers on the forefront of innovation.

Watch Now

Panel Discussion: Next-Generation Leaders

Angela Accurso, Director of Workforce Programs, MxD
Marlon Alberto Gonzalez Martinez, Storage Order Management Fulfillment Team Leader, IBM
Megan McCarthy, Business Process Manager for Global Manufacturing Electrical, General Motors
Jonathan Miller, Automation and OpEx Manager, Saint-Gobain Life Sciences
Facilitator: Penelope Brown, Senior Content Director, Manufacturing Leadership Council

What’s on the minds of the next generation as they develop into tomorrow’s leaders? This panel discussion examines the hopes, aspirations, and visions for the future from some of 2024’s top finalists for the Manufacturing Leadership Awards Next-Generation Leadership award.

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Case Study: Hardwiring Innovation Processes from Tech Into a 155-year-old Company

Craig Slavtcheff, Executive Vice President and Chief R&D and Innovation Officer, Campbell Soup Company

The pace and scope of innovation demanded in food by consumers continues to be high, driven by a range of macrotrends including food exploration and nostalgia, ag-tech, and sustainability. To keep pace, food companies have to innovate on how they innovate, focused on the three drivers of “speed to insight,” “speed to design,” and “speed to execution”. Campbell has looked to the world of tech, including machine learning and AI, as inspiration to transform its innovation processes, building on its 150+ year old history of inventing delicious, nutrition, safe, and affordable foods.

Watch Now

Case Study: Making the World a Better Home through M4.0

Regan Gallo, Director of Operations, Coated Abrasives NA, Saint-Gobain North America

With more than 145 manufacturing locations across North America alone and several acquisitions in the past few years, Saint-Gobain faces steep challenges when it comes to deploying a unified digital strategy across the organization. With takeaways applicable to manufacturing companies of any size, learn where the company has found success, where it needed to make pivots, and the progress it has made as it works to meet its mission – making the world a better home.

Watch Now

About the author:

Jeff Puma is Content Director for the Manufacturing Leadership Council

GenAI is a necessary tool for competitive advantage, but companies will need to navigate the risks and capitalize on the opportunities—or risk being left behind. 

TAKEAWAYS:
● Most supply chain and operations executives (73 percent) are planning to deploy GenAI, yet only 62 percent have reassessed projects, and merely 7 percent have completed implementation.
● Organizations further ahead in the autonomous supply chain journey are 5.2 times more likely to see success with GenAI, further widening the digital gap.
● The greatest gains from GenAI come when projects align to a strategic vision, data are AI-ready, and value is maximized by addressing cyber and data risks.  

Since 2020, the global economy has been in a new paradigm, marked by disruptions and changes that are more forceful, appear more frequently, trigger more interconnected and widespread impacts, and often strike immediately. Chief operating officers and supply chain managers know this all too well—the supply chains they oversee exist on the frontier of this high-risk climate, bearing the brunt of its disruption.

In response, many companies refocused on boosting resilience in supply chains, which involved diversifying operations across multiple countries and suppliers. Supply chains that had once been ultra-lean gained protection against external shocks—but often at the price of reduced efficiency.

Enter generative artificial intelligence (GenAI), which can help companies leapfrog their technical maturity and accelerate their path toward an autonomous supply chain. GenAI is being used to not only analyze and interpret vast amounts of data but also to create new scenarios, generate innovative solutions, and remove frictions in real-time. As a result, managers have end-to-end visibility and more human time for higher-order work.

“Companies see GenAI as a critical capability for remaining competitive in the future and are investing in it accordingly.”

Unlike traditional AI, which primarily focuses on data-driven insights and automation, GenAI can design new processes, forecast future demands with greater accuracy, and seamlessly identify the most cost-efficient routes and carriers in the event of a disruption.

But the combination of GenAI and traditional AI is a game changer in bridging the gap to self-driving supply chains, because of GenAI’s breakthrough capabilities and the ways in which the strengths of the two technologies complement each other. It is a compelling vision, but one that has so far remained elusive.

EY global research of 460 supply chain and operations executives has found that even among organizations that have started preparing for GenAI in their supply chain, only 28 percent have achieved a low-human-touch supply chain, and only 50 percent have achieved end-to-end visibility across the supply chain.

The Great AI Reset

Companies see GenAI as a critical capability for remaining competitive in the future and are investing in it accordingly. Three-quarters (75 percent) are planning to deploy GenAI in their supply chains, and 80 percent believe it can reinvent supply chains. In addition, 69 percent believe that failing to integrate GenAI into their supply chains will put them at a competitive disadvantage.

Despite this optimism, a retrenchment is taking place. In the past 12 months, 62 percent of respondents have reassessed their GenAI supply chain initiatives, and only 7 percent have gone on to complete deployment.

Why?

Two reasons:

1. Concern and lack of understanding around the unique risks created by GenAI; and
2. Challenges of implementing this complex technology.

Our findings suggest this reset is about achieving scale and maximizing impact. In-depth interviews with seven supply chain and operations executives revealed that it was tougher than expected to make the technical leap from proof-of-concept to GenAI at scale.

Leading the Way to Autonomous Supply Chains

Organizations further ahead on the journey to autonomous supply chains (front-runners) have created strong digital foundations that enable them to adopt and take advantage of GenAI quickly. This momentum is likely to widen the digital divide, unless those who are behind (followers) take prompt action.

GenAI is giving organizations a faster pathway to the autonomous supply chain. Front-runners are more ambitious about using GenAI in the next two years, anticipating deploying GenAI in 12 use cases for the supply chain on median compared to eight for followers. Front-runners are going beyond the supply chain to align more closely with other business functions and external parties and driving end-to-end visibility across the supply chain.

Most organizations’ supply chains (82 percent) are using both AI and GenAI across a wide array of use cases to take advantage of their different, often complementary strengths. Traditional AI is rules-based, requiring prepared datasets and predefined logic to solve business problems. GenAI is great for text-rich environments and unstructured data, creating new content based on the data it has been trained on. For example, companies using traditional AI for demand forecasting and quality optimization are finding that a GenAI layer improves accuracy and democratizes adoption of the tools.

Substantial GenAI Growth Expected

Substantial growth in GenAI is anticipated over the next two years. Given that front-runners already use traditional AI more and are more confident about future growth, the gap between them and followers is likely to widen. Looking more broadly at where front-runners are focusing the first wave of use cases, those with high GenAI deployment today and continued high anticipated use in two years will likely focus on

• Product design
• Logistic network design
• Global trade optimization
• Demand forecasting

These are areas where traditional AI has long been available but has been limited by the need for highly trained data scientists, which has made them too expensive for many. GenAI provides the benefit of a natural language, interpretive layer that can become a democratizing force that puts these tools in the hands of the workforce. These areas are also supply-chain functions with well-defined datasets, a high percentage of unstructured data and high value to be gained.

The next wave of use cases, those with lower GenAI deployment today but high anticipated use in two years, potentially includes

• Supplier management
• Production yield or quality optimization
• Risk management
• Customer service chatbots and product training

These high-growth areas also offer clear commercial returns, either by improving the speed of customer service through chatbots or reducing costs through quality optimization in manufacturing. Demand forecasting is another key area of focus for GenAI. It’s perceived to be a use case that will resolve a lot of pain points for the supply chain and offers clear metrics that can make the business case an easier sell to the CEO and the Board of Directors.

Many of these use cases suffer from back-end challenges when using traditional AI (quality optimization, predictive maintenance) and require bespoke solutions. Customer service chatbots also have a high degree of risk given the chatbots’ direct interaction with the public. Both factors may be contributing to the time horizon suggested here.

Three Actions to Overcome Implementation Challenges

Implementing GenAI in the supply chain involves a complex interplay of technical, organizational, and operational challenges. Organizations should take the following three action steps to advance their journey to the autonomous supply chain.

1. Align people and investments to strategic vision

For front-runners, the top factors for success in GenAI deployments are securing support from leadership (67 percent), building support from third parties (65 percent), and availability of technical talent (64 percent). The largest gaps between front-runners and followers are in prioritizing the strategic vision and support from third parties, which highlight the importance of a cohesive vision and external support in ensuring GenAI pilots and implementations are focused on delivering business value.

A cohesive, strategic vision can clarify investment priorities against the seemingly endless list of use cases of GenAI, minimize the risk of multiple business units duplicating investments, and improve AI outcomes by guiding the augmentation of large language models (LLMs) with reusable algorithmic patterns, such as retrieval-augmented generation.

2. Prioritize data readiness when considering use cases

The demands of GenAI are shining a spotlight on the myriad complexities of enterprise data management. Despite data availability, quality, and privacy being top of mind when prioritizing use cases, organizations are still struggling. Maintaining data quality is the number one implementation challenge cited by respondents (38 percent), with access to data (33 percent) another top challenge.

Any organization hoping to compete in GenAI needs to get its data house in order, by prioritizing data cleansing, standardization, systems, and engineering to reduce latency, and enhancing metadata so data can be consumed by retrieval augmented generation (RAG) systems to improve the accuracy of GenAI outcomes.

3. Maximize GenAI value by mitigating cyber and data risks

GenAI is a nascent technology, so it is not surprising that 40 percent of respondents say their organizations do not fully understand the new risks and challenges of GenAI in supply chain. GenAI poses new vulnerabilities—for example, through prompt injection attacks designed to provoke LLMs into leaking sensitive data or manipulate their outcomes. Front-runners are more likely than followers to focus on the new risks that GenAI poses, such as inaccuracies and hallucinations, exposure to legal liability through IP infringement, overreliance on untested technology, brand or reputation damage and job insecurity.

The need for stronger cybersecurity is paramount as organizations look to deploy GenAI. Supply chain and operations leaders need to work closely with cybersecurity teams from the beginning to help with the secure adoption of GenAI in the supply chain. This includes embedding the cyber team in use case identification and governance to ensure the value potential of GenAI in the supply chain is maximized.

GenAI’s Potential and Pitfalls

GenAI is emerging as a transformative tool, enabling more autonomous supply chains with end-to-end visibility and real-time problem-solving capabilities. EY research suggests that GenAI adoption is crucial for competitiveness, but challenges in understanding risks and implementation complexities have led to a strategic reassessment. Front-runners in GenAI adoption are leveraging it for improved demand forecasting and operational efficiency. To overcome these challenges, companies must align strategic visions, prioritize data readiness, and mitigate cyber risks. For more information, visit Will GenAI accelerate autonomous supply chains? | EY – US.  M

Authors:

Ayoub Abielmona is an EY Global GenAI Supply Chain Leader.

Matthew Burton is an EY EMEIA Supply Chain and Operations Leader.

Sumit Dutta is Principal, Supply Chain & Operations, Ernst & Young LLP.

David Guarrera is Principal with EY Americas Technology Consulting, leading Generative AI initiatives.

Jocelyn Hallum is EY Global Supply Chain Transformation, Planning and PLM Transformation Solution Leader.

Glenn Steinberg is EY Global Supply Chain and Operations Leader.

Over the course of 2024, five research studies depicted an industry still very much in transition to the digital model of manufacturing. 

Ever since its founding in the early 2000s, the Manufacturing Leadership Council has been devoted to using survey research to help take the pulse of digital transformation in manufacturing. Regularly surveying the operational leaders who are leading digital efforts in their companies has yielded insights into digital maturity levels, how advanced technologies are being used, what challenges manufacturers are having in embracing the digital model, and their aspirations for the future.

MLC survey research is usually associated with the Council’s Critical Issues agenda, but with the launch of the Future of Manufacturing Project in 2021, the scope of research widened to include more topics and in-depth white papers.

This pattern continued in 2024, a year in which MLC fielded five major Manufacturing 4.0-related research studies – on Smart Factories; Digital Maturity; Data Mastery, a white paper under the Project; Artificial Intelligence; and Leadership, Culture, Organization, and People.

In aggregate, MLC research for 2024 suggests that manufacturers are at an intermediate stage with M4.0 adoption, with many still working at redesigning their organizational structures, adapting their company cultures, scaling the use of new technologies, and growing leadership skills and behaviors for the digital age.

Here are the key findings of MLC’s research during 2024, as published in the Manufacturing Leadership Journal and in white paper format:

“Smart Factories Are Still a Work in Progress”, February MLJ – This study chartered the stage of M4.0 adoption and found that most respondents are at an intermediate stage in their transformation journeys. The study also found that the most significant roadblocks to implementing a smart factory strategy are organizational structures or cultures that resist change.

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“For Manufacturers, Digital Maturity Hasn’t Yet Come of Age”, MLJ April 2024 – A key finding was that a majority of manufacturers either do not have a formal process for measuring their digital maturity or do informal benchmarking. Another important finding was that a majority of manufacturers have a digital maturity target of Manual Execution with Digital Support, with an equal balance of companies on track or behind schedule on this goal.

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“Data Mastery: A Key to Industrial Competitiveness”, White Paper, June 2024 – This White Paper, under MLC’s Future of Manufacturing Project, examined data governance and organization, how manufacturing data is being used, the business impact of the data, and future trends. A key finding of the study was that 86% of respondents said that the effective use of manufacturing data in their organizations will be “essential” to their competitiveness in the future.

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“Manufacturers See AI as a ‘Game-Changer’ as They Ramp Up Investments”, MLJ August 2024 – No doubt spurred by the interest in generative AI, 78% of manufacturers said they plan to increase spending on AI tools in the next two years. And a majority of respondents, 56%, said they expect AI to change the rules of the industry by 2030, despite issues with data and a lack of AI-related workforce skills.

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“Leadership Preparedness Improves, But Gaps Remain”, MLJ October 2024 – Understanding digital roles and skills for the future manufacturing enterprise is only somewhat understood, said 71% of survey respondents. To change that equation, digital manufacturing leaders need to augment traditional leadership qualities with new skills such as fostering a data-driven culture and guiding the workforce through change.  M

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About the author:

David R. Brousell is the Founder, Vice President and Executive Director, Manufacturing Leadership Council

Celebrating expert insights from a year of manufacturing innovation and leadership. 

As the manufacturing landscape continues to evolve, 2024 brought a wealth of insights, breakthroughs, and transformative ideas to the forefront. From exploring the practical applications of digital twins to harnessing the potential of Generative AI, the articles featured in this year’s best-of issue highlight the ingenuity driving Manufacturing 4.0 forward. The Manufacturing Leadership Journal not only showcased cutting-edge technologies but also delved into the leadership practices, workforce strategies, and cultural shifts necessary to thrive in an era of rapid change.

In this special compilation, we revisit some of the most compelling narratives of the past year. Discover how manufacturers are leveraging AI and machine learning to optimize operations, using smart technologies to attract a new generation of talent, and adopting innovative frameworks for digital transformation. Whether you’re looking to refine your Manufacturing 4.0 approach, improve organizational alignment, or tackle workforce challenges, these articles offer a roadmap for success in the dynamic world of modern manufacturing.

While every article published in the Manufacturing Leadership Journal is chock full of ideas and information that is worth exploring, here are a few of 2024’s best articles.

FEBRUARY / MARCH 2024
SMART FACTORIES AND TRANSFORMATIVE TECHNOLOGIES

How Digital Twin Applications Can Help Manage and Optimize Inventory

By Casey Chapman and Joe Krause, RSM US LLP

Discover how a midsize industrial food production company leveraged digital twin simulations to optimize operations, avoid unnecessary capital expenditures, and enhance decision-making. Highlighting the transformative potential of digital twins, this case study offers insights into improving inventory management, mitigating risks, and driving profitability in a competitive manufacturing landscape.

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Connect, Analyze, Improve — Then Rinse and Repeat

By Randal Kenwothy and Kris Slozak, West Monroe and AJ Alexander, Sorba.ai

Struggling to start your smart manufacturing journey? This article explores how connecting equipment and using machine data to solve specific problems can build a strong foundation for Manufacturing 4.0 success, with real-world case studies demonstrating rapid ROI and expanded operational benefits.

Read Now

APRIL / MAY 2024
THE M4.0 MATURITY CURVE

GenAI Ushers Human-Like Intelligence in Manufacturing

By Amol Adgaonkar, Microsoft; Gonzalo Chavez, Alex Lemken and James Zhang, PTC; and Howard Heppelmann, PTC ThingWorx IoT Solutions

Generative AI is set to revolutionize manufacturing by democratizing data analysis, automation, and problem-solving across the value chain. By leveraging techniques like Retrieval Augmented Generation (RAG) and building a unified, contextualized data foundation, manufacturers can identify high-impact use cases, pilot innovative solutions, and drive significant productivity and efficiency gains.

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Unlocking Manufacturing Excellence with the M4.0 Maturity Model

By Roberto Cisneros and Krishnan Venkat, Softtek

The Manufacturing 4.0 maturity model provides manufacturers with a structured framework to assess readiness and capabilities across key dimensions, including supply chain management, technology, sales and operational management, and knowledge, skills, and attitude. By uncovering gaps and setting actionable goals, the model empowers organizations to navigate technology adoption, foster continuous improvement, and achieve operational excellence.

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JUNE / JULY 2024
CHANGE MANAGEMENT IN THE M4.0 ERA

The Human Factor in Industry 4.0: Capability-Led Change

By Mike Doheny, Roberto Migliorini, Ewelina Gregolinska and Justin Grover, McKinsey

A leading global CPG company successfully scaled its digital transformation by prioritizing people, aligning organizational goals with tailored learning programs, and fostering a culture of continuous capability building. Through a structured approach that emphasized local and global collaboration, measurable outcomes, and on-the-job application, the initiative achieved a double-digit uplift in throughput across its manufacturing network while enhancing employee engagement and talent retention.

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Technology as Manufacturing’s Skills and Applicant Solution

By John Coykendall, Deloitte LLP; Victor Reyes, Deloitte Human Capital; Kate Hardin and John Morehouse, Deloitte Research Center for Energy & Industrials; Gardner Carrick, The Manufacturing Institute

The U.S. manufacturing industry is experiencing robust growth, yet persistent workforce challenges threaten its trajectory, with nearly 1.9 million manufacturing jobs potentially going unfilled by 2033. By leveraging technology to attract, engage and empower workers — through flexible work arrangements, advanced training tools, and high-tech environments — manufacturers are innovating to address the talent gap and meet evolving workforce expectations.

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AUGUST / SEPTEMBER
AI IN MANUFACTURING

Unlocking AI and ML’s Potential in Manufacturing

By Adrian Wood, Dassault Systèmes

Artificial intelligence and machine learning are transforming manufacturing by optimizing production execution, predictive maintenance, and scheduling, resulting in improved quality, reduced downtime, and greater operational efficiency. Successful implementation requires robust data management, collaboration, scalable pilots, and workforce training, while emerging technologies like GenAI and autonomic systems promise to revolutionize self-optimizing operations and model development.

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Exploring the Potential Value of Generative AI Throughout Manufacturing

By John Coykendall, Deloitte LLP; Kate Hardin, John Morehouse and Kruttika Dwivedi, Deloitte Research Center for Energy & Industrials

Generative AI represents a transformative step in the manufacturing industry’s digital evolution, with potential applications in product design, training, supply chain management, and aftermarket services. By leveraging GenAI, manufacturers can enhance efficiency, resilience and innovation, addressing challenges like labor shortages and operational disruptions. As companies explore this technology, they stand to unlock significant value and strengthen their competitive position.

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OCTOBER / NOVEMBER 2024
LEADERSHIP, CULTURE, ORGANIZATION, PEOPLE

Effective Digital Transformation Leadership Practices

By Chris LeBeau, Advanced Technology Services, Inc. (ATS)

Successful digital transformation in smart factories requires clear problem-solving focus, flexible frameworks, and strong leadership. By leading through influence, celebrating small wins, and fostering collaboration, leaders can drive cultural alignment and organizational buy-in. This approach, combined with scalable solutions and trusted expertise, ensures smart factory initiatives deliver lasting value and support long-term strategic goals.

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How Manufacturers Can Use Industry 4.0 to Attract Gen Z

By Buddhi Ratawal, Dassault Systèmes

Industry 4.0 technologies offer manufacturers innovative ways to attract Gen Z talent by aligning with their values of autonomy, collaboration, and purpose-driven work. By leveraging data-driven empowerment, eliminating repetitive tasks, and embedding sustainability into operations, manufacturers can redefine worker experiences and improve their appeal to the next generation of the workforce.

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About the author:

Jeff Puma is Content Director for the Manufacturing Leadership Council

The Future of Manufacturing Project provides a how-to guide for manufacturers to achieve future success 

As manufacturing accelerates into a future defined by rapid innovation and transformative trends, the Manufacturing Leadership Council’s Future of Manufacturing Project has become an essential guide for industry leaders. This project continues the council’s longstanding mission to challenge manufacturers to “imagine a better future.” Through curated insights, the Future of Manufacturing Project brings clarity to the shifts that will shape industry in the years to come.

We’ve gathered some of 2024’s best articles and webinars from the Future of Manufacturing Project, each delving into critical developments that every manufacturer should know. From groundbreaking tech advances to leadership strategies for an evolving workforce, these pieces reveal how embracing change isn’t just necessary – it’s the key to thriving in manufacturing’s next era.

Future of Manufacturing Project: The Coming Data Value Revolution – Reflections on Nashville Panel Discussion

Listen in as this expert panel addresses some of the most important aspects of how manufacturers can leverage operational data to enhance their competitiveness today and in the future, including:

• What the future holds for data monetization, data ecosystems and data-driven innovation;
• How data-driven manufacturing will reshape the future workforce;
• How AI, data visualization, and industrial metaverse technologies will redefine operational excellence;
• And, drawn from the interactive sessions at the event, what manufacturers are planning to do now to prepare themselves for a data-centric future.

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Access to the MLC Member Portal is required to view this video.

Seeing the Unseen: Observability in Manufacturing

Eric Danzinger, CEO and Co-Founder, Invisible AI

What if manufacturers could achieve the same deep visibility into operations as software engineers do with code? Explore how the principles of software observability are transforming manufacturing, making real-time monitoring and data-driven improvements possible on the factory floor.

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Placing Innovation at the Heart of Transformation

David Takeuchi, Global Strategy and Transformation Business Model Innovation Leader, EY
Jerry Gootee, Global Advanced Manufacturing Sector Leader, EY
Claudio Knizek, Global Advanced Manufacturing and Mobility Leader, EY-Parthenon

To build the right foundation for long-term growth, manufacturers must put innovation and digital strategies at the heart of transformation.

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Crystal Ball: Business Resilience in 2030 and the Digital Dexterity Effect

Siva Gurupackiam, Senior Vice President of Manufacturing Industry Solutions, NTT DATA

Digital transformation is today’s tested and proven path to business resilience. This era is defined less by permanence than by alacrity of adaptation. Manufacturers must already evolve their approach to business resilience to remain competitive.

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About the author:

Jeff Puma is Content Director for the Manufacturing Leadership Council

MLC’s member-exclusive virtual working groups offer a pathway to building M4.0 value.  

The MLC’s Decision Compass groups are invaluable for discovering ways to level up your business. In 2024, these five groups explored a number of important M4.0 topics, including considerations for deploying generative AI; meeting the expectations of a multigenerational workforce; how operating data can predict equipment failure; managing supply variability with cloud technologies; and innovative ways to capture and reuse carbon emissions.

These are just a sampling of what was featured in 2024. A full docket of sessions from this year and past years is available in the member-exclusive MLC content library.

Note: These recordings are archived in the MLC content library, an exclusive benefit for MLC members. Members can log in to view the recordings. Nonmembers will be asked to complete an information form to gain access.

Digital M4.0 Technologies
From Data to Insights: Generative AI as a Catalyst for the Fourth Industrial Revolution

Topic Leaders: Srinivas Kuppa, Chief Product Officer, SymphonyAI Industrial; Rahul Shahani, Partner and Industry 4.0 Leader, McKinsey & Company

Where and how can manufacturers apply Generative AI to derive optimum value? What are the best ways to define and achieve ROI? And how does Generative AI factor into broader initiatives for digital transformation? This session dives into unlocking step-change in performance, moving past data silos, setting a strategy to scale, and more.

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Next-Generation Leadership
Attracting Talent and Building Resilience in the Frontline Workforce: A Multi-Generational Approach to Employee Experience

Topic Leaders: Tyler Freeman, Associate Partner; Marino Mugayar-Baldocchi, Research Science Expert; McKinsey & Company

The current manufacturing workforce is comprised of four generations, each with their own desires and expectations within the workplace. Find out about the innovative strategies that manufacturers are using to reimagine the frontline worker experience to keep each generation engaged to boost productivity and employee retention.

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Operational Excellence
Using Operating Data to Predict Equipment Failure

Topic Leaders: Tim O’Neal, Global Operations Director for Operational Excellence and Leveraged Services; Scott Lawson, Maintenance Technology Center Director; Dow

Learn more about the role of data on reliability through four different lenses: shop floor outside (from operators and maintenance teams); shop floor inside (utilizing alarm limits and operator trending); engineering analysis (key variables, inspection trends, and work order data); and AI/machine learning.

MLC MEMBERS: WATCH NOW

NONMEMBERS: WATCH NOW

Supply Networks
Farm to Shelf: Producing, Packing and Delivering the Perfect Mandarin with IoT and Cloud Technologies

Topic Leader: Dhruba Misra, Information Technology Manager, The Wonderful Company

Meet the winner of the 2024 Manufacturing Leadership Award for Digital Supply Chains and find out how The Wonderful Company used cloud-based inventory and manufacturing systems to manage a high degree of supply variability in its production of Halos mandarins.

MLC MEMBERS: WATCH NOW

NONMEMBERS: WATCH NOW

Sustainability and the Circular Economy
GHG Scope 1/2/3 + PCF/LCA and Carbon Capture Case Study

Topic Leader: Kevin Norfleet, Global Sustainability Director, Celanese

Many carbon capture processes capture carbon dioxide emissions and then store them to keep them out of the atmosphere. But what if those emissions could be reused for downstream production? Find out how Celanese is doing just that – and fostering circularity by using those emissions to create products that can reduce the need for fossil fuels.

MLC MEMBERS: WATCH NOW

NONMEMBERS: WATCH NOW

About the author:

Penelope Brown is Senior Content Director for the NAM’s Manufacturing Leadership Council

Kyle Bender

EVP
Falcon Plastics

www.falconplastics.com

https://www.linkedin.com/in/kyle-bender-84138ab7/

Bob Border

Chief Digital and Information Officer
Ingredion

www.ingredion.com

https://www.linkedin.com/in/bob-border-338493/

Evan Doepker

President and CEO
Doepker Industries Limited

https://www.doepker.com/

https://www.linkedin.com/in/evan-doepker-9b089723/

Rodolfo Luzardo

Principal
ZS

www.zs.com

https://www.linkedin.com/in/rodolfoluzardo/

Rick Organ

President and CEO
Hynes Industries

https://www.hynesindustries.com/

https://www.hynesindustries.com/about/leadership

Jon Singer

President/COO
AirLife

https://myairlife.com/

https://www.linkedin.com/in/jon-singer-5b6a492/

Colin Speakman

Digital Transformation Leader
Kalypso, a Rockwell Automation Business

https://kalypso.com/

https://www.linkedin.com/in/colinspeakman/

Angela Accurso, a Next-Generation Leadership winner, shares how accessibility through tech and training can bring untapped workers into manufacturing. 

Penelope Brown: Hello everyone and welcome to our latest edition of Executive Dialogue and I’m happy to be here today with Angela Accurso who was one of our Next-Generation Leadership winners at this most recent year’s Manufacturing Leadership Awards. We had a really great crop of young leaders honored in this category this year and Angela, of course, had some really outstanding accomplishments.

Angela it’s great to have you here today.

Angela Accurso: It’s great to be here thank you for having me.

PB: And I should back up a bit. Angela is the director of workforce programs at the MxD, so much of her focus is on accessibility in manufacturing and that’s what we’re going to talk about a little bit today.

Angela much of your work is about making manufacturing more accessible to those with disabilities, so how do you define accessibility?

AA: Great question and on the baseline level, it really truly means that everyone – regardless of disability status or any other characteristics – has access to and can fully engage within the workplace within manufacturing. But as I think of it, it also goes beyond that so what can manufacturers and the workplace do to meet the physical, social and cognitive needs of every employee and also foster that sense of inclusion where everyone feels valued, everyone feels heard and actually feels that they’re a part of something larger and are appreciated for who they are as a person and what they bring to the table.

PB: Let’s talk about some of those specific things that manufacturers can do. How can a manufacturer take stock of their accessibility? And what are the some of the things that they might be able to do to improve it?

AA: There are a number of different ways to do that. First is really put in the work to undergo the training it’s not a surprise that perhaps not many workplaces have really invested in disability inclusion or disability accessibility training. There are a number of different partners within communities – whether that’s a community-based organization within your network or other folks or consultants that specialize in disability inclusion – those are certainly really great places to start.

“[Accessibility] truly means that everyone – regardless of disability status or any other characteristics – has access to and can fully engage within the workplace.”

MxD is developing an accessibility training for manufacturers as well as university and community college partners, and we’re developing that in partnership with Access Living of Metropolitan Chicago, which is a community-based organization here in the Chicagoland area and they’re part of a national network for centers for independent living and we’re developing that. Currently we’re actually undergoing in-person trainings with manufacturers across Illinois and Wisconsin, but we are developing a training that is going to be a virtual course on our virtual training center platform so individuals who would like to really prioritize disability inclusion can take those courses and understand how to learn the language of disability, how to understand how your operations can be improved, and they can do that from anywhere in the world. That’s going to be dropping in just the next month or so,  but if you’re really prone to wanting to work with a community-based organization, your own community is a really great way and place to start. Doing the work and walking your talk is a really great place to showcase to your employees that you do care about disability inclusion.

Other great ways to do that are doing a full audit. How do you empower your staff to actually feel comfortable coming forward if they do have a disability? One thing I’ll illuminate to manufacturers is that you very much have employees with disabilities already working with you right now. There are 61 million people in the United States who identify as having a disability. While the rates of unemployment are higher for individuals with disabilities, there are certainly individuals with disabilities  represented across every sector in the United States including manufacturing. Manufacturers may just not know that an individual has a disability because they don’t feel comfortable coming to disclose or feel perhaps their job might be at risk. We know that about 76% of employees with disabilities do not disclose that to their employer. And so employers may be thinking “Oh that’s not a need I have to meet on one side because I don’t have an employee that has actively shared that they have a disability.” Whereas employees are saying “I don’t feel comfortable fully coming forward and I can’t fully engage with work the way I want to.” So being able to analyze your current practices and how you could make your workplace more inclusive from the get-go from anybody would be a great place to start. Analyzing policies and procedures, do you have accessible infrastructure, do you have a ramp that could be useful for somebody that uses a wheelchair, those sorts of things.

A lot of those are relatively simple fixes. The median cost of a reasonable accommodation is only $500, so for relatively small investment, employers could be making really great headway for individuals who work for them.

And then also thinking about technology. Technology not just for innovation’s sake, but how you can actually use technology to create accessible working environments because we believe that technology can actually be something that levels the playing field for all individuals in the workplace.

PB: Yes, let’s dive into that a little bit. I mean obviously here at the Manufacturing Leadership Council our focus is very much on shop floor technology, so how can that be something that can bridge a gap for workers with disabilities?

AA: Some of the technologies that we discuss as emerging technologies are great use cases for disability inclusion: augmented reality, virtual reality, the use of drones or robots. You know those are things that while they have a use case for innovation that we talk about time and time again, we don’t necessarily talk about them being something that can actually support disability inclusion.

“The median cost of a reasonable accommodation is only $500, so for a relatively small investment, employers could be making really great headway.”

For an example, augmented reality. We have this on MxD’s factory floor tour. We demonstrate augmented reality in an assembly line fashion and it essentially projects through light-guided systems work instructions onto an assembly line table and provides turn-by-turn instructions for an individual to actually go through the assembly process, which is really great if you think about an individual who perhaps has a disability that impacts their memory, being able to have the turn-by-turn instructions is a great way to ensure that they’re fully comfortable with the process.

Virtual reality is also a great way to simulate what a working environment can look like before an individual even starts the work process and is great for an individual with a disability to be comfortable with what the environment will look like and gain that spatial awareness.

There are a number of different use cases where we talk about technology being a catalyst for innovation but also being a catalyst for disability inclusion. We challenge manufacturers to think a little bit about it being a win-win scenario: not only are you investing in emerging technology to help your operations run better, faster, smarter, more efficient, but you’re also allowing a whole crop of really talented people and an untapped talent pool to be able to participate in work that they perhaps haven’t been able to before.

PB:  Let’s talk a little bit about that untapped talent pool because obviously manufacturers, are struggling to hire. At the end of June there were something like 900,000 unfilled jobs nationally in manufacturing and maybe that figure has been updated but I can’t imagine it’s moved very much. So I would think that creating a more accessible workplace would help to address some of that issue and could bring in more talent to a manufacturer?

AA: Absolutely. We know exactly 900,000 open manufacturing jobs and then a grimmer outlook too when you’re thinking about the end of the decade when a projected more than 2 million unfilled manufacturing jobs are expected to be lost due to a skills gap and due to retirement but also not being able to build up that talent pipeline. And we can’t keep thinking about if we, perhaps, have a vision of what an ideal worker in manufacturing looks like that doesn’t include untapped or under-engaged talent including individuals with disabilities, black and brown communities, veterans, women – these are all talent groups that really have not been engaged as much as they should be within manufacturing. As a result, we don’t have that talent pipeline to build up.

Some really great organizations including MxD, including other Manufacturing Innovation Institutes (MIIs) and other community-based organizations have seen that this is really an opportunity for manufacturers to think outside the box of what their traditional thought process is behind an ideal individual to work in manufacturing. But that also requires manufacturers to have to do the work to make sure that they’re creating workplaces that are incredibly accessible and inclusive because we’ve heard time and time again employees say, “I did the work, I went through the training, I dedicated my time to get a certificate in what my employer said, but at the end of the day I didn’t feel comfortable working there because I didn’t see myself there, I didn’t feel that I had the infrastructure there to support me, whether that be time off to leave early and pick up my kids from school or take time off for leave or at the very basic level there wasn’t a ramp for me to be able to enter the building  if I use a wheelchair.”

There’s certainly a lot of individuals who understand what a great career at manufacturing can be but we need manufacturers to really understand they have to do the work to make it a great place to work.

“We need manufacturers to really understand they have to do the work to make it a great place to work.”

PB: Let’s talk a little bit about your career background. Before you came to MxD, you were working on strategic programs at YWCA, which is obviously a much different organization than heavy industry or manufacturing. How did that experience inform what you’re working to accomplish in manufacturing now?

AA: Yeah, you’re totally right. It was a completely different industry in that I came to MxD without a whole lot of technical background on manufacturing, but I certainly knew what a great economic opportunity it could be to have a manufacturing job. And the programming that I led at YWCA Chicago was aligned mostly toward their mission to eliminate racism and empower women. We had a number of different programs to do that including economic empowerment programming – programs to help women and black and brown communities get into trades jobs. That could include electricians, it could include manufacturing, but we had a number of programs to get folks into the trades and we saw individuals have just the most incredible success stories. Going from working $12 an hour to going through a 12-week training program at YWCA Chicago and graduating after that 3 months making $65,000 starting salary and with benefits. That is huge, huge, huge and it speaks to the potential of a manufacturing career. When we think about manufacturing jobs I think they can be a catalyst to rebuild the American middle class, can be a source of financial sustainability, and really making sure that folks who have not had access to those job opportunities or those career opportunities it really is a game changer for economic and financial sustainability.

These are really strong family sustaining wages that have been very strong for a really long time and is one of the really wonderful perks that manufacturing offers and a lot of what I saw at YWCA Chicago has guided the design of our MxD programming as well.

So how do we engage the under-engaged including women, minorities, veterans, individuals with disabilities and you-name-it, but also how do we get the word out about what I think is just a really great kept secret – and I don’t want it to be a great kept secret anymore: American manufacturing jobs are really, really strong and they can be such a source for economic vitality within the United States.

PB: In your nomination for the Manufacturing Leadership Awards last year, a theme that kept getting repeated over and over again was your ability to really create collaborations, especially across organizations. What do you think are keys to creating a successful collaboration?

AA: As much as I would like to say that MxD, alone, is going to close that 2 million worker gap, it’s just not feasible. No single organization is going to be able to do this alone. When you find those right partners that have that shared mission, that vision and goal are really going to be key to a strong collaboration. Often we get bogged down in the day-to-day and think that we’re the only ones focused on this work, but in reality there are a lot of really great organizations that are so aligned and so eager to partner. It could be a community college that is really ingrained in their community and really understands what their community needs and it has tapped into high schools and employers needs as well. And then perhaps how can we support them in getting great content? Or how can we be thought leaders about American manufacturing jobs and the importance of an inclusive workforce? And there is also an employer that says “I’m on it. I absolutely believe what you’re putting down and now I’m going to go out there and hire more individuals with disabilities, but I do need your support in helping me do the work.”

That’s how we can support and help with our training courses. So I think that mission alignment and goal alignment and the shared values are going to be key to really setting up any collaboration for success.

PB: Well, all of this is incredibly important and shows why you are one of our top Next-Generation Leadership winners last year. You have some tremendous insights on all of this. Thank you so much for your time today and for sharing all of this with us and we’re really excited to see what you do in the future.

AA: Thank you very much. It’s been a pleasure and an honor to be a part of this process.

PB: Thanks, Angela.

AA: Thank you.  M

About the Interviewer:

Penelope Brown is Senior Content Director for the NAM’s Manufacturing Leadership Council