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ML Journal October 2023

Getting Tech Investments to Stick and Deliver ROI

Four fundamentals for realizing maximum potential and business value from technology investments 

Despite economic challenges, global digital transformation spending is set to soar by 2026, emphasizing its resilience-building role and response to workforce issues.
Manufacturers aim to enhance digital maturity by 2030, with 91% planning higher tech spending, including a 20% focus on AI.
Extracting tech value remains tough, with limited focus on business cases. Max ROI needs strategy, proof, execution, and staff engagement.  

Despite continuing economic concerns, the pace of digital transformation is not slowing. By one projection, global spending on digital transformation is projected to reach $3.4 trillion by 2026 — representing a 57% increase from 2023. Transformation is key to building agility and maintaining growth amid challenges such as a shrinking labor force.

Most manufacturers view themselves in the middle in terms of digital maturity. But by 2030, they expect digital operations to deliver tangible benefits in the form of speed and flexibility, customer satisfaction, and financial returns.

To support their digital aspirations, manufacturers expect to increase technology investment relative to current levels. In research for the Manufacturing in 2030 Project, 91% of organizations said they expect to increase spending on technology — 29% expect increases to be “significant.” Moreover, 20% expect investments in artificial intelligence (AI) to increase by at least 50%.

Expectations for return on investments are high. But in the Manufacturing Leadership Council’s 2023 Digital Leadership Survey, one finding stood out: There is relatively little focus on the value derived from technology. Only 16% said their executive management team wants to know the business case for and payback from digital transformation, and only 14% said management wants to know which digital use cases will drive the most value for the investment.

If manufacturers are to get maximum value from their technology investments — including forays into AI and other emerging digital capabilities — they must be laser-focused on value and their approach to delivering it.

Why manufacturers have struggled to derive value from technology

It is hard to fault those who are struggling to maintain the trajectory of digital transformation amid a procession of major — if not generational — macroeconomic challenges. These issues of the moment are critical and have commanded attention. That said, short-term focus often creates a fragmented approach to longer-term strategy. And present cost-containment imperatives run the risk of derailing critical projects — particularly those in early stages that have not yet produced a return on investment. When the markets rebound and attention shifts from cost to growth, manufacturers that have paused or cut critical projects will be behind.

Continuously evolving technology trends can also cause transformation to stray from “the plan.” There is perhaps no better example of this than the recent surge in interest around generative AI and its potential to assist with challenges such as worker shortages and the need to do more with less. The fact is, this technology does not necessarily have immediate impact potential for all manufacturers, but many have probably shifted resources away from critical initiatives to explore it.

“When the markets rebound and attention shifts from cost to growth, manufacturers that have paused or cut critical projects will be behind”


Risk aversion is another big factor — most notably, cyber concerns that have impeded the full convergence of information technology and operational technology (IT/OT) necessary to create insights for the digital factory. Additionally, IT/OT integration requires effective collaboration with the IT function, which historically has focused on managing spend and securing data. This doesn’t always create a return on investment, and in some cases works against it.

Finally, many manufacturers have struggled to scale up new technology initiatives. The World Economic Forum’s Global Lighthouse Network recently looked at this issue. More than two-thirds (70%) of manufacturing companies worldwide are currently stuck in pilot mode when it comes to digital transformation projects. It is hard to maximize value from technology when it is only used on a small scale.

How to maximize the potential and business value of technology

Value does not come through technology alone. It is the product of the organization’s approach to investing in it and capabilities for deploying it. Here are four fundamentals that are at the center of maximizing potential and return on investment.

1.   Determine the projects that are foundational enablers – and do not let them get derailed by short-term thinking

There is a tendency to look at every project as a discrete investment and try to prove the return. When it comes to evolving technologies in manufacturing, there are many interrelationships. Some technology investments are “nice-to-haves” when you look at them alone, but they are “need-to-haves” when it comes to the bigger picture. It may be hard to calculate a direct return on investment because their real value comes through their enablement of other capabilities.

A good example is master data management. A project to clean up and restructure data does not necessarily produce a return on investment. Alone, it looks like just another cost. But it is a necessary step for building analytics that will help your organization identify problems, increase reporting accuracy/timeliness, or even enable advanced strategies such as data monetization. It is beneficial to couple projects or investments in a way that makes it easier to understand the impact and justify the investment.

This is why technology transformation requires long-term vision and the ability to move toward it with pace. Having a macro, multi-year picture that shows how discrete projects connect, and which ones are vital to building the foundation that enables long-term value creation.

“Some technology investments are ‘nice-to-haves’ when you look at them alone, but they are ‘need-to-haves’ when it comes to the bigger picture”


This long-term perspective is particularly critical in the current economic environment – with the temptation to cut budgets or delay spending in the interest of cost containment. When it comes to these foundational enablers, be thoughtful in asking and answering the question: If we cut or delay investments now, what happens to processes and plans down the road? Many times, we have seen scenarios where it costs more to fix things later, or to accelerate stalled capabilities. Furthermore, short-term thinking in order to balance cost, timeline, and ROI today may exclude future options and inadvertently paint the organization into a corner later.

2.  Identify the right investment opportunities — and use data to build conviction in their materiality by demonstrating the ROI

Beyond the foundational enablers, technology decisions and investments should always be grounded in business strategy and the imperatives that are driving the need for change and new technology — whether that is reducing cost, making more product, increasing sales, improving response time, or eliminating defects.

Evaluating and prioritizing opportunities should involve a value identification exercise that:

  • Identifies current issues and diagnoses the causes
  • Explores how technology can solve issues and create new value
  • Establishes a baseline that allows you to measure future impact and value creation
  • Defines your method for measuring impact on the P&L statement; anyone can pick metrics, but metrics don’t always measure return on investment

Many manufacturers struggle with the ROI part — specifically, demonstrating technology’s projected ROI to secure approval for investment. For one thing, it is not uncommon for the value or return from new technology to lag. Consider the generative AI example: Maximizing value from this capability requires time to build up the skills, the tools, and the capabilities for the payoff to really materialize.

Demonstrating ROI also requires being able to bridge the gap between the art of the possible and the realities of today. Data is key. You must be able to analyze data to translate potential into quantifiable operational and financial outcomes. Ideally, this analysis should use your own operational data rather than simply market benchmarks, which lack consistency in calculation and do not account for unique aspects of your business.

“If you are piloting a digital solution on one line or in a lighthouse facility, design it in a way that makes it simple to deploy to the rest of your operation and start planning for expansion to other lines or facilities”


This is a critical step, and there is a tendency to try to address it internally. For many, it is a matter of cost. Keep in mind that this is new territory for many in manufacturing, so consider carefully whether you can do it effectively on your own or consider external expertise. Some questions to ask include: How mature are our data and analytics capabilities? Do we have the bandwidth or expertise internally? How quickly can we get it done versus having an external resource?

3.  Bridge the gap between strategy and execution

Even if a company can effectively identify value potential, delivering it is another matter. The 2023 Digital Leadership survey revealed measured anxiety about organizational vulnerability due to current levels of preparedness. More than one-half of respondents (59%) consider their organization to be moderately vulnerable. One statistic was telling: 33% believe their executive management team is not at all prepared to lead and manage digital transformation. This was up from 10% in the 2022 survey.

Improving execution requires several factors:

  • An effective management system and structure to guide progress
  • Clear execution plans with clear definitions of responsibility
  • Tracking to capture progress, assess value, and inform timely adjustments based on what is and is not working

In particular, an effective management system facilitates execution by integrating people across functions, establishing proper controls within processes, and providing structure and oversight for developing the right capabilities. This system also plays a critical role in fostering positive behaviors such as a bias for action, sense of accountability, willingness to challenge the status quo, reliance on data, and others.

For manufacturers, one big execution opportunity is improving the ability to scale successful pilots. There are many ways to do so. If you are piloting a digital solution on one line or in a lighthouse facility to see how it works and assess return, design it in a way that makes it simple to deploy to the rest of your operation and start planning for expansion to other lines or facilities. In addition, look for additional applications for technologies in which you have invested, such as your IIoT or cloud platform, FactoryTalk, AWS SiteWise, or others. Since digital operations produce data and data enables insight, look at the insight you are deriving from implementing digital technologies and ask, “What else could we learn with additional analysis?” Always be looking to learn and grow.

4.  Bring your people along

A big element in bridging the strategy-execution gap is people. This deserves its own point. Wherever you are on your digital journey, do not underestimate the need to bring people along.

Giving people digital tools and systems enables them to look at information and make good, timely decisions. But you must engage leaders, employees, and teams in the process — rather than just handing off new technology — so they understand how and why to use it. Without that knowledge, they will not own or embrace technology, and you will miss out on additional innovations that they can bring as users of the technology. That is how value is created and grows.

“You must engage leaders, employees, and teams in the process — rather than just handing off new technology — so they understand how and why to use it”


Dedicated attention to this not only creates enterprise value, but also career potential and longevity for employees and aids in addressing one of manufacturing’s biggest challenges: retention.

This is a big opportunity many companies are missing. The digital journey must have a change management strategy and plan. Yet 60% of manufacturers do not have a change management strategy to support their digital strategy.

An adequate training/learning approach is also critical. Manufacturers cannot fall back on traditional “blanket” training formats. Instead, look to techniques that really imbed knowledge and understanding such as personalized coaching plans or instructions and help screens built into the tools for reference when employees need it most.

The real value comes with the right approach to deployment

Value realization is about more than the technology itself. In fact, the four fundamentals described above really have little to do with technological features and functions at all. Establishing them requires effort, and some of it is hard — and often unfamiliar — work for manufacturers. But do these well, and you will see exponential growth in the business value and potential from your technology investments.  M


About the Authors:

Randal Kenworthy
is Senior Partner, Consumer and Industrial Products, West Monroe



Kris Slozak
is a Director, Consumer & Industrial Products, West Monroe


ML Journal October 2023

M2030 Perspective: FOMO Can Spark Next Digital Revolution

What the past tells us about an AI and ML enriched future 

MLC Crystal Ball

Manufactures can leverage AI/ML strategies to overcome the prohibitive cost related to data-driven decision-making
AI and ML are business investments not technology investments
In the future, AI will drive people, process and technology changes that deliver manufacturing ROI   

Past is prologue when it comes to AI

More than two and a half decades ago the world was in a state of panic and desperation over technology. The situation was simple: business solutions that ran supply chain, financial, and payroll systems – to name a few – could have a basic flaw in how dates were stored, and on January 1, 2000, they could simply stop working and business would stop working. The Y2K problem led to a massive investment in IT spending, turbo charging the technology capabilities that brought about the recent advancements in artificial intelligence and machine learning (AI/ML).

Then as it is now, we have a disruptive concept that threatens to upend business as usual, and executives can no longer avoid the hard discussions on how to prioritize technology investments. Then as it is now, we have a juxtaposition of business and technology interests intersecting a common goal of relevance and sustainability. The difference is that with artificial intelligence the fear of missing out is driven by excitement and opportunity.

“Leader must be able to ideate, innovate, and create future solutions while leading, empowering, and engaging teams”


The concept of data-driven decision-making, which is the basis of leveraging AI/ML in manufacturing, is not new but it has been relatively unattainable due to costs. Manufacturing is a people-driven enterprise that needs to maximize efficiencies to keep costs down and react to customer demands. The challenge is how to balance business operations with the disruption in leveraging new systems and process, balancing the potential skill gaps in using automated systems, and driving towards measurable business outcomes. As we move toward the year 2030, AI/ML can be the unifier of all those goals if manufacturers can map executive goals and business outcomes to people, process, and technology.

People power to drive top-down transformation

With the popularity of topics around generative AI and ability to access technology infrastructure for various back-office automation tasks there is urgency to innovate to stay ahead. The change that occurred since last year is driven by awareness, access, and actionable insights in the manufacturing space driving pragmatic business innovations. Executives have greater awareness of how to leverage AI/ML in business operations and are empowering their teams to invest in digital strategies around automation and efficiency. IT and business leaders within organizations are finding common cause in terms of goals for improving supply chains, understanding customer behavior, improving product quality, and investing in sustainable practices. The general concerns and narrative that AI may replace people is shifting to how AI is helping teach the workforce and automate complex production processes. While common misconceptions around AI sophistication and the practicality of certain use cases still exist, the urgency of change is overcoming the stagnant comfort of the predictable. As we move towards 2030, we will see more broad adoption of these technologies to work side-by-side and seamlessly in performing business actions severely reducing manual repetitive tasks.

Pragmatic solutions to drive maximum ROI

Concepts like Industry 4.0 drive business goals towards automated dark factories heavily leveraging IoT sensors that constantly improving plant output. Looking beyond factory operations, AI/ML solutions can optimize entire value chains as illustrated in a sample view of an equipment as a service manufacturer/distributer.

Figure 1: Leverage AI/ML solutions to drive enterprise value in manufacturing and after-market service.


The benefits earned from leveraging AI/ML solutions to track asset management and performance, labor, and financial planning are an example of data-driven decision-making and leveraging “one version of the truth” in business operations with clear ROI.

Considering these use cases can be setup very quickly and incrementally, they can drive manufacturers’ changes and urgency to adopt measurable business innovations.

Investment in business versus technology

Manufactures, like other businesses, are loathe to spend money on IT for the sake of re-platforming the business. Perception of IT spending as a costly task changes significantly with AI/ML-powered solutions.

The target goals for these solutions are business productivity, plant scrap reduction, financial visibility, and improved revenue, which are markedly more impactful than purchasing a new business application. Other tangible business applications are better customer and employee experience driven by personalized and contextual actions. The implicit benefits of cloud-based architecture and improved cyber security are attractive but not as much compared to improved margins and responsive business operations.  Focusing on technology as a means for better business results is a big difference in driving future investments in the next several years. Manufacturers will invest in approaches that drive better, transparent, explainable, and faster time to value that provide seamless decisions between different business systems with more emphasis on action versus interaction. Focus will be on how automated decision-making results in improved productivity versus the current focus on user experience and complex manual tasks.

Bringing digital revolution 2.0 into focus

As manufacturers begin adopting data-driven decision-making tools, the current AI/ML strategies will change to adapt to more complex ecosystems. While we already have focus on sustainability and automated decision-making, the future will bring more emphasis on circular economy value chains involving leveraging data across different companies across the product lifecycle and responding proactively to customer needs. By 2030, this will open up an avenue to track products, customer use, recycling, and maintenance, which will expand business opportunities for new product development and new manufacturing processes. This will ultimately and hopefully lead to digital revolution 2.0!  M

About the author:
Sandeep Anand 
is Senior Director of Decision Analytics and Science Platform at Infor. He has over 15 years of experience building and delivering AI/ML solutions. Experience includes solutions around yield/scrap, supply chain improvements, and smart asset management strategies. He leads the AI/ML practice at Infor, a leading enterprise cloud solutions provider.

ML Journal October 2023

Strategic Scalability: The Key to Realizing Your Factory of the Future

Merging new tech with established operations requires a thoughtful approach and a roadmap for sustainable expansion. 

A strong technology strategy that addresses the organizational workforce, engineering processes, and effective technology solutions is vital for manufacturing resilience.
● For long-term success, a phased implementation strategy often outperforms rapid adoption of cutting-edge technologies.
Effective scaling involves swiftly adapting your technology strategy to the unique demands of each operational setting, while establishing a repeatable foundational framework.   

In an era characterized by rapid technological evolution and intensifying global competition, manufacturers must not only adapt but strategically reimagine and restructure their manufacturing processes to survive. This journey is unique for every industry sector, individual organization, and manufacturing facility, but often begins with a unified vision for digital transformation and the “Factory of the Future.” This vision is anchored by disruptive technologies such as the Internet of Things (IoT), artificial Intelligence (AI), hyper-automation, and advanced robotics, all aimed at achieving significant gains in efficiency, flexibility, and competitive advantage.

While these new technologies hold the promise of transforming traditional manufacturing into highly agile, customer-centric operations, many manufacturers struggle with their adoption—stifled either by a lack of vision or a failure in execution. These misconceptions or missteps can hinder the progression towards fully digitalized operational environments. The integration of these technologies demands a nuanced, continuous approach underscored by a well-defined strategic framework. In this article, we provide comprehensive guidance on how to formulate, implement, and scale technology strategies that cater to both immediate operational objectives and overarching long-term goals.

Crafting the Strategy: Bridging Today and Tomorrow

To build the factory of the future, organizational leadership must first delineate what the future looks like for their organization. This foresight isn’t about predicting exact technological trends, but rather about understanding where the industry is moving and where an organization’s capabilities lie. The intersection of industry trends and an organization’s core competencies gives birth to its unique technology strategy.

“The intersection of industry trends and an organization’s core competencies gives birth to its unique technology strategy.”


Unfortunately, in their enthusiasm for technology adoption, many manufacturers overlook the foundational step of strategic planning. Failing to plan is essentially planning to fail. This lack of foresight often leads to a fragmented technology landscape, where isolated, high-cost investments yield little impact on overall operations. Conversely, an effective strategy adopts a comprehensive approach that integrates technology, human resources, and operational processes. Central to this strategy are clearly defined objectives—be they cost reduction, quality enhancement, or customer satisfaction—that align with the broader organizational mission and vision.

Developing a coherent strategy includes the following components:

  • A detailed assessment of your current manufacturing capabilities, processes, and workflows;
  • A robust risk assessment and mitigation plan;
  • An assessment of existing skill sets within the organization;
  • An analysis of the readiness of the current systems to integrate new technologies; and
  • An analysis of the financial implications of the technology roadmap.

Through this rigorous exercise, leaders can identify the most critical technologies that will provide a quick win while serving as a foundation for future scalability. Using this information as a baseline, your technology strategy will evolve from a simple shopping list of the latest technologies into a more comprehensive roadmap that addresses identified gaps and leverages the strengths of existing operations.

Executing the Strategy: A Phased Approach

Navigating the journey from traditional manufacturing processes to the factory of the future is a multidimensional challenge that requires both visionary leadership and grounded pragmatism despite the inclination to adopt cutting-edge technologies in the pursuit of an “overnight overhaul.” This urge to quickly integrate innovative technologies often misleads manufacturers into thinking that speed is a proxy for success. By taking a more systematic and phased approach that compartmentalizes such a massive undertaking, organizations can mitigate risk strategically and allocate resources judiciously, ensuring they maintain operational stability while they improve technological capabilities iteratively. A phased approach also offers the agility and flexibility that are paramount in today’s volatile business environment, without sacrificing the thoroughness and attention to detail that manufacturing firms require. Creating a digital backbone is a prerequisite for success and taking the “start small, scale up” approach drives the ability to make an immediate impact on operations.


According to a 2023 survey conducted by Deloitte, nearly 62 percent of manufacturing firms that opted for an overnight digital overhaul experienced significant operational disruption, compared to just 28 percent of those that implemented a phased approach. While the allure of an expedited transformation is understandable—especially given the competitive edge it promises—these numbers make a compelling case for incremental change. The same Deloitte survey found that 45 percent of companies that opted for rapid transformations incurred unexpected costs that exceeded their initial budget by 20 percent or more.

“Creating a digital backbone is a prerequisite for success and taking the “start small, scale up” approach will enable manufacturers to make an immediate impact on their operations.”


Beyond the statistical backing, a phased approach allows for real-time adjustments and optimization, providing a “safety net” of sorts that can help mitigate unforeseen challenges and expenses and simultaneously align well with the complexities and intricacies of manufacturing. The production process often involves multiple layers of interdependent systems and workflows; a change in one area can have ripple effects across the entire operation. By employing a phased approach, executives can monitor the impact of changes more closely and adjust strategies as needed without disrupting the entire ecosystem. For industry leaders and executives, this approach offers a harmonious blend of innovation, strategic risk management, and astute financial planning. It fosters a smoother transition and higher rates of adoption among employees, creating a rapid—tangible and intangible—time to value.

Ensuring Seamless Execution: The Importance of Integration

Ever-advancing technologies that continue to change the shape of manufacturing are fundamental drivers for reimagining processes, operational efficiencies, and customer satisfaction. Taking small steps to strategically incorporate them into an organization’s operational ecosystem is only the beginning of the journey.

Once a well-articulated strategy is in place, attention must shift to integration, and here the sequence of execution is just as crucial as the technologies selected. While it is tempting to chase the allure of disruptive technologies, indiscriminate adoption often leads to redundant systems, wasted investment, and a complicated, unmanageable technology stack.


System integration is the cornerstone of operational effectiveness in any modern factory setting. A host of variables—including the persistence of legacy systems, data siloing, and a multigenerational workforce—can serve as impediments to the successful enactment of an industrial transformation strategy. The role of human capital and procedural methodologies is non-negotiable in the successful integration of emergent technologies. Multidisciplinary teams, comprising experts in operational technology, data science, and on-the-ground operations, are indispensable for calibrating collective initiatives toward a common engineering objective. The robustness of any digital architecture is fundamentally reliant on three key pillars: 1) seamless technological integration, 2) uniform data governance protocols, and 3) adaptive human resource strategies. Incongruence in any of these domains risks compromising the operational efficacy of the overarching strategy.

Successful integration is not just a catalyst for competitive advantage, it’s a determinant for survival.

In the 2023 Manufacturing Trends Report, Alithya revealed that companies effectively integrating the right technologies with their operational dynamics have experienced a surge in productivity by an astounding 27 percent, and a subsequent reduction in operational costs by nearly 19 percent. What the manufacturing organizations leading the charge in transformation have in common is that they have each cultivated a culture of innovation and integrated these innovations into the very fabric of their operational DNA.

Scaling a Technology Strategy: A Blueprint for Future Readiness

Characterized by fluctuating demand, raw material availability, and geopolitical uncertainties, a manufacturer’s ability to effectively scale a technology strategy is tantamount to achieving operational agility and sustained growth. Scaling is not merely a replication of solutions that have demonstrated value in a small-scale setting; it is an intricately planned endeavor that aligns technology with business objectives, regulatory frameworks, and workforce capabilities. Contrary to popular belief, it does not merely refer to deploying more of the same technology across an enterprise. It involves understanding the contextual intricacies of each operational environment and adapting the foundational strategy to the idiosyncrasies accordingly.

“Your factory of the future vision is not a linear path but a complex, multi-dimensional journey. It requires an integrated approach, focusing equally on technologies, human factors, and processes. ”

What works well in a controlled, small-scale environment may face unexpected challenges when applied across multiple locations or larger operational scales. The foundation of effective scaling is a well-defined technology architecture that adheres to modular design principles and allows components to be added or replaced without disrupting the entire operation. This foundation is essential for project success. This process of using open, yet defined standards ensures that the architecture remains flexible and eases the burden of incorporating new technologies without excessive alterations or a complete overhaul. This symbiosis not only enhances data-driven decision-making but also promotes a culture of continuous innovation, and essentially creates a scalable ecosystem that accommodates growth while maintaining optimal performance levels.

Are You on the Right Path?

Your factory of the future vision is not a linear path but a complex, multidimensional journey. It requires an integrated approach, focusing equally on technologies, human factors, and processes. Successful technology strategies are built on the pillars of organizational alignment, an understanding of your operational ecosystem, and robust governance with a focus on scalability and futureproofing.

Developing transformation strategies are ultimately less about technological adoption and more about transformational amalgamation. It’s not simply adding technology to existing processes but reshaping those processes around new capabilities. For example, AI-driven analytics do not merely speed up data processing; they revolutionize decision-making algorithms. Similarly, advanced robotics are not just about automating tasks, they reimagine the workflow. Thus, in the quest for the factory of the future, it’s crucial to see technology as a dynamic catalyst for organizational evolution.

Manufacturing success is not determined merely by the adoption of cutting-edge technologies, but by a multifaceted approach that synergizes internal capabilities with strategic external partnerships. This synergy allows for the seamless integration of modular solutions into the existing technology infrastructure. Concurrently, it is imperative for executives to foster an organizational ethos that views technology as the critical fulcrum for navigating present challenges and capitalizing on future opportunities. This integrated approach accelerates the return on investment and also augments the firm’s competitive edge in innovation. As we stand on the cusp of a revolution driven by technological capabilities we are only beginning to understand, the existential question for manufacturing leaders is no longer about whether to invest in digital transformation, but rather how to delineate a strategy that judiciously chooses the appropriate technologies and partnerships for transformative and sustainable execution.  M

About the authors:


Holly Becker is Director of Commercialization, ATS Applied Tech Systems, LLC


ML Journal October 2023

SURVEY: Manufacturers Go All-In on AI

A growing number of manufacturing organizations have brought AI to the shop floor, and as usage grows, so do aspirations for its future impact, a new MLC survey reveals.   

There were two open-ended questions at the close of the Manufacturing Leadership Council’s 2023 Transformative Technologies survey. The first: Which single technology is currently having the most impact on your manufacturing operations?

Of the 171 respondents to this year’s survey, 19 cited AI or machine learning, the most commonly mentioned response, with four of those mentioning generative AI specifically. Not far behind were manufacturing execution systems, with 13 responses. Rounding out the pack were automation and data analytics with about 10 responses each. Honorable mentions went to cobots, vision systems and ERPs. Some respondents elaborated on the power of system consolidation and broader data access.

Far more consensus was seen, however, on the second open-ended question: What technology do you believe will have the most future impact on your manufacturing operations? For this question, 79 respondents mentioned AI or machine learning (or their cousin, predictive analytics), with robotics and digital twins also receiving multiple, if far fewer, mentions.

This tracks with the 40% of respondents who said they have adopted AI widely or at least on a pilot basis, with another 53% saying they were either researching use cases of developing a plan for implementation.

The MLC has been closely following the AI groundswell in manufacturing, with more use cases continuing to emerge. Most are currently using it for process improvement or predictive maintenance, and many plan to broaden its use in their supply chain, distribution, and sustainability efforts.

Read more below on the results of this survey:

Part 1: Strategy and Organization

While almost 90% of respondents to last year’s Transformative Technologies survey said they expected M4.0 technology adoption to increase, this year’s response shows a more measured approach to investment – possibly due to economic headwinds faced by many manufacturers.

1. Do you expect your company’s rate of M4.0 technology adoption to increase or decrease over the next two years? (Check one)

Many companies continue to take a fragmented approach to M4.0 – just 16% say they have an organization-wide technology roadmap. Far more either only have partial strategies or an informal approach to M4.0 deployment.

2. Do you expect your company’s rate of M4.0 technology adoption to increase or decrease over the next two years? (Check one)

The person in charge of M4.0 strategy is most frequently an operational VP, followed by the CIO/IT department or a collaborative effort between teams.

3. Who is responsible for leading and implementing your M4.0 strategy? (Check one)

At the end of the day, the bottom line rules – most respondents said that reducing costs and improving efficiency were the top reason for investing in M4.0.

4. What are the most important reasons your company invests in transformative M4.0 technologies? (Check top 3 reasons)

Part 2: Technology Investment Plans

Presently, the most common OT/IT investments are data analytics software, cloud computing, ERP planning software, and MES. Strongest near-term plans (12-24 months) are in AI and supply chain management software followed by edge computing and product lifecycle management. Additionally, about a third are considering digital twins and quantum computing for longer-range plans.

5. What are your company’s investment plans for the following OT/IT-related technologies? (Check one in each column)

For production technologies, process control systems are in the lead for current investments while nearly half say they have industrial robotics and/or vision systems in place. Other popular technologies are additive manufacturing and AGVs or mobile robots. In the near-term manufacturers indicate plans to implement machine learning and condition monitoring technologies.

6. To what extent have pandemic-induced supply chain disruptions eased for your company? (select one)


Drilling specifically into AI, over a third of respondents said they had implemented AI in pilots or on a single project (34%), with almost as many saying they were developing a plan for implementation (27%), a slight uptick compared to last year’s responses on AI usage. Very few say they have implemented it widely (6%), while even fewer said they had made no progress toward adopting AI (5%).

7. Where does your company stand today in adopting AI in manufacturing operations? (Check one)

The current key applications for AI usage are process improvement, preventive/predictive maintenance, productivity/cost reduction, and quality improvement.

8. What are the key application areas for AI and Machine Learning technologies in your manufacturing operations? (Check all that apply)

Outside of manufacturing operations, respondents say the greatest number of current AI use cases are in sales and customer intelligence followed by product design and development. Within the near term, manufacturers expect to broaden its use within distribution and logistics, supply chain, sustainability, and procurement.

9. In what other areas of the organization are you deploying AI and Machine Learning, or planning to deploy over the next two years? (Check one option for each area)

Asked specifically about generative AI technologies like ChatGPT or Bard, 44% indicated they are using it on a limited basis and 17% said they are using it regularly. 10% say they have no plans to use it at all.

10. What is your organization’s position on using generative AI like ChatGPT or Bard? (Check one)

Views on the impact of AI in manufacturing show that while many already see it as significant (36%), nearly half of respondents felt it will be a game-changer by the year 2030 (47%).

11. Overall, what is your current assessment of the potential of AI and Machine Learning, both today and by 2030? (Check one in each column)

Part 4: Impacts and Challenges of Transformative Technologies

When it comes to sharing data across functions of the enterprise, the digital thread is coming –only 23% of manufacturers have one currently, but 50% say they have plans to implement it in the future.

12. Has your company implemented a digital thread to share data generated by one or more of the M4.0 technologies you have adopted across multiple functions? (Check one)

The trend toward workplace flexibility, likely emerging out of necessity during the pandemic, hasn’t fully abated – 63% say they have invested in technology meant to allow for remote operations.

13. Has your company made technology investments with an eye toward allowing greater workforce flexibility (i.e. remote operations)?

When asked what their most pressing challenges were related to M4.0 adoption, the top responses were developing a cohesive strategy (42%), assessing the cost and benefit of deployments (37%), understanding and evaluating new technologies (31%), and migrating from or integrating with legacy systems (31%).   M

14. What are your top three challenges related to adopting and using M4.0 technologies? (Select top three)

About the author:

Penelope Brown

Penelope Brown
is Senior Content Director for the Manufacturing Leadership Council.

Survey development was led by the MLC editorial team with input from the MLC’s Board of Governors.



ML Journal October 2023

How Immersive Technologies Make Manufacturing More Efficient

Immersive technologies strengthen asset management and bolster smart factories. 

Higher levels of reliability, speed, and productivity as well as enhanced safety and compliance are benefits of immersive technologies in smart factories.
Immersive technologies reduce training costs and increase new-hire productivity with real-time, interactive, on-the-job training.
Tools like augmented reality and virtual reality enhance maintenance practices, reduce downtime, lower maintenance costs, and enhance manufacturing performance.   

While manufacturing automation could be considered the original smart factory technology, it is certainly far from the last. Transformative technologies are making inroads at all levels of plant operations, including equipment maintenance and repairs.

Not long ago, industrial maintenance was primarily a laborious, reactive function routinely performed after the damage was done or the asset ran to failure. This changed with the advent of Industry 4.0 technologies, which made the maintenance of complex and increasingly sophisticated equipment more proactive, predictive, and to some extent even automated. Using smart maintenance tools and practices helps manufacturers avoid costly equipment failures and unplanned production downtime, and enables plants to increase performance and throughput and boost the bottom line.

Immersive technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR) are prime examples of smart maintenance technologies. Providing an immersive reality experience to technicians and reliability engineers is transformative in terms of improving maintenance efficiency and effectiveness. In turn, immersive technologies drive smart factories to higher levels of reliability, speed, and productivity while enhancing safety and compliance.

Immersive Technologies Explained

Immersive technologies extend and deepen the information available from which manufacturers can make decisions and take action. Digital or simulated reality supplement a technician’s perceived reality in the real world. There are three immersive technologies commonly used: augmented reality, virtual reality, and mixed reality

Augmented Reality

AR augments the scope of information available to technicians beyond what their senses, memory, work orders, inspection checklists, or other materials normally at hand provide. It takes live video of the real asset or environment via a camera-equipped phone, tablet, or wearable such as goggles and glasses (for a hands-free approach), and then overlays these additional digital images, graphics, notes, or other supporting materials over the real-world setting—on demand, in real-time, through a head-up display. Hand gestures, eye tracking, and voice recognition are among the ways a technician can request supplemental data and imagery and take it into immediate consideration.

“Augmented reality stands out for its hands-free, real-time data access, empowering technicians with unparalleled insights on the shop floor.”


To further facilitate maintenance and inspection, AR with audio capabilities enables real-time communication with remote experts, anywhere in the world, who can assist by answering questions, diagnosing problems, or guiding the technician through specific tasks—without incurring the time and travel costs of an on-site visit. AR stands out for its hands-free, real-time data access, empowering technicians with unparalleled insights on the shop floor.

Virtual Reality

VR headsets bring technicians into a completely virtual world, separate from their physical surroundings, so they can actively and safely interact with the virtual environment.

VR is frequently used in manufacturing to simulate real components, machines, processes, or facilities, and the virtual environment then becomes a maintenance training and troubleshooting aid. It provides a “hands-on” experience without putting the employee or equipment at risk of errors. VR also serves as a design and optimization aid for reliability engineers, who can simulate and improve conceptual assets or environments before seeking investment in the physical solution. VR solutions with remote collaboration capabilities enable more effective discussions because the participants can interact with the virtual components and “see” how they might work in a real-world setting—compared to simply studying technical drawings or CAD models.

Virtual reality’s potential for cost savings through remote access to parts and machinery is a game-changer for communication and training processes, offering greater timeliness and efficiency.

Mixed Reality

MR blends the capabilities of AR and VR. It allows users to interact with both real-world and virtual objects at the same time, or to have touchpoints to the real world while immersed in a virtual world. As an example, a technician working on a pump can call up the work history and a digital twin of a component for additional context (AR), and then manipulate the digital twin (VR) to explore different ways to tackle the problem before applying the best option to the pump.

Each of these immersive technologies enhances maintenance practices by delivering the right information at the right time, with minimal effort. Using tools like these to reduce downtime and maintenance costs elevates manufacturing performance.

Figure 1: AR/VR Explained

Positive Influence on Maintenance and Manufacturing

Applications for immersive technologies in maintenance are widespread and growing rapidly. This trend is not surprising considering the increasing sophistication of industrial machines and the risks of failure. Smart factories are gaining extensive efficiencies and operational benefits from immersive maintenance practices across the board. We highlight seven specific benefits:

  • Maintenance: Industrial maintenance, troubleshooting, diagnostics, and inspections are more accurate when technicians can see, right in front of the equipment being serviced, digital aids such as instructional notes with arrows prompting them through the task at hand. Immersive reality makes the old ways of digging up supporting or historical materials or working with incomplete information, antiquated. Immersive technologies improve maintenance quality, repair time, and the first-time fix rate while reducing scrap and rework.
  • Operations: Operator rounds are more effective when the operator can call up historical data to evaluate whether the present conditions are anomalous.
  • Training: Learning and practicing maintenance tasks in a virtual space, and refreshing and reinforcing knowledge using augmented means, protects employees and equipment by ensuring those doing the work have every opportunity to do their jobs correctly. Immersive technologies reduce training costs and increase new-hire productivity with real-time, interactive, on-the-job training. It facilitates knowledge transfer from more experienced personnel and enables remote coaching and upskilling to keep up with changing technologies, processes, and standards.
  • Safety and regulatory compliance: Manufacturers can avoid safety incidents when technicians can troubleshoot problems in a virtual environment without having to disassemble the equipment. Lockout/tagout procedures and safety data sheets cannot be missed when they appear in front of the technician’s eyes. Simplifying the way records of performed work are captured ensures proper documentation is available for audits.

“Virtual reality’s potential for cost savings through remote access to parts and machinery is a game-changer for communication and training processes, offering greater timeliness and efficiency.”

  • Reliability engineering: When a virtual playground for experimentation and improvement exists, design for reliability and asset optimization initiatives can thrive.
  • Cost control: Immersive technologies have immediate cost benefits in addition to the extensive savings that come from improving asset reliability, efficiency, safety, and reducing downtime. In one example, Advanced Technology Services (ATS) observed that instead of having an original equipment manufacturer (OEM) provide in-person maintenance support that would have required two full days (16 hours) at $250 per hour, enabling remote OEM support using AR integrated with Microsoft Teams saved the manufacturing plant thousands of dollars.
  • Recruiting: Modern maintenance technologies and processes are appealing, particularly to the younger generation, and highly beneficial in recruiting and retaining talent. AR, VR, and MR help to close maintenance skills gaps by enabling the development of a global technical workforce that communicates as if they were all present locally. Lessening the dependence on the experience of individual technicians is more important than ever with the rapid advancements being made in manufacturing technology. Advanced controls, sensors, networking functions, and robotic processes are just the beginning as the industry moves toward Industry 4.0.

Tooling for Future Factories and Industry 5.0

The factories of the future are smart. According to PwC, one-third of manufacturers have already embraced smart factory technologies, and the numbers are set to grow even more in the next few years. The statistics speak volumes about the significance of maintenance technology in modern manufacturing.

Immersive reality is among the technologies leading the way to the next industrial revolution. With immersive technologies gaining momentum, Gartner’s prediction that 75 percent of large enterprises will integrate AR and VR in the next few years emphasizes the importance of staying ahead of the game for manufacturers.

Take the Plunge

Thanks to innovative, transformative solutions such as immersive technologies, maintenance technology will continue to play an outsized role in strengthening modern manufacturing. But with so many compelling devices and virtually unlimited applications to choose from, assessing, prioritizing, and implementing solutions that will have the greatest effect is challenging without the right know-how.

To simplify the process and expedite the rewards, look for an industrial technologies service partner like ATS that has extensive experience in the latest industrial technologies and maintenance and reliability expertise to help manufacturers improve uptime and save costs. Partnering helps factories keep their equipment running reliably and efficiently when internal resources are limited, and it facilitates the adoption of digital transformation to give manufacturers a competitive edge.

About the authors:

Chis LeBeau, ATS

Chris LeBeau is the Chief Technology Officer at Advanced Technology Services. He works with industrial maintenance experts and technology leaders to enable Industry 4.0 strategies and maximize the value of technology to manufacturers. 




Micah Statler is the Director of Industrial Technologies at Advanced Technology Services. He is responsible for the strategy, execution and delivery of technology-driven maintenance solutions. 


ML Journal October 2023

The Industrial Metaverse May Be Closer Than You Think

Nearly 80% of manufacturing executives seem confident that the metaverse will transform aspects of manufacturing in the next five years, a new Deloitte/MLC study reveals. 

Executives say that the Industrial Metaverse offers new ways to solve a variety of pressing challenges they face in the near term.
Attracting and retaining top talent and building resilience and visibility in supply chains are top goals.
Among the chief challenges with the Industrial Metaverse are cybersecurity, data protection and IP, brand, and safeguarding personal information.

In May 2023, Deloitte and the Manufacturing Leadership Council (MLC) embarked on a study to better understand the industrial metaverse and its applications in manufacturing. This article presents some of the key highlights from the resulting publication “Exploring the industrial metaverse” (referred to as “the study” in this article), including findings that the majority of manufacturers surveyed are already progressing on their industrial metaverse journey and are deriving benefits from even partial adoption.

A Paradigm Shift

The industrial metaverse is the convergence of individual technologies that, when used in combination, can create an immersive three-dimensional virtual or virtual/physical industrial environment. As technology evolves, the industrial metaverse will likely allow access to these immersive 3D environments from any internet-connected device, including virtual reality (VR) and augmented reality (AR) devices, as well as smartphones, tablets, laptops, and equipment, from anywhere in the world.

A majority of manufacturing executives surveyed[1] are bullish on the potential of the industrial metaverse in the near term. More than 70% of surveyed executives believe that in the next five years it will have a high rate of adoption in the manufacturing industry. Nearly 80% are confident that the metaverse will transform R&D, design, and innovation and enable new product strategies.[i]

Surveyed executives generally agreed that the industrial metaverse offers new ways to solve a variety of pressing challenges they face in the near term, such as attracting and retaining top talent, and building visibility and resilience into their supply chains (figure 1). They tend to view the industrial metaverse as a pathway to future value realization through improved new product introduction rates and new customer experiences and services. Respondents also expect a broad positive impact across the business and are confident that the industrial metaverse will improve key business outcomes such as competitiveness, market share, revenue, and costs, among others.

However, the study results indicate that manufacturers aren’t just betting on the future, they seem to be building it. Some respondents shared that they have already made significant investments in metaverse initiatives, and nearly three quarters plan to increase their investments over the next 1–3 years (figure 2).

Building on Smart Factory Momentum

Through digital transformation, smart factory solutions have generally allowed companies to collect important data from their processes, products, assets, and operators and perform advanced analyses to generate valuable insights, and then augment human intelligence with machine intelligence to implement significant and sustainable improvements. These advancements have resulted in greater asset efficiency, enhanced product quality, reduced costs, and increased safety and sustainability.[ii]

In a previous Deloitte paper that focused on how manufacturers can derive value from smart factory technologies,[iii] four primary ecosystems were introduced: production (quality sensing, factory asset intelligence, product development, etc.), supply chain (supply network mapping, digital warehousing, control towers, etc.), customer (aftermarket services, virtual product experiences, etc.), and talent (recruiting, training, etc.).[iv] Within the production ecosystem, a set of eight use cases were introduced, aptly named the “Great 8,” as the most prevalent use cases for smart factory technologies that manufacturers are operationalizing. Because of the scope of what the industrial metaverse can offer—connection to data-rich, immersive 3D environments from anywhere there is a broadband internet connection—its potential value stretches far beyond just the production ecosystem and the Great 8 use cases.

The manufacturing industry appears well-positioned for the adoption of the industrial metaverse. Given their continued focus on digital transformation and their journey toward the smart factory, the majority of companies surveyed have made significant investments and are already using the foundational technologies that power the industrial metaverse. Companies are generally either implementing technologies like data analytics, cloud computing, AI, 5G, and Internet of Things technologies across multiple projects and processes, or they are currently experimenting with one-off projects (figure 3). The same is true for digital twins, 3D modeling, and 3D scanning, which can all serve as building blocks for the immersive 3D environments of the industrial metaverse.

The study shows that not only do most manufacturers seem to have a strong technology foundation in place, many of the surveyed respondents are already combining and leveraging these technologies today to implement industrial metaverse use cases and create value.

Manufacturers Appear to be Driving Toward Adoption

Nearly all (92%) of surveyed executives said that their company is experimenting with or implementing at least one metaverse-related use case and, on average, they are currently running more than six. Building on their smart factory efforts and leveraging the foundational technologies already in place, the production ecosystem was the most common for use case implementation, with more than one-third of respondents already integrating metaverse technologies, followed by the customer, supply chain, and talent ecosystems (figure 4).

Respondents then shared the primary use cases they are implementing using metaverse technologies. The study provides the complete details about these use cases, including their definitions, prevalence of implementation amongst surveyed respondents, the primary benefits derived, and some examples of use cases in action. Process simulation and real-time monitoring/digital twin were the two most common use cases overall, and the remaining production-focused use cases were also prevalent. Immersive training ranked third, followed by supply chain management and immersive customer experiences, demonstrating a healthy distribution of use cases across the talent, supply chain, and customer ecosystems.

The use cases and case examples that companies have reported seem to demonstrate that manufacturers are deriving value today from implementing industrial metaverse initiatives. However, they may still feel that there are challenges and risks to overcome to move toward its full adoption.

Cyber, Data Protection Are Important Risks

Cyberthreats are pervasive and can have a disastrous effect on a company if not properly mitigated. Implementing the industrial metaverse will likely bring new challenges since it derives its unique power from making proprietary 3D data about parts, products, facilities, etc., available to a variety of internal users, customers, and suppliers. It does this by allowing users to access the data through a myriad of interaction technologies over the internet, such as AR/VR devices, tablets, and phones.

One executive mentioned that because the metaverse will require significant data management; data protection, privacy, and security is a concern.[v] In fact, more than 70% of the respondents agree that cybersecurity is one of the greatest risks associated with implementing metaverse-enabling technologies (figure 5). Rounding out the top four are respondents’ concerns about protecting data and IP, brand, and personal information, all of which can be compromised in a cyberattack.

Digitalization has required manufacturers to drive collaboration between informational technology and operational technology to create an effective cybersecurity approach.[vi] Companies should develop capabilities to identify and address risk in an information technology (IT)–operational technology (OT)–interaction technology (ET) environment. One executive explained that his company is working to establish OT security capabilities and standards for how to review equipment efficiently, following the company’s IT policies, so that the operations and engineering teams can more quickly pilot and implement new equipment. This includes ET, especially since they are typically low cost (<$5,000) and don’t rise to the same level of review priority for IT as, say, a muti-million-dollar software package.[vii]

While cybersecurity risk may increase with the industrial metaverse, the study suggests that manufacturers generally believe the value it will deliver outweighs the risk, especially with the right mitigation strategies in place.

Unleashing the Power of the Industrial Metaverse

The 2023 Deloitte and MLC Industrial Metaverse study indicates that manufacturing executives seem not only confident that the industrial metaverse may hold great promise for the industry – some are already taking what’s next and transforming it into what’s now. In many cases, they are driving forward with metaverse use cases and appear to be deriving significant value across the organization. The study identifies a three-pronged approach that can be used by a broad spectrum of companies to identify, initiate, and scale industrial metaverse initiatives.

About the authors:

Paul Wellener is a Principal within the US Industrial Products & Construction practice with Deloitte Consulting LLP. He has more than three decades of experience in the industrial products and automotive sectors and has focused on helping organizations address major transformations.



John Coykendall is a vice chair, Deloitte LLP, and the leader of the US Industrial Products & Construction practice. John has more than 25 years of consulting experience focusing on global companies with highly-engineered products in the A&D, Industrial Products and Automotive industries.



Kate Hardin Deloitte

Kate Hardin, executive director of Deloitte’s Research Center for Energy and Industrials, has worked in the energy industry for 25 years. She leads Deloitte’s research team covering the implications of the energy transition for the industrial, oil, gas, and power sectors.



John Morehouse is the research leader for industrial products manufacturing in the Deloitte Research Center for Energy & Industrials. He has over 25 years of experience in manufacturing-related roles in industry, academia, and government.


David R. Brousell
is the founder, vice president and executive director the MLC.

[1] On behalf of Deloitte and the MLC, an independent research company conducted an online survey of over 350 senior executives in the US manufacturing industry in May 2023. The survey findings were supplemented by a series of executive interviews with technology leaders in the industry conducted in June 2023.

[i] Deloitte analysis of the Deloitte and Manufacturing Leadership Council (MLC) Industrial Metaverse survey, 2023.
[ii] Deloitte, “Smart Factory for Smart Manufacturing,” accessed August 18, 2023.
[iii] Paul Wellener et al., Accelerating smart manufacturing, Deloitte Insights, 2020, p. 6.
[iv] Ibid
[v] Insights gleaned from manufacturing executives’ interviews conducted in June 2023.
[vi] Ibid
[vii] Ibid

ML Journal October 2023

Scaling in the Fourth Industrial Revolution

How U.S. manufacturers can become technological front-runners.  

The future of manufacturing is digital, yet despite the influx of capital investment in U.S. manufacturing recently, many U.S. manufacturers still struggle with breaking through the performance ceiling of analog operations to achieve industry-leading operational performance.
One big barrier for U.S. advanced manufacturing is limited operational scale. For U.S. companies to compete, they need to begin with scale in mind.
There’s much U.S. manufacturers can learn from how “Lighthouses” are deploying transformative technologies to transform their own factories into Lighthouse-level powerhouses — including network-level thinking that must be called for by CxO leaders.    

Adopting and scaling digital technology in manufacturing has become increasingly urgent in today’s dynamic and highly competitive manufacturing landscape. Five years ago, the struggle for factories scaling digital lay in getting out of the gate. In 2019, 70% of manufacturing companies we surveyed named “pilot purgatory” as their biggest barrier to realizing the benefits of digital. Today, the Global Lighthouse Network — a World Economic Forum initiative co-founded with McKinsey & Company — has identified 132 global factories, representing 80 companies, that have escaped pilot purgatory and achieved industry-leading operational performance.

These plants, in industries ranging from aerospace and medical devices to food and car manufacturing, show us that at least 80 companies have figured out the “lean + digital” blueprint needed to break through the performance ceiling of analog operations. On average, site-level improvements achieved by these Lighthouses include 10-25% reductions in production cost, 20-50% increases in productivity, and 30-70% reductions in lead time. Now, these same 80 companies are well underway in scaling these benefits across their full production networks.

With such levels of savings and productivity gains on offer, and as more manufacturers see peers like those in the Global Lighthouse Network accelerating digital transformation efforts, it’s no surprise that 89% of respondents to the NAM’s 2022 transformative technologies survey expected their company’s rate of adoption of M4.0 technologies to increase over the following two years.

Five years ago, the challenge was in getting from pilot to factory. Today, the goalposts have moved: companies across the globe, including many within the Global Lighthouse Network, are focused on scaling transformative technologies from factory to network.

U.S. Manufacturers Need Advanced Manufacturing

The U.S. is experiencing an unprecedented manufacturing boom on the back of favorable domestic policy incentives and geopolitical trends. In the past year alone more than 100 new factories have been announced in the U.S., including more than 50 semiconductor and electric vehicle factories and more than 60 greenfield announcements across other clean technology verticals. Together, these represent a doubling in planned capital investments since 2022, including $166 billion in announced investment in semiconductor and electronics manufacturing. Often, policy incentives are a core part of the business case for these new factories.

“Five years ago, the challenge was in getting from pilot to factory. Today companies across the globe are focused on scaling transformative technologies from factory to network.”

Policy incentives won’t last forever. According to McKinsey analysis, for U.S. manufacturers to be competitive in the long term across new or near-shored goods, manufacturing cost reductions of 30-40% will be needed, likely through strategic deployment of advanced manufacturing technologies. And with such technology critical to the long-term competitiveness of the U.S. manufacturing base, it’s concerning that only 11 out of the 132 sites in the Global Lighthouse Network are in the U.S.—a number which underrepresents U.S. contribution to global manufacturing GDP by a factor of two.

Why Are so Few Among the Technological Elite?

First, talent — and especially digital talent — is expensive. The most talented technologists are those likely to be capable of the biggest and most transformative impact, and they are also likely to be well aware of their skills’ market value. Against big-city tech and finance companies able to offer high pay, rapid advancement, and flexible working models, manufacturers will need creative solutions. Their traditional hiring approaches, evolved in a different talent environment, often require in-person work in smaller towns — with unclear career paths because the existing base of digital roles has been so small.

Furthermore, upskilling workers for the challenges and opportunities offered by digital transformation is a muscle few manufacturers now have. Building it will be increasingly critical as more U.S. manufacturers find themselves without enough — or the right — skills to grow or digitalize. This will only be amplified by 2030, as manufacturers face:

  • An increasing shortage of workers. As of January 2023, there were 803,000 openings for manufacturing roles – a number projected to swell to more than 2 million + by 2030.
  • A rapid loss of industry knowledge. Over a quarter of the U.S. manufacturing labor base is composed of workers over the age of 55, and attrition rates remain as high as 40%.
  • An accelerating skills shift. Digital manufacturing requires a different set of skills than analog approaches. According to analysis by the McKinsey Global Institute, by 2030 the U.S. manufacturing sector will require 259,000 more engineers, but 479,000 fewer production workers—resulting in transitions for around 1 million workers, or roughly 8% of the workforce.

Finally, the deployment of U.S. advanced manufacturing is limited by operational scale. US factories tend to be smaller, both in physical and workforce size, than at comparable sites in China, for example, with far fewer employees per U.S. factory. Digitalizing a facility carries several one-time costs that are incurred whether a facility is large or small, such as setting up cloud infrastructure. In the U.S., there are smaller economies of scale to overcome these fixed costs of site-specific digitalization programs — it is less economical to embed the tech teams needed to support transformation at smaller, more numerous sites.

“We can learn from how Lighthouses are deploying transformative technologies, which primarily focus on business needs.”



Together, these points help to explain why the U.S. has comparatively few manufacturing Lighthouses. At the same time, we can learn from how Lighthouses are deploying transformative technologies, which primarily focus on business needs such as productivity via workforce augmentation, matrix manufacturing via cross-site scheduling, and throughput maximization via digital twins and predictive maintenance.

Focus on Network-Level Strategy

If scale is part of the challenge for transformative technologies in U.S. manufacturing, then the solution must be to start with scale in mind. Siemens Digital Industries provides a good example of the possibilities. Its two Lighthouse factories serve as anchors for a network-level digital implementation strategy. Like every other factory in the Siemens network, each of these two sites maintains its own strategic posture for digital innovation. That allows some sites to prioritize factory operations improvements, for example, while others can focus on end-to-end connectivity.

Yet at the same time, common resources mean that use cases, once proven, can be rapidly deployed across the network. All sites use a modular reference architecture that lets use cases plug in easily, with central digital teams providing expertise for local implementation. This approach to scale can make the economics more palatable than the one-factory-at-a-time transformation model — which in the U.S. too often simply can’t be done.

A Role for Leaders

CEOs and their CXO colleagues are the ones who can elevate the vision to network levels, rather than dwelling on site-specific plans, delivering real-world benefits of better customer experiences, lower costs, and accelerated innovation. And they have the playbooks to help them do it, built from evidence, data, and analysis from hundreds of digital transformations. They are the ones to get the board on board with such long-term, capital-intensive journeys, and to dedicate time and resources to making the necessary talent and capability upgrades.

“CEOs and their CXO colleagues are the ones who can elevate the vision to network levels, rather than dwelling on site-specific plans.”



  • Invest in capabilities with a longer time horizon. Our analysis finds that the best-performing digital manufacturing sites are those that “go slow to scale fast,” spending a year or two strategically building a data and technology foundation for deploying use cases and training analytics models. Once established, some Lighthouses deploy a dozen or more use cases in weeks, not months or years. Four years into its digital transformation, one Lighthouse company deployed a standard operating procedures (SOP)-interfacing chatbot for workers in just 1.5 weeks.
  • Cultivate a tech-forward workspace for eager technologists. Attracting and keeping the right profiles will require innovative workspace models that exemplify the tech-forward future of work — providing fast-paced, problem-solving heavy environments with significant real-world impact and clear pathways to learn and advance. Because this talent can be expensive, the economics may work only by keeping network scale in mind.
  • Build scale into the business case from day one. Though most use cases will need to be piloted within a specific site, this is just the first step in a plan to achieve end-to-end value. Such solutions are most effective when they address critical bottlenecks across the production network, such as cross-site production scheduling for matrix manufacturing. Likewise, factory-specific use cases, such as vision systems, can be designed to address business needs that a majority of the production network also faces. Think too small, and the ROI dwindles.

The future of manufacturing is digital. To compete, U.S. manufacturers can start with scale in mind. This requires network-level thinking that must be called for by CxO leaders.  M

About the authors:


Rahul Shahani co-leads McKinsey’s Industry 4.0 efforts in North America including the Innovation & Learning Centers and focuses on digital manufacturing transformation design and execution. He is a partner in McKinsey’s Operations Practice, based in New York.



Dan Swan co-leads McKinsey’s Operations Practice globally, and helps manufacturing and service companies transform their operations performance and capabilities.




Henry Bristol focuses on Industry 4.0 technology adoption strategies for industrials, electronics, and new energy manufacturers. He is a Fellow with the World Economic Forum and an Engagement Manager in McKinsey’s Operations practice.

ML Journal October 2023

How a Private Equity Approach Can Drive Auto Suppliers’ EV Transition

By adopting a PE lens, companies can transition more smoothly from legacy internal combustion engine business to the EV market. 

The $1.9 trillion global auto supply sector will experience a shakeout in the changeover to EVs, and the slowest movers risk losing opportunities or bankruptcy.
Companies that have hesitated to initiate change can benefit by assessing future options using the relentless, enterprise-value based approach of private equity firms.
Lessons from early movers highlight the value of planning the funding, rigorous execution and governance, and workforce transformation.  

The global automotive industry is undergoing existential disruption as the world market embraces electric vehicles (EV) over internal combustion vehicles and hybrids. At this point—in the early stages of the transition and with a long way still to go—some challenges facing the diverse auto parts supplier industry are becoming clearer; however, many companies are still figuring out how quickly to embrace those changes.

The market transition to EVs is gaining momentum due to advances in technologies such as batteries and charging stations, and growing buyer interest thanks to the success of Tesla and other electric models. The U.S. and other governments are doubling down on commitments to transition to EVs. Recently proposed federal emissions limits in the U.S. would effectively require two-thirds of cars sold in the country to be electric by 2032; California will require that all new vehicles sold in the state after 2035 be zero-emission vehicles.

Parts suppliers—from gas tanks to fuel injectors—for traditional cars are already seeing their market shrink, and the decline will only worsen. This $1.9 trillion industry will see a painful slowing of growth in coming years as internal combustion engine (ICE)-based product lines are phased out. About one quarter of profits currently generated from legacy ICE components will be the most adversely affected, resulting in a 50 percent decline from current levels by 2030. For some suppliers, the market will eventually resemble a game of musical chairs, with fewer and fewer safe economic places to land—and the slowest movers losing the game.

Segments of the ICE market will remain, such as parts for existing vehicles, and suppliers can continue to earn some revenue in this area long after production of new ICE models is phased out. Also, commercial vehicles have lagged passenger cars in the EV transition, so far, and are likely to offer another “long tail” of continuing opportunity for parts makers.

“About one quarter of profits currently generated from legacy ICE components will be the most adversely affected, resulting in a 50 percent decline from current levels by 2030.”


The impacts are hitting auto suppliers in different ways and at different velocities, depending on the parts they make and the size of the company. For example, many larger makers of powertrains and other ICE components, whose production lines require long development lead times to transition, have embraced the need to change. They are developing transformation strategies and making operational changes, announcing new manufacturing strategies, new partnerships, and new product lines. Public companies also benefit from the involvement of boards of directors and stockholders that push them to adopt transformation strategies earlier while balancing risks.

In contrast, small, medium and privately held companies, with traditional, risk-averse management styles and fewer resources tend to lag. Companies that continue to delay adoption of an operational transition strategy may soon find themselves at risk.

Julie Fream, president and CEO of the Motor & Equipment Manufacturers Association – Original Equipment Suppliers (MEMA), says that one of the biggest challenges companies face is estimating when the growing market for EV products will overtake the ICE components they are replacing. This uncertainty is causing many executives to hesitate as they plan their company’s operational transition from ICE to EV.

“Suppliers know they need to be able to do both, but they are asking, ‘How do I know the volumes?’” Fream says. “How do they manage the crossover point, knowing that you can’t accurately predict at this point where the two lines will cross—where EV becomes dominant, and ICE becomes a secondary product line. That’s the question they are all asking.”

Companies that invest in EV too early could end up financially overextended, with excess capacity and a market that is not ready for their new products. Late arrivals could lose opportunities completely if faster competitors beat them to the punch.

Companies seeking ways to exit ICE businesses also face challenges as certain, carved-out legacy ICE assets will become steadily less attractive to buyers. Without successful consolidation or divestiture, many will be forced to shut down, particularly if cost cuts and efficiency improvements cannot keep pace with commercial declines. At the same time, suppliers wishing to embrace new technologies must invest heavily in new product and service innovations targeted at EVs. Often, these competing priorities absorb cash while also preventing the bold action that is needed in today’s market.

The Private Equity Mindset as Transformation Catalyst

Many auto suppliers, especially those that are small or mid-sized and often privately owned, have relied successfully on a traditional management approach of incremental improvement and risk management. This approach, however, is ill-suited for the current market upheaval. Tomorrow’s winners will innovate and reinvent their whole business, embracing their initial entrepreneurial spirit.

Fortunately, a ready model for bold decision-making exists that can serve as a model for the aggressive approach these companies will require—that of the private equity (PE) investor. Two aspects of the PE mindset stand out as the essential, outside-investor viewpoints that many ICE suppliers urgently need:

  • The ability to make bold decisions, without attachment to unprofitable “sacred cows”; and
  • A relentless focus on enterprise value.

For companies accustomed to incremental change that need to become unstuck, the PE lens can be a catalyst for the swift cultural makeover needed to get moving.

“The PE mindset is something many suppliers should consider,” says Fream. “There’s a lot of money on the sidelines right now that could eventually come in. This would create some opportunities if you accurately analyze your business and understand what needs to be done.”

“For companies accustomed to incremental change that need to become unstuck, the PE lens can be a catalyst for the swift cultural makeover needed to get moving.”


This is not easy, she notes: “Suppliers need the skill set to assess what’s happening in the marketplace. The PE firm is ultimately trying to drive the company to be more focused on the marketplace. Generally, they don’t tolerate anything outside of that. But this can be very difficult for some suppliers, especially smaller, privately-held companies.”

Typically, PE has a variety of strategic approaches to generate profits from companies in either fading industries or startups in new technology fields. PE investors can improve enterprise value and prepare an asset for profitable sale through organic upsides—topline growth and bottom-line improvements—as well as inorganically, through a potential portfolio play (Figure 1).

Figure 1: Private equity value creation approach


Leadership can use the PE agenda to conduct a data-based assessment of how their business creates value today and its potential future role in the developing EV ecosystem. Based on this deeper understanding, leaders can adopt and implement a transformation strategy that aims to maximize enterprise value, as highlighted in the 2022 EY article “How auto suppliers can navigate EV technology disruption in four steps.”

Identify the Appropriate Operational Strategy Using the PE Approach
Not all traditional product components are expected to decline in the near term. Many product lines can be suitable candidates to pivot to other markets, and some can continue as sources for income that can be invested in new EV product development. Strategic approaches for how companies are managing their existing ICE businesses can be grouped in three broad categories:

  • Exit (cash out and focus): One clear option is for suppliers to wind down or sell their ICE products businesses to a buyer that can squeeze value from them, for longer, than the original owners can. The spin-off strategy can reduce exposure to the declining demand for ICE products and can raise capital for reinvestment in new market opportunities.
  • Consolidate (double down): Suppliers can prepare for the coming shakeout by consolidating existing ICE-only businesses to create synergies and scale so they can continue to derive value from their operations for as long as possible, with the option to continue to operate them alongside new EV businesses.
  • Pivot (parallel pursuit): Companies can wind down their ICE businesses according to a clear step-down plan, while pivoting product lines to new EV products. This may require massive investment in R&D, innovation, and digitization to develop new EV products and services. It could include acquiring EV startups or niche players to expand product portfolios and acquire new skills or technologies.

The Transformation Journey So Far: Signs of Success and Warnings

Quite a few original equipment (OE) suppliers have identified enterprise EV transformation strategies and begun to execute them. Based on these OE suppliers’ track records, other suppliers can ascertain the challenges of redesigning value chains or operating models.

Example: A Supplier Divests ICE Divisions and Invests the Proceeds in EV Partnerships
A German powertrain company with ICE and EV products that was spun off in 2021 is following a two-part strategy of divesting its remaining internal combustion divisions to help fund its growing EV business, which is focused on EV innovation and growth through investments and partnerships.

“Companies must take care to minimize the impact on employees. . . . Employees should have the option of transitioning to new business roles, where possible. Companies should develop and activate a clear engagement strategy for unions early in the process.”


The company is selling its catalyst and filters operation and intends to divest its ICE division, which has been profitable since 2021 after years of losses. Meanwhile, it is bolstering its EV business through partnerships with automakers to jointly develop power electronics and electronics companies for access to semiconductors. The company’s electronic mobility business has significant orders from Hyundai and is expected to break even in 2024.

Enablers of Transformation: Risks and Opportunities

Once organizations have determined the strategic way forward, they will face significant challenges in executing the agreed-upon roadmap. The experiences of companies that are already making progress, coupled with recent EY work with automotive supplier clients, can make it easier to identify which strategic enablers companies will need to carry out the transition, as well as useful observations and lessons (Figure 2).

Figure 2: Strategic enablers

Rigorous Execution and Governance

Companies will need to review and adjust their governance so they can manage a rapidly growing, subscale new business and another business in slow decline. Many larger companies are opting to carve out their legacy EV business into a separate division.

Cash flow must be tightly monitored as, given the likely scale of costs, budgeting can be extremely sensitive to small changes in assumptions. Companies should implement robust short- and medium-term forecasting processes during the transition period where cash flow may be tight.

Active portfolio management is equally critical, so that only products with profit potential continue to receive investment. Companies should tier their governance to match the investment and uncertainty levels and to ensure they maintain appropriate controls. They should also take care to avoid excessive reporting and bureaucracy, which can steal oxygen from innovative new ventures.

Workforce Transformation

Staff who will be transferred to new products will need retraining. In cases where companies require new skills that are hard to find within the sector, they can recruit from adjacent industries where employees have transferable skills. In addition, companies will require creative solutions to retain key staff from the legacy business, for as long as is required, to ensure a smooth transition.

Companies must take care to minimize the impact on employees. Open and transparent communication and change management are important, as communication vacuums tend to be swiftly filled with rumors and uncertainty and that can raise stress levels and harm operations. Employees should have the option of transitioning to new business roles, where possible. Companies should develop and activate a clear engagement strategy for unions early in the process.


Once a company sets its strategy, it needs to fund it, and change can be costly. New products will require expensive capital equipment purchases, and the new business is likely to lose money at first. Companies will need to write down legacy machinery and obsolete stock and take special care to avoid large customer and supplier claims.

Cost control will also be critical during this period, particularly given the long timeframes involved. It is important that companies understand the combined costs of legacy products being ramped down and new products being ramped up. They should do sensitivity analysis to stress-test this requirement, identify budgets, and fund them appropriately. Companies should establish and implement their strategy soon to avoid facing simultaneous closure and setup costs.  M

About the authors:

Joern Buss
is an EY-Parthenon Partner, Advanced Manufacturing and Mobility, Ernst & Young LLP. Focused on strategic growth, product technology, transformation, and turnaround, he supports manufacturing and mobility companies, alongside private equity and investors.



Jon Slatkin is an EY-Parthenon Partner, Turnaround and Restructuring Strategy, Ernst & Young LLP. He is an Ernst & Young – United Kingdom Strategy and Transactions Partner.



James Nicholson is an EY-Parthenon Partner, Advanced Manufacturing & Mobility, Ernst & Young LLP. He is one of the leaders in the EY UK&I Strategy practice. He helps clients plot successful paths to growth, adopt new business models and evolve their core capabilities and global footprints.



Kevin Rebbereh is Director, Automotive Strategy, EY-Parthenon GmbH. He is a director with EY-Parthenon in Hamburg, Germany and  is in the leadership team of the automotive strategy practice in Europe West.



Lloyd McRitchie is EY-Parthenon Partner, Turnaround and Restructuring Strategy, Ernst & Young LLP. He is a partner on the Turnaround and Restructuring Strategy team.


ML Journal October 2023

Dialogue: M4.0 Leadership from a Digital Champion

Johnson & Johnson’s Bart Talloen shares his thoughts on meeting and exceeding customer expectations, supply chain as a competitive differentiator, and why manufacturing leaders need to focus on curiosity, continuous learning, and shaping the ecosystem of the future. 

Recently named the Manufacturing Leadership Awards’ Manufacturing Leader of the Year, Bart Talloen Vice President, Operational Services and Standards for Johnson & Johnson, has been instrumental in leading an innovation-driven shift in J&J’s supply chain. He has had a significant impact on J&J’s standing as a global leader, as demonstrated by the company’s record 11 lighthouse designations from the World Economic Forum.

In this interview he discusses how technology plays an essential role in exceeding customer expectations, the importance of upskilling and training the leaders of the future, and why it’s necessary for every leader to build and leverage a diverse, far-reaching network of internal and external collaborators.

Q: You have been with Johnson & Johnson for 27 years. How has your role evolved during your career there?

A: For 19 years I had different operational supply chain roles with increasing responsibility in our J&J pharmaceutical, over the counter and consumer businesses. This included engineering, planning, manufacturing operations and general end-to-end supply chain management in Europe, Asia, and North America. I have also acquired and divested business operations, built five and closed three manufacturing facilities and was also responsible for the supply chain during the successful execution of a consent decree for McNeil Consumer Healthcare, J&J’s U.S. over-the-counter business.

For the past eight years I have been responsible for J&J supply chain strategy, driving innovation and overseeing large-scale transformation programs encompassing all three of J&J’s business sectors. The overarching evolution of our supply chain is going from a focus on cost and operational excellence to making supply chain a business enabler and competitive advantage. My role has evolved accordingly, from initially building and deploying supply chain strategies that were centered around foundational improvement capabilities such as lean, Six Sigma, and operating systems to bolder strategies built on major capability transformation programs. This includes technology, go-to-market models and channels for access to care, such as supporting outpatient clinic-based settings and telemedicine, as well as customer centricity and personalization.

In the last couple of years I have also been focused on technology and digital innovation, next-generation customer enablement solutions to drive differentiating experiences and outcomes as well as the whole value chain from suppliers to customers.

Q:  What most excites you about the role you are in now?

A: It is really about the difference we make as supply chain for our customers, which is twofold:

One is that we are building and deploying cutting edge customer strategies and solutions that transform the experiences and outcomes for the customers and patients that we serve every day. One example is a solution called Advanced Case Management, which uses case schedules and patient data to manage inventory at the point of consumption – ensuring we have the right orthopedic implants for the right patient at exactly the right time. Another example is an autonomous order fulfilment and inventory management system deployed in hospitals, which is supported by an AI engine that suggests operating room improvement opportunities, simplifies and automates healthcare practitioner work, and helps reduce inventory and logistics costs. Connecting that to our supply chain planning enables real-time alignment of the supply to the demand from hospitals.

Second, risks are no longer an isolated event, they are interconnected. That is why we are moving toward multidimensional and proactive approaches to resilience. We are making significant advancements in supply chain resilience, enabling us to always provide our customers with the products and solutions they need whenever, wherever and however they are needed and expected, through whatever disruptions may happen. It strengthens our ability to consistently deliver products, providing confidence and assurance through times of uncertainty. And that is what our customers expect.

“We are strengthening our ability to consistently deliver products, providing confidence and assurance through times of uncertainty.”


Our proactive resilience capability is centered around a resilience engine that leverages data science and analytics for multi-dimensional risk evaluation. Combined with network and product-specific data, it enables improved velocity and quality of trade-off decisions at supply chain, product and network levels. This results in proactive risk identification and mitigation while ensuring continuity of our product supply.

Q:  Beyond innovation, what are some of the other main supply chain initiatives underway at J&J?

A: Currently there is a strategic focus on customer anticipation, resilience, and end-to-end supply chain orchestration. This translates into new supply chain capabilities we’ve been building and deploying such as advanced customer enablement solutions, smart operations with the adoption of advanced I4.0 technology innovation, end-to-end supply chain control towers to give us real-time visibility and tracking across the supply chain, and digital connectivity with our customers and suppliers. There is also a focus on proactive supply chain resilience and how we leverage digital capabilities.

At J&J, we’re also heavily investing in the development of our workforce. Some examples:                                                                          We are enabling a digitally connected and augmented workforce program, developed with a worker-centric mindset, built around four capability components: worker assistive applications, immersive and wearable solutions, intelligent automation, and hybrid ERP + cloud-based technology platforms.

Also, we’re upskilling our people using a high-touch model of leadership training that includes our Global Operations Leadership Development program, our Compass Executive Quality Leadership Development program, our Plant Leader Development Program, our STAR programs to provide action-learning experiences for emerging supply chain leaders, and an entry-level program for recent graduates. We extended the program to include a well-being component. Last year alone, 600 new participants were enrolled in leadership development programs, with 30 participants in GOLD and more than 25,000 participants in other training programs.

Additionally, we’re partnering across Johnson & Johnson with schools, universities, and external partners through sponsorship of our Women in STEM program, which aims to ignite the power of women inside and outside of our company.  By 2025, we want half of our global management positions to be occupied by women; we have already achieved this goal in Europe, the Middle East, Africa, and Latin America and are on track for the same in other markets.

Q:  J&J is the recipient of 11 lighthouse designations from the World Economic Forum for advanced manufacturing, the most of any company in the world.  What are the essential components to becoming this type of global leader?

A: First, it is important to have a digital strategy, driven from the top and supported by senior management, with laser-sharp focus on execution and implementation driving tangible business impact. This strategy needs to be driven by the customer and business needs, not based on what I call a “tech push,” meaning you are just pushing deployment of technology solutions and tools into the business vs. pulling tech solutions that help address customer and business needs or pain points.

“It is important to have a digital strategy with a laser-sharp focus on execution and implementation driving tangible business impact.”


Second, data availability and visibility are critical, but for many companies still a challenge given the complexity of operational infrastructure with a lot of legacy systems that don’t talk to each other. We have a thoughtful data strategy, IoT systems and a defined digital stack – a combination of digital products and platforms that help us scale digital solutions faster. We have created a data lake where the data from different operational systems is ingested and made available for the digital stack and ML/AI algorithms and apps to “work their magic.” This infrastructure has been very helpful and a critical component of our digital strategy and its successful implementation.

Third, an important enabler and differentiator is leveraging external partnerships to complement the internal J&J capabilities. Over the past few years, we have established a broad external collaboration ecosystem with academia, consortia, startups, other industry companies, suppliers, etc. We are leveraging these ecosystems to advance capabilities in tech innovation, influence the global Industry 4.0 agenda and drive tech progress across industries. These partnerships were built and came to fruition in our innovation hubs, which are based in global tech hotspots, and technology capability centers that are focused on specific Industry 4.0 technologies such as 3D printing, smart sensors and vision systems, advanced materials, advanced robotics and so on.

Q:  Additionally, you were just named the 2023 Manufacturing Leader of the Year at this year’s Manufacturing Leadership Awards Gala. What are the leadership skills and attributes you believe to be most important in the era of Manufacturing 4.0?

A: It is of course important that leaders have a good understanding of Manufacturing 4.0 technologies, how they can be applied and the value and impact they can bring for your business. But in my opinion, there are three leadership characteristics, and differentiators, that are the most important:

First, supply chain leaders of the future will be more like “ecosystem orchestrators” vs. “managing a supply chain in a particular company.” To do this, you first need an understanding of the customers and markets you serve – an outside-in perspective that you can use to shape your strategy. Additionally, it’s about realizing that no one person or company can or should do it alone. If I take maintaining business continuity as an example, it takes a network of suppliers, governmental agencies, companies, third-party logistics providers and so on all working together. It’s about building those networks, internally and externally, and doing it with a focus on where the expectations of your customers are evolving.

Second, it is also critical for leaders to be creative and curious. Seek continuous learning and think about new ways to work together across functions and reach out to business partners to learn more about how you can work together.

“Supply chain leaders of the future will be more like “ecosystem orchestrators” vs. “managing a supply chain in a particular company.””


Third, keep seeking connections not just within your organization, but externally as well. We have to be intentional about cultivating professional relationships. This could include attending forums and conferences, joining industry associations and consortia, or creating collaborations with other companies inside or outside of your sector and/or suppliers. All these relationships and connections invite diverse perspectives and encourage thought sharing. For J&J, this offers better insight into what patients and customers want and need from us, but also provides insight into other industries and how they’re enhancing operations, innovating and engaging with their customers and deploying advanced tech solutions and innovation. It is important to approach collaboration broadly and holistically to get the most out of it – and it is dynamic; you are never done.

Q:  What do you feel is the most important lesson you’ve learned in your career so far?

A: Phenomenal things can be achieved via technology, but in the end it is all about the people. We can have the right process, the right technology and systems, but it is the people that are the differentiator. You need the right leadership – bosses that trust and believe in you. You need the right employees to get the job done, and you need the right peers and collaborators because you cannot do it on your own. I have seen this play out in my career time and time again.

Q:  What do you foresee as manufacturing’s greatest challenges and opportunities in the years ahead?

A: I believe there will be three great challenges:

One, making manufacturing an interesting place to work and a magnet for current and future talent will be key. So, we need to continue to enable and develop our workforce with regards to future capabilities and skills, talent acquisition, retention, employee engagement and development, culture, and so on. In the current dynamic environment and competitive labor market this will need strong attention and will be a critical success factor for manufacturers.

Two, having a robust data strategy, data availability and visibility isn’t easy for most companies given their complex system infrastructures and legacy systems. But it is critical to crack this nut to be successful in the digital era.

Three, supply chain resilience. The unpredictable and volatile world of today requires that we evolve from the traditional supply chain risk management and isolated risk approach to a holistic multi-dimensional and pro-active resilience approach.

The greatest opportunity I see is for manufacturing leaders to become end-to-end orchestrators vs. just leading the operations within the four walls. The differentiator to accelerate your value creation and impact is through holistic collaboration and by leveraging an ecosystem of partners. Manufacturing leaders need to own, shape, and drive this ecosystem.  M

FACT FILE: Johnson & Johnson
Headquarters: New Brunswick, New Jersey
Industry Sector: Diversified healthcare products
Annual Revenue (2022): $94.9 billion
Employees (2022): 155,800
Production: 97 manufacturing sites worldwide

Title: Vice President, Operational Services and Standards for Johnson & Johnson Services Inc.
Education: Katholieke Universiteit Leuven, Belgium; B.S. and M.B.A.
Previous Roles:
– Vice President, Strategy Innovation and Deployment, J&J Supply Chain
– Vice President, Supply Chain North America OTC, J&J Consumer
– Vice President, Supply Chain WW Nutritionals, NA OTC, Franchise Strategic Operations, J&J Consumer
– Vice President Consumer Supply Chain Asia Pacific, J&J Consumer

About the author:
Penelope Brown is Senior Content Director of the NAM’s Manufacturing Leadership Council.

ML Journal October 2023

AI’s Outcomes Rely On Its Rollout

If done right, AI will revolutionize everything from safety and quality to efficiency and maintenance 

● AI represents a unique opportunity to improve the efficiency of production, especially when it comes to preventative maintenance.
Integrating AI into production can increase cyber risk by creating new potential access points for bad actors.
More extensive training is required for AI to accomplish complex tasks, while repetitive tasks require less training.  

For centuries, manufacturers have faced the challenge of addressing safety hazards in factories while boosting efficiency and controlling costs. Now, many manufacturers are learning that artificial intelligence (AI) can help them make new strides when addressing protracted worker safety and efficiency challenges. Consequently, it is no surprise that 36% of manufacturers say they will pursue Industry 4.0 investments according to the 2023 BDO Manufacturing CFO Outlook Survey. As more manufacturers explore AI’s future and how it can help them improve safety and efficiency, it is critical that they set themselves up for success by securing employee buy-in and preparing factory infrastructure to take full advantage of AI-based systems.

Today, much of the public has become aware of generative AI, such as OpenAI’s ChatGPT, with its ability to quickly answer complex problems with written prompts. Generative AI is exciting to the general public, but many plant managers are passionate about AI systems that are specifically designed to operate in a manufacturing setting. Rather than only using written prompts, AI systems used by manufacturers leverage inputs from computer vision, lasers, and other sensors to predict when a safety issue may occur, which robot caused a manufacturing defect, and how machines should be calibrated to minimize downtime.

Leveraging Technology to Combat Safety Hazards

Despite decades of regulations and improved protocols, safety issues persist in modern manufacturing. Updated safety codes and maintenance plans cannot always prevent worker injuries that result from negligence or unpredictable hazards in a manufacturing facility. While strong employee training programs remain one of the best ways manufacturers can improve safety in settings from chemical plants to automobile factories, even the most dedicated employees are not constantly vigilant to workplace dangers. This is why many manufacturers are investing in AI-powered safety systems, which can predict when and where a safety violation could happen, allowing plant managers, and sometimes the AI itself, to remedy the situation before it becomes irreparable.

However, to work successfully with AI, workers need to be trained to interact with these new systems.  The latest advancements in augmented reality (AR) and virtual reality (VR) technologies enable companies to make training programs for employees who interact with AI-powered safety systems even more effective, further empowering workers to keep themselves safe. AR or VR simulations can give employees a sense of what it is like to work with potentially dangerous machinery in preparation for operating the real equipment. Workers can also practice carrying out the necessary safety procedures before entering a secure area or shutting down a robot in need of repairs on the factory line. These AR- or VR-enabled training exercises can reduce the likelihood of mistakes, which could result in worker injury or damage to machinery.

“Many manufacturers are investing in AI-powered safety systems, which can predict when and where a safety violation could happen”



While AR and VR training can help workers keep themselves safe, these simulations can also teach factory employees about AI systems designed to improve safety. Many workers have never interacted with AI systems tasked with actively reinforcing safety guidelines before and may be inclined to ignore or distrust safety alerts that do not come from a person. AR and VR training can incorporate scenarios where a machine breaks down, or a co-worker fails to follow safety protocols to teach employees about which hazards an AI safety system can detect and how they should respond.

Detect Issues with Computer Vision

One of the main methods AI safety systems detect potential hazards is through computer vision. This technology lets AI “see” the factory floor through cameras, allowing the AI to raise safety alerts in real-time if, for example, it observes employees who might not be wearing the proper personal protective equipment (PPE). In geofenced safety zones, a computer vision-enabled AI can shut down equipment, restrict access to certain areas, and alert employees if it sees or anticipates a safety concern. It is important to note that AI-based safety systems are not a replacement for traditional training. Rather, AI-enabled computer vision is an important tool that factory managers can use to make their plants even safer.

AI can also help manufacturers improve safety on an individual employee level with the help of data analytics. An AI system can keep tabs on the number of safety incidents an employee is involved with, like warnings or violations. Then, AI can take this data and factor it in with individual employee information to construct a holistic view on a worker’s safety record. This record can be informed by data like years of experience, hours worked, and metrics collected from a wearable device like body temperature, muscle strain, and the amount of weight lifted per shift by the employee. Manufacturers can use this to better understand if their employees are under too much strain to sustain a safe working environment. Not only can manufacturers use AI-powered data analytics to determine which employees would benefit from additional safety training, plant managers can also use this information to better match employees to tasks based on their skills and physical abilities.

For decades, manufacturers have tapped robots to take over tasks that have a high risk of causing worker injury. Keeping humans away from heavy, moving machinery, toxic chemicals, and other risky environments has allowed manufacturers to place people in safer roles where specific skills and dexterity are highly valued. For example, even the most capable human workers possess a limited field of view. A forklift crash in a warehouse caused by a worker who failed to pay attention or missed a small hazard in their way could be devastating. However, an AI-powered forklift can use computer vision to monitor for hazards across the warehouse, reducing the risk it could bump into an employee or be crushed by a stack of pallets it crashed into. The former forklift operator could be reskilled into an oversight or maintenance role where they could provide even greater value.

Manufacturers can also assign dangerous inspection tasks to AI. Drones equipped with AI can inspect products stacked up to the high ceiling of a warehouse and determine the stability of the inventory. In plants where noxious fumes or dangerous chemicals are present, AI-powered robots can limit human exposure to potential hazards by performing regular maintenance and monitoring tasks. When humans must be involved in chemical plant turnarounds and total cleanouts, AI can monitor for safety compliance and environmental hazards.

Embracing the New Efficiency Paradigm

AI also represents a unique opportunity to improve the efficiency of production, especially when it comes to preventative maintenance. Currently, most factories use pre-scheduled maintenance plans to help prevent manufacturing errors that could stymie production. While maintenance schedules have undoubtedly prevented production disasters, pre-scheduled maintenance plans can be inefficient because they are not always informed by actual repair needs.

“Publicly available generative AI systems can hallucinate, including making up facts, citing nonexistent sources, or other false information”



On the other hand, a predictive maintenance plan powered by AI can allow plant managers to perform maintenance exactly when and where it is needed. By gathering information from transducers and other sensors, AI can monitor equipment temperatures, current power draw, and the status of different machine parts. This can help an AI system predict and prevent failures. When the technology detects an impending problem, a predictive maintenance system can further limit downtime by scheduling repairs when they will be least disruptive to factory operations.

AI can also support quality assurance (QA) and quality control (QC) by using laser distance sensors to perpetually scan for errors across a vast and fast-moving production line. In addition to keeping tabs on the quality of manufactured goods, AI can help plant managers keep machines properly calibrated by monitoring tolerances. Because many QA and QC tasks are highly repetitive, an AI system that has been taught which defects to look for can perform these tasks, allowing employees to focus on higher-value tasks within the plant.

In addition to preventing production problems, AI can also be used to address manufacturing defects and prevent similar ones going forward. In the event a customer returns a product, a plant manager can use a serial number to quickly determine when the production error occurred, and even which machine caused the error. Traditionally, diagnosing and solving the issue which caused the production error can be time consuming. However, an AI system with laser distance sensors can scan the product to determine the exact nature of the error and then ascertain which machine caused the error and how the equipment can be recalibrated to both fix and prevent the issue. This allows a factory to reduce both downtime related to diagnostics and inventory lost to production errors.

AI’s Risks

AI is not without risks. Integrating AI into production can increase cyber risk by creating new potential access points for bad actors. Manufacturers pursuing AI must consider the risks related to cybersecurity and take measures to mitigate potential issues. Additionally, publicly available generative AI systems can hallucinate, including making up facts, citing nonexistent sources, or other false information. Businesses that do not put safeguards in place to check these outputs could make decisions based on inaccurate information. Finally, if a company enters proprietary data into a publicly available generative AI, it does not have control over how that data is stored or used by the AI. This means it could be hacked or used to inform answers the AI provides to other users.

Approach to Adoption

While the future of AI in manufacturing could involve a revolution in production efficiency, it is important to understand where this technology might not be a fruitful investment. The less repetitive and homogeneous the task, the more extensively an AI needs to be trained by humans to properly execute it. Jobs which require greater dexterity, creativity, and experience may simply be better for a human to perform from both a cost and quality standpoint. For example, a local furniture factory may find that a worker assigned to evaluate fit and finish may only be able to flag 90% of errors. Even if an AI equipped with computer vision and laser distance sensors could flag 99% of defects, the cost and complexity of training an AI to correctly spot miniscule flaws may make a digital QA/QC improvement strategy untenable.

“The cost and complexity of training an AI to correctly spot miniscule flaws may make a digital QA/QC improvement strategy untenable”


When it has been determined that AI-based systems are feasible tools, manufacturers should carefully evaluate the potential ROI by clearly outlining which capabilities their plant actually needs. For example, a plant that produces dangerous chemicals may need the infrastructure to support a robust network of air quality sensors across an entire plant. On the other hand, a large candle factory might only need to support air quality sensors in specific rooms, and thus has smaller infrastructure requirements.  Finally, given worker concerns over the efficacy of AI-powered safety systems, plant managers must also help employees overcome their skepticism with a transparent dialogue.       

Preparing to Adopt AI Systems in Manufacturing Facilities

Tools like computer vision, sensors, and drones require adequate networking capabilities to be effectively used by an AI system in a manufacturing setting. While networking is an additional expense, the barrier to entry for powering AI-powered systems is lower than many manufacturers may think. In most cases, 5G wireless networking is not cost-prohibitive and is more than sufficient to provide fast, reliable connectivity.

A third-party vendor can be tapped to install the 5G networking equipment and software, as well as provide regular maintenance, software updates, and technical support. AI systems which rely on 5G-connected inputs can also be provided by third-party vendors, who may charge a monthly subscription to use their software or hardware. For most manufacturers, using a third-party vendor to supply and manage AI systems and networking equipment can help save money while allowing them to stay up to date with the latest technologies.

Enabling Employee Adoption

While the infrastructure for AI-based systems can be paid for with a monthly subscription, securing buy-in from factory workers being instructed to adopt AI systems is more complicated. Manufacturers can employ a variety of strategies to help employees understand that AI can make their jobs safer — not render them obsolete.

To overcome skepticism, manufacturers should start by demonstrating success via small pilot projects that illustrate the value of AI systems to workers. Tapping an influential employee to serve as an AI ambassador can help make pilot success stories more personal and authentic during discussions with skeptical employees and stakeholders. Demonstrating that workers’ safety is a priority and providing a platform for their concerns to be heard can also build faith that AI systems are there to support them in the workplace.

Depictions in popular culture often characterize AI as a risky unknown, or as a tool that executives use to automate away manufacturing jobs. Manufacturers should reiterate to workers that their industry faces a labor shortage, and rather than replacing jobs, AI can help factories fill some of the roles for which companies are struggling to hire. AI can also open doors for current employees to move into more productive, less repetitive roles. This could allow many workers to deploy their skills in a more valuable way while also working in a safer environment.

The Future of AI in Manufacturing

AI has the potential to transform worker safety and boost efficiency — but not in a vacuum. Manufacturers should take care to determine that investments in AI, including employee training, infrastructure development, and buy-in campaigns, will likely lead to tangible improvements to worker safety and generate cost savings from efficiency gains. For example, manufacturers won’t realize the benefits of sensors and cameras tasked with helping an AI system prevent manufacturing defects if the networking equipment that connects them to an AI system is unreliable or inadequate.

“Tapping an influential employee to serve as an AI ambassador can help make pilot success stories more personal and authentic”



Even with the most meticulously planned AI systems, manufacturers who are serious about AI success must train and educate workers to make them feel more confident in AI. Highlighting AI success stories, especially those that emphasize collaborative roles with employees, can help earn the support of factory or warehouse staff. Without employee buy-in, manufacturers may struggle to improve worker safety or boost efficiency with the power of AI.

Over the coming decades, new technologies are likely to help manufacturers achieve ever greater improvements to worker safety and factory. As companies grapple with the implementation of today’s AI technology, plant managers should remember that even the most exciting technologies can change little on their own. Manufacturers who learn to quickly secure employee buy-in and source a strong infrastructure of supportive technology will find themselves leading innovation rather than following other innovators.  M

About the author:


Maurice Liddell is Principal and Senior Market Leader at BDO Digital

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