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Preparing for manufacturing’s AI revolution requires security, teamwork and data literacy to navigate new technological landscapes 

TAKEAWAYS:
● AI Integration and Security: The deployment of AI in manufacturing requires careful consideration of security measures, including secrets management.
● Empowering Workers with Data Literacy: Successful AI implementation hinges on involving daily operators, developing security champions and promoting data literacy among workers.
● Infrastructure and Network Adjustments: The shift towards AI-driven manufacturing necessitates significant changes in infrastructure, including increased internet connectivity and changes to network security architecture.  

As artificial intelligence arrives for manufacturing but it does not require hasty sacrifices in operational security. The technology spans a variety of use cases and personas, including predictive maintenance, supply management, and customer service. Expect AI to come with major changes to architecture, such as demand for greater public internet connectivity, leading architects to revisit security architecture. With such sweeping change the challenge becomes how to accept these new opportunities to improve the business, while managing the risks without giving up the whole game. For example, one can choose OT monitoring systems to observe the internet-connected sensors that drive many digital twin projects. Beyond the quick wins, leaders should offer autonomy and support to their plants’ operations experts, to team with these new tools, and give them the resources to evaluate their estate using a data-driven approach that coheres to the AI program’s assumptions.

AI denotes the use of computerized decision systems to drive tasks that would otherwise use human intellect¹. Sensors for these systems can span a range of inputs and would be positioned throughout the factory floor². Ranging from heuristics to statistical learners and large language models³ — the factors in developing an optimal decision system often depend on system specifications and learning objectives. Most systems are designed explicitly for teaming between subject matter experts and the underlying learners, so the interfaces of these systems depend on their technical requirements⁴.

On the factory floor, there are three prospects for transformation. First, is the notion of digital twins⁵ and intelligent factories, which uplifts product lifecycle management into a fully computerized realm. Practically, this implies wide scale sensor deployments and ubiquitous availability of engineering computers, handheld, and personal devices. Second, is the increasing use of analytics to control supply chains and inventory. The state of the art has grown from ABCD analysis in the context of lean manufacturing, and into forecasted reorder systems and predictive maintenance regimes⁶. Automated support equipment is more likely to produce metadata, implying connectivity to processing grids designed for analytics⁷. Third, is the increasing use of customer feedback to optimize field operations. Call lines are increasingly centralized into virtualized equivalents of physical systems, with calls and e-mails being transcribed and feeding Voice of the Customer systems⁸. Data that was typically solely reserved for the back office is now available everywhere.

Regard the deployment of AI in your manufacturing environment as an opportunity to serve your daily operators as customers who require buy-in to execute your roadmap

To meet the challenge of transformation, infrastructure changes will come to the factory floor. Many sensors will need internet access to send communications to cloud-hosted brokers. This traffic can be proxied via self-hosted infrastructure, broadening access previously dedicated to the plant’s workload. Operations will also have to abide by a broadening raft of data sovereignty rules⁹. As the data bandwidth increases, companies will also find themselves racing to keep their key contributors appraised with the new systems. Engineers will have to become familiar with new protocols, cloud services, and most challenging of all, the analytical assumptions for these new decision systems¹⁰.

There are huge security trade-offs with the revolutionary changes discussed above. Enterprises should be prepared to proxy internet enabled devices. Certain applications need direct internet access and may cease functioning when routed through a proxy. Operators should be aware that those same devices will demand far more network bandwidth than prior. Network perimeter audits will have to scale with the added traffic and expanded blast radius. Bad actors may find additional resources for lateral movement where networks have not been adequately segmented. The use of additional systems of record also increases the risks posed by shared secrets, a challenge illustrated by the unprecedented data leak Snowflake customers face today¹¹. AI systems have complex software dependencies that compound hardware and software BOM data collection. This creates a stark paradox given that the nature of recent high severity vulnerabilities, such as with LangChain¹², are simple and can be exploited with little novelty on an adversary’s part. Finally, AI systems operate based upon a variety of approaches to data analysis, which can vary from simple decision trees and logistic regression models to large language models and other so-called overparameterized models. Regardless of these systems’ methods, retaining personnel capable of interpreting their output is crucial for good outcomes.

There are ways for operators to be prepared for the challenges of direct access, greater data egress, expanding vulnerabilities, and added areas of special expertise needed to operate. Be it through architectural adjustments or unlocking new areas of technical expertise, the following advice draws from primary sources that are indispensable for manufacturers. These are the SANS Five ICS Critical Controls whitepaper as well as the NIST guidance for manufacturing (most notably, SP 1800-10 and the manufacturing framework profile)¹³.

The crucial resource is humans, and if you intend to fund AI solutions on the factory floor, be sure to also invest in your workers’ data literacy

A finding in common with SANS and NIST publications is that defensible architecture depends on definitive logging on networks. This can mean further transport and analysis of firewall logs, but also recognizes that perimeter firewalls have little insight into internal networks, including remote access sessions or resulting traffic and actions within the OT environments. Fortunately, there are many first- and third-party solutions that can help detect threats on the perimeter, particularly in secure access areas¹⁴. The challenge becomes detecting threats that have made it beyond the perimeter. Secrets management programs can also help manage the blast radius by reducing secret reuse. Finally, one should invest in an asset monitoring program that tracks vulnerabilities, rewards patching discipline, and concentrates on risk mitigation for systems where patching is not possible. OT monitoring systems such as the Dragos Platform can build a bridge from ingress logs to a plant’s historians and other internal measurements, adding context in the case of a breach and maximize ICS network visibility. Having a registry of OT assets also allows one to focus their attention on the systems with the greatest effect on plant operations. Such a registry is typically a deliverable from a crown jewels analysis, a systematic, OT-focused threat analysis workshop¹⁵. In addition, canary token programs, where one issues a token that is tracked but does not provide deep access, is a useful approach to find areas of exposure16.

Just as creating shared ownership is a cornerstone to operating a security culture, growing shared technological expertise is the strongest means of hardening an environment subject to uplift by AI. The major theme for achieving this is to regard the deployment of AI in your manufacturing environment as an opportunity to serve your daily operators as customers who require buy-in to execute your roadmap. While it is typical for consultants or a center of excellence to develop AI systems, it is crucial that ideation includes these operators at all steps of the requirement gathering process. These operator roles can span from technicians and fabricators who assemble parts, the designers and engineers who specify drawings, all the way to inspectors and process analysts who keep the plant safe and thrifty. From these ranks one should identify champions as those who enjoy systems and/or security thinking, are empathetic for the conditions of their teammates, and are motivated to teach their findings.

Developing security champions for AI should not be treated as any other milestone. Getting buy-in from contributors will imply investing in education, not just for the AI system you are running, but also general data literacy and context for security architecture. The intuition for teaming with your individual contributors should be that AI is most effective in construction as a human-in-the-loop system¹⁷. The individual contributor should become more introspective about their operations such that it expresses growing data literacy. Leaders should reward examples of data literacy every day. For example, it is common for operations staff to know what typical levels look like, sometimes with greater detail than the equipment’s operating parameters. Challenge these experts to define these parameters formally via control charts, to impart this additional precision systematically¹⁸. Such a study applies just as effectively for AI-driven systems as well — one may consider this as calibration for the system. Finally, these findings have security implications too. A well-quantified baseline for normal activity in a plant is crucial for timing security response as well.

The winds of change in manufacturing tend to be steady and strong rather than sudden and violent, and this extends to AI. There will be deployments of troves of sensors, and greater networking of extent devices, which on the security front, means increased risk. More effort should be spent auditing traffic to the public internet and isolating important data and credentials. Security platforms, solutions, and services can be utilized for this effort, and OT-focused offerings such as the Dragos Platform and crown jewels analysis will be most in tune with your plant’s specific needs. The crucial resource is humans, and if you intend to fund AI solutions on the factory floor, be sure to also invest in your workers’ data literacy. Common awareness of the new operational norms and security architecture of your AI solutions will go a long way into hardening your environment as it becomes more efficient.  M

Footnotes: 202408_MLJ_Dragos_Footnotes

About the author

Jonathan Reiter is a Principal Engineer at Dragos Inc, where he has been since 2020. In his career he has contributed to router firmware, antivirus telemetry systems, industrial demand forecasting modules, and tagging systems for 737 fuselages for use on the Boeing Renton Factory floor. 

The Manufacturing Leadership Council, a division of the National Association of Manufacturers, hosted a record number of attendees at the 20th anniversary of Rethink, its signature event. Rethink continues to focus on helping manufacturing companies accelerate their digital manufacturing journeys. 

The Manufacturing Leadership Council hosted the 20th iteration of its signature Rethink event in early June at the JW Marriott hotel in Marco Island, Florida. Nearly 900 manufacturing executives, a record, participated in the event over the course of its three days, which consisted of MLC’s annual Council Day meeting, its two-day conference program, and the Manufacturing Leadership Awards gala.

The theme of the event was Accelerating Digital Transformation in Manufacturing. Speakers hailed from Johnson & Johnson, Saint-Gobain, Northrop Grumman, Eaton, Flex, Campbell Soup Co., Protolabs, and the World Economic Forum, among others.

A Women’s Manufacturing 4.0 Networking Luncheon and Panel Discussion, a first for the event, was held as was a session with leading industry analysts. EY gave an overview of economic trends affecting manufacturing. More than 20 technology and service providers demonstrated their innovations. And at the awards gala, more than 150 companies and individuals were honored for their achievements in digital manufacturing.

Following are selected scenes from the 20th anniversary Rethink event.

Award finalists gather on stage with their trophies.

Providing software-based services offers manufacturers new avenues of profitable growth.

TAKEAWAYS:
● Industrial SaaS revenues generate margins two to five times that of traditional manufacturing businesses.
● Manufacturers with successful SaaS businesses share four traits: products with impact, relevance, connectivity, and deep customer relationships.
● CEOs can apply BCG’s six-pronged framework and review its latest report to develop winning industrial SaaS business models.  

Industrial companies’ Software as a Service (industrial SaaS) ambitions have reached a tipping point. As many as 55 percent of North American manufacturers plan to use industrial SaaS business models as a source of growth, according to a recent survey conducted by the Manufacturing Leadership Council.

The reasons are obvious. Traditional avenues of growth for manufacturing businesses are becoming limited; products and systems are all connected and generate large volumes of data today; and industrial SaaS models are financially attractive, both in terms of margin and valuation multiples.

Although the opportunity is well known, only 15 percent of manufacturers have had success in building a significant industrial SaaS business, according to the same survey. Clearly, there’s a large gap between ambition and reality.

If you’re the CEO of a manufacturer looking for new avenues for growth, how do you start building an industrial SaaS model that will change your company’s growth trajectory?

Why Industrial SaaS is Future Critical

Many manufacturers have exhausted traditional opportunities for growth, such as market share and geographic expansion, and are exploring industrial SaaS as a next-generation growth platform. Rapid advancements in product and system technology that go beyond data availability to scalable ways that leverage data—AI and generative AI among them—have made industrial SaaS businesses technologically viable today.

The benefits are evident. Industrial SaaS revenues are more profitable than conventional ones, with margins two to five times that of traditional businesses. They’re less cyclical, too, with subscriptions less likely to churn than product sales. And they generate revenue multiples in the stock markets that are two to five times larger than those of the core business.

“Industrial SaaS models are financially attractive, both in terms of margin and valuation multiples.”

Recent BCG research highlights three key reasons why manufacturers have reached an inflexion point with industrial SaaS business models, and why we suggest starting with them now. First, the first-movers have tasted success. Between 2020 and 2023, companies with significant service revenues grew between 1.5 and 2 times faster than traditional manufacturers did.

Second, investors’ expectations are rising. A BCG analysis of around 100 manufacturers’ earning calls revealed a 15 percent growth rate in conversations with investors on industrial SaaS models, while traditional product-related topic discussions have fallen every year.

Third, investors want to see industrial SaaS businesses scale before they fully reward the strategy in terms of multiples. Another BCG analysis reveals that the business must grow to around 20 percent of a company’s enterprise value before investors will grant higher multiples.

Pioneering Successful Industrial SaaS Models

Consider two successful transitions to industrial SaaS business models, one by a large multinational manufacturer and another by a smaller regional player.

A $50-billion industrial equipment manufacturer has, over the past 15 years, increased the on-board telematics, connectivity, and machine-vision capabilities of its machines as well as its data capture and processing abilities. That has allowed the company to rewrite its corporate strategy in terms of creating fresh customer value through machine and job optimization. The manufacturer is delivering new solutions to customers and has set a goal for its industrial SaaS business to cross 10 percent of revenues by 2030.

A $300-million industrial automation market leader designs, builds, and services refrigeration systems across the US. Leveraging its market leadership and large installed base, the company has developed an AI-powered, data-driven, digital service technician​ platform. Using the platform reduces clients’ maintenance, repair, and operations costs, and provides them with a three to five times return on investment in the platform. This company, too, expects its industrial SaaS business to account for over 10 percent of revenues in the near future.

“Between 2020 and 2023, companies with significant service revenues grew between 1.5 and 2 times faster than traditional manufacturers did.”

Like these two companies, manufacturers that have successfully created industrial SaaS-based businesses share four characteristics. As a starting point, check whether your company meets these four criteria as you plan your transition:

1. Impact––Your products measurably affect customers’ operations and improve their bottom lines.
2. Relevance––Your products are a large contributor to customers’ operations in terms of criticality, cost, and/or performance.
3. Continuous Upgrades––Payments for services on an ongoing basis require continuous value delivery that must improve over time (think software upgrades).
4. Customer Relationships––You have close, long-term relationships with end users that provide deep insights into customer needs and behaviors.

How to Build a Winning Industrial SaaS Business Model

The technology is available, the upside is obvious, and the pioneers have experienced success. To act on the industrial SaaS opportunity, BCG has developed a six-pronged framework for CEOs:

1. Identify opportunities by customer value unlock.––Quantify this value to determine if the payoff is worth the effort and for which customers. Then, set a bold vision with value-unlock targets, and communicate them across the organization. This will tell you where to focus.
2. Use bundling to refine your go-to-market strategy.––Simplify your offer map and create bundled offerings. Bundling is a proven way to increase customer acquisition, facilitate cross-selling, and improve customer retention.
3. Define and build the tech stack.––To support an industrial SaaS model, your company will need Internet of Things (IoT), customer relationship management (CRM), and enterprise resource planning (ERP) systems, data analytics, cloud computing platforms, cybersecurity, hardware, and connected products, all working in concert, not in silos. Consider the compatibility between existing and future products and systems, scalability, and the potential to meet future needs. Establishing a vision for the tech stack early is critical to ensure resources aren’t wasted.
4. Use M&A proactively and opportunistically.––To build momentum, companies must be prepared to acquire products, capabilities, and talent to address new needs. Some manufacturers have made acquisitions to plug into existing channels and to house all their industrial SaaS operations in one place, among other reasons.
5. Invest in delivering customer outcomes.––Companies will have to build industrial SaaS-specific sales motions such as land and expand; learn to focus on ensuring customer success; and develop the capabilities to provide software updates that improve customer performance over time. Lifecycle product management requires a different operating model that usually stretches most hardware-focused industrial companies.
6. Specify the new talent and capabilities needed.––The talent needed to succeed with industrial SaaS models at scale is different than those manufacturers usually possess. Upskilling existing teams and becoming an attractive landing spot for digital talent are both difficult, but essential steps.

To delve deeper into how companies can execute an industrial SaaS strategy, please see Playing to Win in the Industrial Software as a Service Revolution.

The good news is that you don’t need to execute all these steps at once. You can sequence them in digestible bits over time according to a roadmap. A CEO can start by asking the strategy and business development team to define what an industrial SaaS strategy could look like for the company before aligning a plan with the leadership team.

But the time to start is right away.  M

Authors Bios:

Merih Ocbazghi is a managing director and partner in BCG’s Chicago office. He is a core member of BCG’s Industrial Goods practice in North America, with specialization in strategy topics.

Katherine Smith is a managing director and partner in the firm’s Atlanta office. She is a core member of BCG’s Industrial Goods practice in North America.

Jonathan Van Wyck is a managing director and senior partner in the Minneapolis office of Boston Consulting Group. He leads the Industrial Goods practice for BCG in North America.

The Fourth Industrial Revolution is set to enter hyperdrive as generative AI and other AI tools prove their value in manufacturing and spur the adoption and time to impact of other technologies. 

TAKEAWAYS:
● AI’s impact is bigger than a combination of use cases, able to orchestrate the complex interplay of a range of platforms, devices, and environments.
● The impact of gen AI is extending beyond the shop floor.
● Manufacturers that have already invested in their data foundation and infrastructure will be better placed to move quickly with AI and gen AI.  

We now have enough evidence that AI is fundamentally transformational for manufacturing, when implemented correctly. This is perhaps best illustrated in the Global Lighthouse Network; a community of manufacturers using Manufacturing 4.0 technologies to transform factories, value chains, and business models.

The most recent cohort of Lighthouse factories has seen compelling returns from AI—both traditional AI tools such as machine learning for yield optimization, and generative AI. Individually observed results range from a two to three times increase in productivity and a 50 percent improvement in service levels to a 99 percent reduction in defects and 30 percent decrease in energy consumption.

But AI’s impact is much bigger than the combined impact of individual use cases. Its true power may come from its position at the top of a pyramid of Fourth industrial Revolution (4IR) technologies, from where it can orchestrate the increasingly complex interplay of wearables and devices, flexible robotics, integrated platforms, and cloud environments.

Putting AI at the top of the technology pyramid effectively allows it to conduct a symphony of impact by unlocking the full value of 4IR technologies (Exhibit 1).

Exhibit 1

Generative AI is only going to amplify AI’s machine intelligence capability and speed up the digital transformation of manufacturing. Those manufacturers who have built up knowledge, skills, and data foundations to support their ongoing 4IR transformations will be first out of the blocks with gen AI, too.

Speed and scope: Generating results from AI and gen AI

The ongoing AI adoption journey among Lighthouses reveals five insights into how frontrunners are driving impact from AI and gen AI.

1. AI use cases are prolific across the whole operations value chain

While more than 80 percent of Lighthouse use cases involving AI are executed at the individual process level, AI is making a significant impact at every supply chain step—including planning, asset management, quality, and delivery. In quality, for example, VitrA Karo, a ceramics manufacturer in Türkiye, has decreased its scrap rate by 68 percent by deploying computer vision in its kiln. And in delivery, China Resources Building Materials Technology, a concrete and cement manufacturer, has optimized its heavy-transport equipment routes to decrease pickup lead times by 39 percent.

2. AI can be assetized to enable deployment at speed and scale

One of the ways that Lighthouses have sped past pilots is by packaging use cases for speed and scale of deployment, leveraging modular design principles to ensure interoperability with existing technology architecture. Agilent, a life sciences equipment manufacturer in Germany, assetized its computer vision technology into a tool kit that enabled five distinct computer vision use cases to be deployed, reducing defect rates by 49 percent in just four months.

3. AI command centers are enabling next-level and system-level automation

Lighthouses are prioritizing closed-loop feedback to improve their models and increase confidence intervals, while also integrating safeguards, monitoring mechanisms, and overrides to reduce risk. Mondelēz, a global snack-food manufacturer, has adopted an AI control center that spans five automated production lines, four intelligent guided vehicles, and nine ingredients in the supply chain—enabling it to optimize processes, analyze consistency, and improve capacity and speed across production lines and supply chains.

4. Gen AI is extending AI’s impact beyond the shop floor

Gen AI could be applied to automate nearly 70 percent of tasks across manufacturing and supply chain-related activities, primarily driven by new capabilities in content generation, insights extraction, and user interaction. New use cases involving unstructured data, like product development and procurement-related activities, could enable robust digital transformations to happen faster than the usually expected two to three years.

5. Gen AI is speeding past pilots, too

Today, the starting line for gen AI is far more advanced than it was for applied AI five years ago. The time it takes new Lighthouses to implement AI use cases has fallen by nearly 25 percent compared with earlier cohorts, and some are even skipping pilots altogether. ACG Capsules, a pharmaceuticals contract manufacturer in India, used gen AI to fully develop and deploy a copilot to interface with standard operating procedures (SOPs) in less than five weeks, reducing mean time to repair (MTTR) by 40 percent.

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AI’s position at the top of the technology pyramid is expected to fast-track the adoption and optimization of 4IR technologies that make up the connected, intelligent factory of the future.

Those manufacturers that have already invested in their data foundation and infrastructure will be better placed to move quickly with AI and gen AI. And for manufacturers that have been slower to realize their 4IR ambitions, gen AI could be the technology wave that finally kickstarts their wider transformation journey.

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The Global Lighthouse Network is a World Economic Forum initiative co-founded with McKinsey, identifying guiding lights that inspire transformational change, exemplify collaboration, and shape a better future for all players in the operations ecosystem. Learn more about the Global Lighthouse Network and how to benefit from the digital transformation playbook they have inspired by contacting the authors directly or by following the application process to be part of the next wave of Lighthouses, here.  M

About the authors

Kevin Goering is a partner at McKinsey based in the Bay Area,

Enno De Boer is a senior partner at McKinsey in New Jersey.

Rahul Shahani is a partner at McKinsey based in New York.

Henry Bristol is a McKinsey consultant based in New York.

As they climb up the maturity curve, manufacturers see a host of benefits as well as challenges with AI, says a new MLC survey.  

KEY TAKEAWAYS:
● 78% of surveyed manufacturers say they plan to increase spending on AI tools in the next two years.
● 46% are already using generative AI tools such as Chat GPT or Microsoft’s Copilot in manufacturing operations.
● 55% expect AI to change the rules of the industry by 2030, despite issues with data and a lack of AI-related workforce skills.  

Manufacturers are planning significant investments in artificial intelligence technologies, including generative AI tools, in the next two years in order to improve their production capabilities, their decision-making processes, and to generate more predictive insights into operations, even as they struggle with data issues and a lack of AI-related skills in their workforces.

Moreover, most manufacturers are moving ahead with AI in a strategic way, with AI closely aligned with their digital transformation and business strategies. And in looking ahead to 2030 and beyond, a majority of manufacturers expect that AI will be a “game-changer” for the industry that will shape the rules of competition for years to come.

These are some of the top-line findings of the Manufacturing Leadership Council’s new survey on AI in manufacturing. The study explored seven major areas of manufacturers’ involvement with AI. These include the maturity level of AI usage, spending intentions on AI tools including GenAI, how companies have organized around the AI opportunity, what benefits manufacturers are looking for from AI, the impact of AI on the workforce, challenges with AI implementations, and the expected future impact of the technology.

Status of AI Adoption and Spending Plans

Over the next 12 to 24 months, 78% of surveyed manufacturers said they plan to increase spending overall on AI tools, with one-fifth expecting to increase their AI investments by more than 30%. Regarding GenAI tools such as Chat GPT or Microsoft’s Copilot, nearly half of survey respondents are currently using these tools in their manufacturing operations and more than 80% said they expect to increase their use in the next two years (Charts 1, 2, 3).

1. Strong Majority to Increase AI Spending

Q: Does your company plan to increase spending on AI tools in the next 12 to 24 months?

2. GenAI Tools Already in Wide Use

Q: Are you currently using GenAI tools such as ChatGPT or Microsoft Copilot in manufacturing operations?

3. More Than 80% Will Increase GenAI Usage

Q: What are your plans for GenAI tools in the next two years?  

Should these investment intentions pan out over the next two years, they will do so against a backdrop of experience with AI that is at an early stage in most companies. Overall, survey respondents indicated that their level of maturity with AI tools in manufacturing operations is nascent (Chart 4). For example, only 5.4% of respondents assessed the maturity of AI tool usage in their manufacturing operations as “advanced”, with 66% indicating it is at an “early” stage and 28% at a “moderate” stage. The findings are similar across 14 other corporate functions, including supply chain, research and development, and quality operations, surveyed by MLC.

This may change as the pace of experience with AI picks up. The adoption of GenAI tools such as Chat GPT and Microsoft Copilot has been remarkable. Currently, 46.7% of survey respondents indicate they are using GenAI tools in knowledge management, to help identify process improvements, in quality operations, and in preventative and predictive plant floor equipment maintenance (Chart 5).

More than one-third of respondents say they plan to substantially increase the use of GenAI tools over the next two years. Another 49% say they are planning a moderate increase in usage of these tools. And a majority, 52.7%, say they will do so according to corporate policies that have been established on the selection and use of GenAI tools (Chart 6).

4. Manufacturing Operations AI Tools are Nascent

Q: Overall, how would you characterize the present maturity of artificial intelligence usage in your company’s manufacturing operations? (on a scale of 1-10, with 10 being the highest level of maturity) 

5. Knowledge Management is Primary Area of GenAI Usage

Q: If yes, in which areas have you implemented generative AI? (top 3)

6. A Majority Have a Corporate Policy on GenAI

Q: Has your company established a corporate policy on the selection and use of GenAI tools?  

AI Strategy and Organization

Perhaps a result of many years of using traditional AI tools such as business intelligence and machine learning technologies, most manufacturers say their companies have a corporate AI strategy (Chart 7). Moreover, 78% say their AI initiatives within manufacturing operations are part of their company’s larger digital transformation and business strategies (Chart 8).

In addition, a substantial percentage of survey respondents, 42.8%, say that their company’s AI governance process is part of their overall data governance strategy. Nearly 20% indicate that they have an AI governance strategy but that it is not part of data strategy, while 27% say their companies do not have an AI governance strategy at all.

But when it comes to being able to identify who or what corporate unit oversees AI initiatives, the lines are blurry. The chief information officer was cited by just over 21% of respondents as the corporate officer in charge of AI initiatives, but an equal number of respondents say that authority is unclear in their companies. Given the proliferation of technology-oriented executive titles and functions in recent years, it is perhaps not surprising that involvement and even responsibility for AI projects has crossed organizational boundaries in many companies (Chart 9).

7. Manufacturers Are Thinking Strategically About AI

Q: Does your company have a corporate AI strategy?

8. Digital, AI Linked in Vast Majority of Companies

Q: Are your AI initiatives within manufacturing operations part of a larger digital transformation strategy for your company? (select one)

9. The CIO is Most Often in Charge of AI

Q: Organizationally, who or what department is in charge of AI initiatives in your company?

Expected Benefits of AI

At the end of the day, what benefits are manufacturing companies looking to get out of their investments in AI? The answers are to be found in the rapidly increasing volumes of data companies are generating from their extensively connected businesses.

When asked to assess a list of 11 potential business benefits using a low/moderate/high scale, the three potential benefits that motivated a strong majority of respondents for their “high” potential were more predictive insights from data, better decision making, and better planning (Chart 10).

In operations, the three potential benefits scoring a “high” ranking were improved predictive maintenance, increased uptime of factory assets, and a more efficient use of the workforce. In the supply chain category, the three were better supply chain planning, more predictive insights, and increased supply chain agility (Chart 11).

Aspirations aside, one of the disciplines that manufacturers will have to get better at as their experience with AI matures is measuring effectiveness. Currently, 66% of respondents say their companies do not have a specific set of metrics to measure the effectiveness and impact of AI implementations (Chart 12).

Although the prospect of AI-supported autonomous factory and plant operations is being widely discussed in the industry today, survey respondents take a nuanced view of the concept. The idea of “fully autonomous” operations is a foreign one, but noteworthy percentages of respondents do expect a substantial degree of autonomy to be achieved in their plants and factories in the distant future (Chart 13).

10. Better Insights, Decision Making Are Top Business Benefits

Q: How would you assess the potential business benefits of AI in your company ? ( top 3 benefits ranked by highest level of response)

11. Data Issues Are the Biggest Challenge with AI

Q: How would you assess the potential benefits of AI in manufacturing operations? ( top 3 benefits ranked by highest level of response )

12. Most Do Not Have AI Metrics

Q: Do you have a specific set of metrics to measure the effectiveness and impact of AI implementations?

13. A Majority Believes that Autonomous Plants Are in the Distant Future

Q: What statement would best describe your expectation about the future state of factories and plants as a result of the use of AI by 2030?

AI’s Effect on the Manufacturing Workforce

As other MLC studies have previously indicated, manufacturers are largely not buying into the fear that AI adoption will result in widespread worker displacement. Nearly one half of survey respondents, 47%, do not expect their company’s workforce headcount to be affected by AI. However, just over one third, 36.4%, do expect that headcount levels will decrease, and seven percent expect that headcount will increase because of AI adoption (Chart 14).

For those expecting some workforce displacement, 56% say that those displaced will be retrained or reassigned to one degree or another, with 22% saying that 20% of more of those displaced will be offered other opportunities.

14. Nearly a Majority See No AI Effect on Workforce Levels

Q: What impact do you think AI will have on your workforce headcount by 2030?

AI Challenges, Policy Considerations, and Future Impact

As it is with any IT or OT technology, there are always challenges associated with their implementation and use. By far the most significant challenge with AI has to do with data, say 68% of survey respondents, with data quality, validation, and contextualization at the top of their punch lists (Chart 15, 16). The lack of AI-related skills in the workforce and understanding the business case for AI were also cited as key challenges.

Although it did not make the top three challenges indicated by survey respondents, embedded bias in algorithms was cited by one-fifth of survey takers as an issue. Misinformation was also cited as a key risk factor (Chart 17).

On the question of whether the U.S. should have a federal-level industrial policy on AI, nearly half of survey respondents are in favor of such a policy. Furthermore, nearly half also support regulation of AI by the federal government (Charts 18,19).

And, for the first time on a question that has been asked in previous MLC surveys, a majority of respondents now feel that AI will be a “game-changer” for the industry in the future (Chart 20).

Just how fast that future could arrive will no doubt be on the minds of manufacturing executives as they think about how to remain competitive in the years ahead.

15. Data Issues Are the Biggest Challenge with AI

Q: What do you see as the biggest challenges to AI adoption and use?

16. Data Quality, Contextualization Are Top Challenges with AI Data

Q: What areas of working with AI-related data are proving most challenging?

17. Misinformation is Seen as the Biggest Risk with AI

Q: What do you see as the most significant risk in using AI?

18. Nearly a Majority Are in Favor of a Federal AI Policy

Q: Should the U.S. have a federal-level industrial policy to encourage AI development and adoption?

19. Nearly Half Favor Federal Regulation of AI

Q: Do you think AI should be regulated by either the states or the federal government? (select one)

20. Most See AI as A Game-Changer for the Industry

Q: Ultimately, how significant an impact will AI have on the industry by 2030 and beyond?

About the author:

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

Demystifying AI and ML to uncover seamless integrations to improve planning, manage disruptions, and enhance operational efficiency

TAKEAWAYS:
● AI and ML significantly improve production execution, predictive maintenance, and scheduling, leading to higher quality, reduced downtime, and optimized operations.
● Successful AI/ML integration requires robust data management, cross-functional collaboration, scalable pilot projects, continuous performance monitoring, and staff training.
● Emerging technologies like Generative AI, synthetic data, and autonomic systems promise to revolutionize manufacturing with self-optimizing operations and advanced model training techniques.   

AI Manufacturing Use Cases

There are many different application areas for AI across manufacturing and production. Even limiting the scope to just “within the four walls of the shop floor,” one could write a novel on all the possibilities (let alone thinking about supply chain and beyond).

These three use cases represent some of the most typical and prominent opportunities for a manufacturing leader.

1. Production Execution – For example, AI-driven quality control systems inspecting products in real-time, ensuring consistent quality and minimizing waste.
2. Predictive Maintenance – For example, AI continuously monitoring equipment health and ML predicting potential failures, together with the goal of reducing downtime and maintenance costs.
3. Production Scheduling – For example, AI optimization creating constraint based schedules and ML analyzing historical schedule performance to drive increased throughput and resource utilization.

Each of these use-cases have typically been managed by spreadsheets, legacy systems or just “tribal knowledge” in the past, but today’s manufacturing and product complexity means that only AI and ML algorithms can now effectively process and analyze the vast amounts of production data in real-time, to help humans make confident and effective plans and decisions.

1.    Optimizing Production Execution with AI and ML

Even simple manufacturing operations generate tremendous amounts of data. Although still a challenge, the easier part of the task is to collect the data from the various sensors and machinery across the shop floor. Once collected, manufacturers can start to leverage AI’s power.

The first, immediate, benefit is visualization of the data collected. For humans, that can be helpful if there are obvious anomalies in what is seen, but most manufacturing problems have root causes that run deeper than obvious and isolated deviations. For example, product defects that present as a lack of worker training but are actually defects related to raw material quality.

AI can efficiently identify anomalies in real-time as the data is collected, and then correlate the data with upstream and downstream processes to identify associated processes that might show causation between the processes and therefore identify the root cause. Doing this in real-time allows immediate adjustment to production runs (especially in batch environments) that can prevent wasted time and material.

“By reducing unplanned downtime, manufacturers can extend equipment lifespan and increase overall production efficiency at reduced costs.”

Because this data is continuously collected, ML can be applied to the historical data store to reveal additional insights and patterns that can help drive future improvements. For example, ML can analyze quality data to identify subtle trends in material or production tolerances that may be leading to failures that are more critical. It could also predict and optimize energy consumption dynamically, which can lead to more sustainable production practices.

Taken a step further, AI and ML could be entrusted to not only identify current and future problems, but to autonomously adjust production parameters in real-time to optimize performance and respond to changing conditions without the need for human intervention. This, of course, requires a certain level of trust between man and machine – more on that in a moment.

2.    Predictive Maintenance Using AI and ML

Despite the best production control and execution, one challenge that every manufacturer deals with is machine maintenance – both planned and (more challenging) unplanned. Just as AI and ML can optimize product quality, the same principles can apply to production resources.

Predicting maintenance problems mitigates the disruption that they cause. Continuous monitoring of equipment performance and health via sensors provides a wealth of data that can feed ML algorithms. These algorithms are adept at developing insights into the probability of unplanned downtime based upon historical frequency and patterns of past machine performance. This means manufacturers will be able to see predictive alerts that can then use to prepare for potential problems: either pro-actively accelerating scheduled maintenance or planning for additional capacity.

Machine failures not only affect finished products but also the asset intensive machine equipment. By reducing unplanned downtime, manufacturers can extend equipment lifespan and increase overall production efficiency at reduced costs.

Does AI (optimization) play a role in predictive maintenance? Not as much in the prediction or prevention of downtime, but it certainly is the hero when disruption hits the production line.

3.    AI and ML Applications in Production Scheduling

The famous statistician W. Edwards Deming said, “Uncontrolled variation is the enemy of quality.” This is absolutely the case in manufacturing, and manufacturers can easily add “efficiency,” “cost” and several other nouns to “quality.” One of the key Industry 4.0 attributes that companies are seeking is “agility” – the ability to react to change as it happens with confident decision-making. AI (specifically optimization) can be applied to this requirement with great effect.

Even simple manufacturing operations contain too many constraints and variables to allow for manual scheduling of jobs with any level of precision. Spreadsheets and manual scheduling simply are not capable of optimizing production against operational goals, let alone providing decision support for possible alternative plans. AI optimization excels at being able to consider millions of potential combinations of material, capacity and skill-based constraints while searching for plans that meet stated KPIs, such as order fulfillment, changeovers, etc.

This ability to create an optimal production schedule in seconds (compared to hours or days) means manufacturers can instantly react to changes in orders, capacity or material. Additionally, they can experiment with multiple “what-if?” scenarios to evaluate trade-offs and opportunities to meet more corporate goals.

ML also plays a role in this area. Since no plan survives contact with the real world, it is inevitable that unforeseen changes will affect even the most promising plans. In the same way that historical machine data can be reviewed to predict failures, ML can mine the performance of previous plans to analyze whether there are patterns that reveal how future schedules can be additionally adjusted to improve their effectiveness.

Implementing AI and ML Solutions in Manufacturing

There are lots of use-cases and lots of benefits, but where and how to start? The most obvious common foundation might be the reliance of these technologies on data.

AI and especially ML, require large amounts of accurate data (and in real-time in many cases). Therefore, manufacturers will need to implement robust data collection and management practices to ensure that the data feeding into AI/ML models is accurate and reliable. This might also mean a strategy and investment into 5G and IIoT, since the data will be coming from sensors and IoT devices that collect real-time data from production equipment.

Beyond simply collecting the data, several other typical project items also deserve consideration:

1. Foster collaboration between IT, data science, and operational teams to ensure alignment and effective implementation of AI and ML solutions. Example: Create cross-functional teams comprising members from manufacturing, IT, and data science to oversee the AI/ML integration process.
2. Begin with small-scale pilot projects to test the feasibility and impact of AI/ML solutions before full-scale implementation. This approach helps manage risks and identifies potential issues early. Example: Implement a pilot project for predictive maintenance on a few critical machines to validate the technology’s effectiveness.
3. Design AI/ML solutions with scalability in mind, allowing for easy expansion as the technology proves its value. Example: Develop modular AI/ML solutions that can scale across different production lines or facilities as needed.
4. Regularly monitor the performance of AI/ML models and make necessary adjustments to improve accuracy and efficiency. Example: Implement a feedback loop to continuously assess model performance, and make improvements based on real-world data.
5. Provide training for staff to understand and work with AI/ML technologies, ensuring they are equipped to leverage these tools effectively. Example: Conduct workshops and training sessions to upskill employees in AI/ML concepts and tools.
6. Consider AI & ML “as a service.” Vendors are now starting to offer MLaaS to manufacturers that do not have the required skillset or budget to implement a long-term strategy, yet need an answer to a specific problem or challenge. By contracting with a vendor for an “outcome,” they can leverage AI or ML to solve key strategic problems while offsetting the cost of software licenses, IT overhead and additional skilled staff.

This last item has an underlying issue that is reminiscent of the continuing references to the “Skynet” of Terminator movie fame: trust in AI. While it is unlikely that a production scheduler is going to trigger the “rise of the machines,” the general complexity of AI/ML, data, and the scope of application means that much of the actual workings of the algorithms are difficult, if not impossible to comprehend. In other words, the output, or “what,” is understandable but the “why” is often not. In fact, the output may even seem counter-intuitive to one’s own gut-feel. Sometimes, this is because the technology is sacrificing particular operational metrics to achieve a greater goal.

For example, an optimized production schedule might meet the primary KPI of “order fulfillment” at the expense of OEE for particular resources. On the shop floor, a human scheduler might see this as a flaw in the plan and attempt to “tweak” the schedule to keep the resource loaded when, in fact, this will affect the plan in negative ways and reduce overall business objectives.

This “trust in AI” is something that will certainly change over time. In the meantime, you can add one additional practical step to the project plan to help:

• Communicate the benefits and potential of AI/ML solutions to all stakeholders to mitigate resistance and encourage adoption. Example: Hold informational sessions and provide case studies demonstrating the positive impact of AI/ML on similar manufacturing operations.

Is it Worth It?

There is obviously some effort and commitment required to implement AI and ML into manufacturing processes but the value is certainly there. By leveraging AI and ML, manufacturers can achieve greater operational agility, reduced costs, and improved decision-making capabilities, positioning themselves competitively in an increasingly complex and dynamic market landscape.

Manufacturers are seeing critical KPIs affected very significantly in the following areas:

1. Cost Reduction: In both production efficiency and quality-related costs, improvements up to 50% are possible
2. Productivity: By replacing manual decisions with automation, planners are recovering 25-50% of the time for more value-added activities
3. Time to Market: More efficient scheduling and execution can accelerate time to market (especially in fast moving sectors) by up to 40%

In addition, the benefits related to equipment maintenance and lifespan can have a significant positive impact on capital expense budgets.

Mileage may vary, of course, so another critical step in the plan is to build a solid business case based upon clearly stated goals and expectations with a realistic state of as-is and to-be. The technology vendors who provide AI and ML are well aware of the capability and complexity of their wares, and so should be willing to work with manufacturers on this step (it is in their best interest after all to ensure a successful long-term partnership).

“Spreadsheets and manual scheduling simply are not capable of optimizing production against operational goals, let alone providing decision support for possible alternative plans.”

Whether manufacturers leverage a technology vendor, consulting partner, or has the expertise in house, they should not skip this step. Even if their executive team has a pot of money and an urgent decree to get on the AI and ML bandwagon, it is imperative to understand why this technology is needed and exactly what element of the possible capabilities will be critical to achieving success and how.

Future Trends and Innovations

The integration of AI and ML into an Industry 4.0 strategy underscores a commitment to innovation and continuous improvement, setting the stage for more sustainable and resilient manufacturing practices. Manufacturers can achieve effective use of AI today (and many companies are already receiving the benefits). But, what of the future? What can we expect from AI in the next decade and beyond?

It would be easy to say, “The possibilities are endless,” and they probably are in many respects. What seemed impossible yesterday is in practice in manufacturing today. Here are some thoughts about what might be possible tomorrow:

• Generative AI for Production Optimization: While this article did not focus on the sub-genre of Generative AI, it has the potential to revolutionize manufacturing by providing powerful tools to optimize production processes. Imagine GenAI developing completely new manufacturing lines or optimization strategies that are not immediately apparent to human operators.
• Synthetic Data for Enhanced Model Training: The use of synthetic data is gaining traction as a method to train AI models without compromising privacy or requiring extensive real-world data collection. Synthetic data can simulate a wide range of operational conditions, enabling robust training of ML models for applications such as predictive maintenance, demand forecasting, and process optimization.
• Autonomic Systems for Self-Optimizing Operations: Autonomic systems utilize AI to automatically adjust and optimize processes without human intervention. These systems continuously learn from operational data, making real-time adjustments to maintain optimal performance. Applications include dynamic scheduling, resource allocation, and process optimization.

It may seem that AI’s evolution will outpace manufacturers’ ability to implement any of its capabilities. This pace of innovation also often causes internal initiatives to move so fast that they can fail. Although AI and ML can be applied to the most complex and strategic processes, they can also be applied to solve very simple and tactical challenges – production scheduling is a perfect example of this. It is a classic situation of not suffering from “analysis paralysis.” In fact, here are four activities every manufacturer could benefit from now:

1. Become educated on AI and ML’s fundamentals: it is a large discipline across many industries. Leaders should become confident and articulate on the basic capabilities and how they could be leveraged in manufacturing scenarios.
2. Don’t get caught up in the hype: for those just starting out in AI, find the practical use-cases and always work backwards from the business problem, not forwards from the technology capabilities.
3. Learn from all voices: there are fast-growing practical experiences in manufacturing and many third party sources and technology experts who are publishing prolifically on this sector.
4. Finally, leverage the extensive network and information provided by the MLC and its members. There are extensive resources and experiences just a mouse-click away.  M

About the author:

Adrian Wood is Director of Strategy & Marketing at Dassault Systèmes

To capitalize on AI, companies need to build a strong data foundation and upskill their workforce with AI skills. 

TAKEAWAYS:
● Manufacturers cannot afford a wait-and-see approach–they need to explore where AI adoption will provide the most tangible and immediate benefits.
● AI can increase the efficiency of administrative tasks, predict demand patterns, and improve inventory planning.
● Companies will need to upskill their existing teams to succeed in a data-driven environment and maximize new AI-enabled capabilities.  

Artificial intelligence (AI) has the potential to transform every aspect of a manufacturer’s business—but some of its greatest impact will be on the supply chain.

Supply chain professionals will be able to enhance their work with the insights that AI provides, allowing them to bring together data from around the business in real time to make data-driven decisions and uncover opportunities to mitigate risk and improve resilience. They can also use AI to increase the efficiency of administrative tasks, predict demand patterns, improve inventory planning, and much more. Many supply chain professionals will soon interact with AI for the first time in their careers as manufacturers seek to increase their AI and machine learning (ML) investments. In fact, BDO’s 2024 Manufacturing CFO Outlook Survey found that 47 percent of chief financial officers (CFOs) are increasing investment in AI and ML this year.

While AI is promising, manufacturers need to build the foundation for adoption; otherwise, their investments will not realize expected return on investment (ROI). How can manufacturers prepare their supply chain management teams for AI adoption?

In this article, we explore steps manufacturing leaders should take to enable successful AI adoption in the supply chain function:

• Build a strong data foundation
• Enable cross-functional collaboration
• Foster AI-related skills in the supply chain workforce
• Create a culture of curiosity around AI usage

Build a Strong Foundation

The first step in any organization’s AI journey is to build a strong data foundation. This involves consolidating disparate data sources, ensuring all data are stored in an accessible location with appropriate reference fields enabling analysis across datasets, and implementing strong data governance standards and processes. Digitally mature manufacturers may already have this data infrastructure in place; however, many manufacturers’ existing data management practices are insufficient to support many of the most promising AI use cases.

To build this foundation, manufacturers need AI-savvy data scientists to help them interrogate and analyze data to extract useful insights. Manufacturers can either hire data scientists directly or work closely with a third-party provider experienced in helping companies set up their data infrastructure.

“Many supply chain professionals will soon interact with AI for the first time in their careers as manufacturers seek to increase their AI and machine learning (ML) investments.”

Once an organization has data scientists onboard, it should collaborate with operations, supply chain, quality, and other leaders to identify the business problems it is trying to solve and the relevant internal and external data that will power its AI tools. Once these are identified, the organization can begin designing the company’s AI-enabled strategy.

Enhancing Cross-functional Collaboration

To deliver the most value, AI requires access to data from across the organization. Enabling this kind of data sharing requires the integration of many disparate systems—including warehouse management systems (WMS), customer relationship management systems (CRM), supplier relationship management systems (SRM), and enterprise resource planning systems (ERP).

The supply function is an ideal area to roll out new data-sharing processes, as supply teams already naturally interact with groups across organizations. Supply leaders can be critical partners to data scientists and other individuals leading AI adoption and setting up new tools and processes. Strong data governance is also critical to ensure that AI tools provide accurate outputs based on high-quality, reliable data sets.

Effective data sharing across systems can provide supply chain professionals with real-time, organization-wide visibility. Powered by AI, supply chain professionals can have quick access to aggregated insights that enhance their decision-making and free them up from having to perform manual analysis. For example, a procurement professional at an appliance manufacturer may receive an automated alert from an AI-powered tool that there has been an influx of negative customer feedback pouring into its CRM system due to unreliable electric motors in some washing machines. Since the CRM system shares data with the company’s SRM system, the procurement professional can identify the relevant supplier and reach out to discuss how to alleviate the issues. The company could also use this information to inform benchmarks for supplier performance.

Fostering New Skills

While many supply chain organizations will need to hire professionals with knowledge of strong data governance principles and an understanding of how large language models and other AI solutions work, the larger challenge will be upskilling their existing teams to succeed in a data-driven environment and optimally leverage the company’s new AI-enabled capabilities.

In a world where machines can automate tasks, perform rapid calculations, and analyze vast data sets to uncover deep connections and patterns, leading supply chain organizations are prioritizing analytical thinking and digital dexterity—that is, the ability of employees to adopt and adapt to using emerging technologies to deliver improved business results—as part of their core curriculum to upskill supply chain teams. Training supply chain professionals to use generative AI tools is also essential. For example, if a manufacturer is deploying generative AI tools, training on prompt engineering—that is, how to design effective queries to extract the necessary data from AI tools in a useful format—will be vital.

“Starting with a small pilot project focused on achieving tangible, near-term ROI can help get team members on board and establish internal AI champions.”

Application-based instruction that leverages a controlled AI environment that is disconnected from the company’s production systems will be critical to building these skills. The test environment can also teach employees about their company’s acceptable use policies for AI and provide a safe place for professionals to learn from mistakes.

Many companies are also investing in third-party developed prompt libraries or guides for sample queries to run in specific scenarios to support increased user adoption. For example, an advanced inventory planning tool might recommend questions like “identify which suppliers have had lead times that were more than five days past system projections over the past three months.”

Creating a Curious Culture

Successful AI implementation requires empowering individuals to use AI and ML tools in their everyday work. To encourage adoption, manufacturers need to foster a culture of curiosity by training and inspiring their teams to explore the possibilities that AI tools can provide. This culture can also help manufacturers overcome common roadblocks to adoption.

For example, some supply chain professionals may worry that AI will replace them or will complicate their jobs. Explaining how AI can augment (versus replace) human expertise and judgment is essential to overcoming these hurdles. Beyond mitigating replacement concerns, manufacturers can demonstrate the value that achieving mastery over AI tools and skills can have for their employees’ professional development.

Starting with a small pilot project focused on achieving tangible, near-term ROI can help get team members on board and establish internal AI champions. For instance, a manufacturer that has access to an internal generative AI tool could work closely with procurement teams to show how it could support researching new vendors or generating information that may be helpful in a negotiation.

The Future Won’t Wait

AI is no longer on the horizon—it’s here, and leading manufacturers are moving quickly to explore how AI-driven solutions can enhance productivity, quality, and safety while making companies more resilient and cost-effective.

Manufacturers who want to remain competitive can’t adopt a wait-and-see approach. Instead, they need to start preparing their organizations for AI by establishing their data infrastructure, equipping their teams with the necessary skills, while also exploring where AI adoption will provide the most tangible and immediate benefits. Those initial wins can then be scaled into broader solutions that create a long-term competitive advantage.  M

About the authors:

Jim Blackwell is market leader at BDO Digital.

Maurice Liddell is manufacturing market leader at BDO Digital.

R. J. Romano is supply chain managing director at BDO USA.

Manufacturers have a treasure trove of data that GenAI can use to enhance performance, agility, and growth.

TAKEAWAYS:
● By combining actual historical product data and simulated insights, manufacturers can unlock new levels of innovation and competitiveness.
● More manufacturers are harnessing GenAI—Eaton, for example, is using the technology to cut product design time by nearly 90 percent.
● Manufacturers that combine robust data sets with clear GenAI use cases are well-positioned to harness the transformative power of GenAI.   

Manufacturers are already seeing glimpses of how artificial intelligence (AI) is reshaping the industry. Applying AI to R&D/product design can have a force multiplier effect across the entire product development lifecycle. Companies can use generative AI (gen AI) to develop new products at lightning speed that are already optimized for cost, carbon, performance, and even factory location. To illustrate the focus in this area, Bain & Company¹ reports that 75 percent of manufacturers surveyed list AI and related technologies as their top engineering and R&D priority.

But missteps today could leave companies adrift amid the AI sea change and unable to navigate new market realities. Proactive manufacturers are addressing how gen AI capabilities differ from traditional AI, are defining specific gen AI use cases for their needs, and are taking steps to generate value from this new technology.

What is Gen AI?

Generative AI represents a new frontier in AI (ChatGPT may be the most well-known example). Traditional AI, also known as deterministic AI, applies pre-programmed rules and algorithms to make decisions. Traditional AI systems solve well-defined problems—for example, determining the most effective manufacturing process based on the properties of a specific part—and perform repetitive tasks.

Instead of using predetermined rules, gen AI identifies data patterns to create new, unique content. This requires accurate data, machine learning (ML) for powerful analysis, and large multimodal models (LMMs) to process and generate information across multiple formats, including text, images, and video.

Data Quality: The Launchpad for AI Innovation

Financial services and other industries are awash in data because different industry sub-segments—such as consumer banking and asset management—can use similar data sets to create AI models for automated customer service and other applications. But that scale doesn’t apply to manufacturing because many sectors have different operating models (think high-volume consumer electronics manufacturing vs. low-volume products for aerospace or other highly regulated industries).

To compensate, manufacturers are combining actual historical product data and simulated insights to provide the data volume and quality required to make informed decisions.

How to Capitalize on Actual Historical Information and Simulated Design Data

A manufacturer’s actual historical data can include design files of its popular products, a list of its highest-margin products, product costs, production volumes, and preferred supplier information. It can also feature detailed performance data regarding company-owned factories and/or production lines.

However, analyzing a manufacturer’s actual historical data doesn’t typically help to identify areas for cost or time savings. For example, how do manufacturers know if they’re overpaying for a component if they only have quote and payment information?

To gain these types of insights, manufacturers rely on simulation and modeling to identify opportunities for improvement across the organization—including design for manufacturing (DFM), cost modeling, sustainability insights, and structural performance (FEA analysis). These simulation applications provide additional analysis and guidance to optimize an array of variables.

“Missteps today could leave companies adrift amid the AI sea change and unable to navigate new market realities.”

Integration across applications and platforms is central to harnessing all manufacturing information effectively. With complete control over product data, manufacturers can instantly adjust shop floor labor rates for a plant in Taiwan or update raw material cost data to reflect inflationary pressures.

Manufacturers who understand why there are discrepancies between actual historical data and insights from simulation applications can use this knowledge to build precise AI models based on the most accurate information and establish parameters from multiple types of data.

Eaton Spotlights the Power of Gen AI

Eaton is a $23 billion intelligent power management solutions provider for industrial and manufacturing industries. Customers regularly require customized Eaton components/products for their new product development initiatives, which can range from passenger car valve stems to lighting fixtures.

Due to technical complexity, it can take Eaton months to complete a manual product design. For example, a lighting fixture design can require input from thermal, electrical, mechanical, optical, and manufacturing engineering.

“Eaton’s vision is to take our traditional design processes from months to minutes,” said Uyiosa Abusomwan, senior global technology manager of Digital Design and Engineering at Eaton.

Eaton’s gen AI capability is built on a robust set of actual historical product design data and insights from the company’s simulation software portfolio—including aPriori for cost modeling, DFM, and sourcing. Eaton combines this information to create detailed model-based design specifications and properties to support its gen AI development.

With gen AI, Eaton runs thousands of design iterations in minutes (or less) and proposes the top five designs. Once the designs are fed through a high-fidelity simulation, Eaton’s digital design and engineering team conducts a detailed review. This workflow empowers the Eaton engineering team to review AI outputs for product validation and quality control, and to streamline decision-making.

Result: Eaton Cuts New Product Design from Months to Minutes

Eaton’s impressive results from its high-fidelity gen AI initiative include the following:

• Minimizing the weight of a liquid-to-air heat exchanger by 80 percent
• Lowering the design time for a high-speed gear by 65 percent
• Reducing the design time for an automated lighting fixture by 87 percent

Eaton’s gen AI capabilities support the company’s goal to scale new product development and accelerate time-to-market to address customer needs. The technology could also support the company’s goal to become carbon neutral by 2030.

Take the Next Step In Your Gen AI Strategy

Despite some early forays into gen AI, manufacturers are still primarily laying the groundwork in this area. The MLC’s “Future of Industrial AI in Manufacturing” survey reports that 28 percent of respondents have gen AI projects that “passed the pilot stage.” What’s revealing is that more than half of those surveyed aren’t incorporating gen AI into their digital transformation strategies, and nearly two-thirds aren’t measuring the impact of their AI investments.

Gen AI technologies continue to evolve rapidly in this dynamic field. Given the pace of innovation, it’s hard to predict what AI algorithms will be capable of during the next few years. However, the need for accurate, robust data are a constant pillar for AI and other business-critical operations.

Today, companies have a wealth of information to harness for short-term gains and long-term success: traditional AI engines that power product design, sourcing, and digital factory simulation—along with their actual product data. Gen AI is uniquely positioned to transform the entire product development lifecycle. Manufacturers that act quickly and strategically are well-positioned to gain new levels of performance, growth, and agility.  M

About the author:

Philippe Adam is the chief marketing officer at aPriori.

References:
1.     Bain & Company. “Bain’s Global Machinery & Equipment Report 2024.” 2024.
2.     Manufacturing Leadership Council. The Future of Industrial AI in Manufacturing. 2023.

Generative AI is increasing the potential for data to fundamentally change the way manufacturers operate throughout the manufacturing value chain — and bring significant value for the workforce.  

TAKEAWAYS:
● Data currently is seriously underused at the operational level, leading to wasted potential for performance improvement.
● GenAI can empower worker efficiency and effectiveness — if they use it correctly. The key is focusing on how AI can augment workers’ expertise and make them more efficient and productive.
● Most manufacturers don’t collect and retain the data needed to benefit from AI. This is a good place to start.

In the Manufacturing Leadership Council’s study, Manufacturers Go All-In on AI (October 2023), nearly half of executives cited AI/machine learning (ML) as the technology they expect to have the most future impact on manufacturing operations — more than any other mentioned. Almost half — 47% — expect it to be a game changer by 2030. In Rockwell Automation’s State of Smart Manufacturing Report, generative AI was the No. 1 area for technology investment in the next 12 months, and 83% of those polled anticipate using it in 2024.

West Monroe’s survey of mid-sized manufacturers, The State of Manufacturing, shows companies are realizing the benefits from AI and ML, and increasingly infusing data into their operations. But one finding stood out: While 84% of companies surveyed use data extensively for decision-making at the executive level, that does not carry to other levels. Only 38% of middle management uses data extensively, and 47% of operational staff rarely use data.

Think about the wasted performance potential that statistic implies. Leveraging data in real time on the operations floor can help employees think more strategically, make informed decisions that reduce costs and improve margins, and drive businesses forward. That’s where AI comes in. It harnesses exponential volumes of data currently going unused to improve manufacturing operations — putting insight in the hands workers that makes them more effective and efficient.

But first, workers need to become comfortable with AI. Real or not, perceptions abound that AI will replace human work and jobs. In The Future of Industrial AI in Manufacturing, executives were mixed on this point. Nearly half (45%) said they don’t expect an impact on the workforce. But a sizeable minority, 21%, do see it decreasing the size of the workforce.

To unlock value for the workforce, manufacturers should be focusing on AI as a way to transfer knowledge “within the four walls” and augment workers’ expertise—empowering them perform day-to-day responsibilities better and more efficiently. Following are some principles for doing so.

Ensure Employees Are Using AI Right 

According to Microsoft, 75% of knowledge workers are already using GenAI at work. But in our opinion, many are using it wrong. They are defining their own tools and approaches, without sufficient monitoring or governance. It’s up to leadership to convince employees to use it the way the company wants. Understand that guiding appropriate GenAI adoption requires:

• Both a top-down and bottom-up strategy
• More of a cultural movement and less of a mandate
• Trust and mentorship
• Success metrics defined on both at the company and individual level

Educate Everyone — Continuously

Because AI is a rapidly evolving discipline, education isn’t a one-and-done project. It requires continuous focus and effort. And it involves everyone—from senior executives to the shop floor. Seek to learn everything you can about the fundamentals of large language models (LLMs), the options, and the skills required. Don’t just read about it. Engage with manufacturing peers or other organizations to share experiences and points of view.

Given the buzz around GenAI, it is particularly important to understand the differences between this form of AI and the broader concept of AI/ML. In a mature state, both AI/ML and GenAI may play roles in optimizing manufacturing operations. A good example is machine maintenance. By putting sensors on equipment, you can use analytics to predict when the machine will need maintenance. That is AI/ML. When the technician is performing maintenance has questions, GenAI can provide answers rapidly, in an easily understandable format.

Pursue the Right Use Cases

We see many organizations trying to explore as many use cases as possible rather than focusing on a handful of the most promising ones. Casting a wide net is a good starting point, but we guide clients to use a value-identification exercise to build a prioritized funnel of potential use cases for further exploration. Make employee efficiency, productivity, and/or effectiveness part of the value formula.

Every function will have its own high-value use cases, but in manufacturing operations, we see three that have significant potential for empowering the workforce:

Reduce the time to output. PLC programming is a good example of this. Say it currently takes one day to code a PLC. With support from GenAI, which augments knowledge and quickly iterates ideas for the desired output, a programmer could produce code in 60% of the time — a 40% efficiency gain. Here, AI isn’t replacing people, but it is helping them to work faster.

Manage uncertainty better. Unanticipated scenarios often disrupt monthly, quarterly, or yearly plans. Machines break down, people don’t show up for work, or defects materialize. In the rush to get back on track, there usually isn’t time to gather and analyze all the potentially relevant information needed to make the best possible decision. AI makes it possible for users to access and analyze data from more sources, internal and external, to reach conclusions that otherwise may not have been possible — injecting a greater degree of reliability into operations. For example, a large Japanese steel manufacturer implemented AI for predictive maintenance and to optimize blast furnace operations, achieving significant cost savings, operational efficiency, and reduced unexpected downtime.

“Given the buzz around GenAI, it is particularly important to understand the differences between this form of AI and the broader concept of AI/ML.”

Knowledge retention and transfer. In West Monroe’s manufacturing poll, 95% of respondents said they worry about the impact of an aging workforce — a key concern, of course, being loss of institutional knowledge. Here again, AI, and particularly GenAI, may be useful for capturing and sharing that knowledge before it walks out the door. For example, create a simple standard root-cause analysis form and begin using it to capture data from operators every time there is an issue. You can then train an LLM to analyze that database of information, along with SOPs, best practices, and troubleshooting guides. Workers grappling with an issue can query the LLM to access relevant policies, instructions, or suggestions.

Integrate AI with Tools Familiar to Workers

One of the beneficial features of AI is the ability to integrate with other systems (relatively) easily. As a result, users can benefit from its capabilities without having to learn an entirely new tool. A worker can query a familiar interface — for example, a commercially available manufacturing intelligence/analytics platform that uses the data from the MES system — to retrain pretrained models to get customized answers for problems that are very specific and contextual to the manufacturer, or even to a specific facility.

In addition, the insights can be made available to worker in a tool familiar to the worker, thus reducing adoption challenges. Industrial co-pilots that can collected MES and other manufacturing data and then leverage the power of GenAI to provide insights in an easy-to -understand form. An example interaction would be where a plant manager can query the Co-pilot in plain language to “forecast the energy consumption of the blast furnace for the next week” and receive an easy-to-understand line chart forecast.

Shore up Your Foundation for Using AI

The idea of using AI to predict machine failure and maintenance requirements is enticing, but the reality is that most companies don’t have the essentials — including job plans or accurate data — to address common failures. Many do not routinely review maintenance procedures for specific equipment. Some don’t have documented procedures at all. The same applies to standard operating procedures. They may exist, but they may be out of date.

One of the most important foundational elements for AI is good data. Many manufacturers don’t collect or retain the data needed to benefit from AI, so that is a starting point. Some collect data, but haven’t “cleaned” it (i.e., detecting and correcting corrupt, inaccurate, or duplicate records from a database) so that analytics tools can produce useful insight. Data hygiene is mundane and laborious — but ignoring it and expecting AI to be able to overcome issues will ultimately lead to suboptimal impact. Think garbage in, garbage out.

If you have high hopes for leveraging AI to elevate performance, start by fixing these core building blocks. An easy way to think about this is cleaning up the dirty laundry that’s been building over the years. Every manufacturer has a pile of it. And every little bit of work to address it will ease the ability to employ and benefit from AI.

Don’t Underestimate the Change Management

For AI to truly become a tool that augments work and improves efficiency, the workforce must become comfortable with and understand it — including what is changing, why, and what’s in it for them. Leadership must actively dispel the myth that machines are here to replace workers. This is also a great opportunity to instill a deeper understanding of work, how individuals’ roles impact performance and how the introduction of GenAI or AI will change work. The change management plan should reflect this.

“Look for ways to begin infusing GenAI into the daily responsibilities of those doing knowledge, leadership, or decision-making work by explicitly making it part of their roles.”

Change management will require a shift in focus from training to learning, as well as new methods of delivering insight that emphasize coaching and mentoring rather than classroom education. In the MLC’s study, linked above, 65% of companies have yet to allocate specific budgets for AI training, highlighting the potential challenges for future workforce readiness.

One way of acclimating workers to change is a “quiet pilot.” For example, you can introduce a small-scale GenAI-powered “how-to” guide within an existing application. This guide can provide prompts and assistance based on the user’s role, helping them discover and use the AI tool independently. This approach introduces people to the concept of AI without making it seem like a big change. It also allows for quick learning and adjustments that can be applied to future investments.

Build GenAI into Roles

Finally, look for ways to begin infusing GenAI into the daily responsibilities of those doing knowledge, leadership, or decision-making work by explicitly making it part of their roles. While this is a recommendation for “right now” it is also encouraged in how manufacturers frame and design jobs going forward. Weaving GenAI activities into roles and responsibilities challenges managers and leaders to re-think the way work can be done. And adding “Experience leveraging GenAI in daily activities” into the knowledge, skill, and experience sections of position descriptions helps to groom the candidate pool, while exploring GenAI skills and experiences in interviewing prospective employees enables manufacture to truly begin building the workforce of the future. This combination reinforces that in most cases GenAI isn’t “a job” but rather a way of working more efficiently in many different jobs.

Take Action—and Start Adding up the Value

The Rockwell Automation State of Smart Manufacturing Report confirms what many manufacturers know: they are using a relatively low percentage (44%) of data effectively. AI can help you begin boosting this right away — and spread the impact from the executive suite down to your operational workforce. The key is focusing on how AI can augment workers’ expertise and make them more efficient and productive. This takes coordinated effort around people, processes, and technology, but the steps above will point your organization in the right direction.  M

About the authors:

Sujit Acharya is a Managing Director with West Monroe’s Technology Practice.

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

Kris Slozak is Director, Consumer & Industrial Products, with West Monroe.

Glenn Pfenninger is a Director, Human Capital Management, with West Monroe.

As manufacturers move toward building smarter factories with connected machines, the data those systems produce can offer a host of benefits: improved efficiency, better productivity, informed decision-making, value creation and, ultimately, competitiveness. Yet becoming a data-driven business comes with its share of challenges. In this year’s Manufacturing in 2030 Survey, Data Mastery: A Key to Industrial Competitiveness, the NAM’s Manufacturing Leadership Council sheds light on the successes and opportunities for how manufacturers are transforming their operations with data.

Security and privacy concerns: As factories become more connected, cybersecurity becomes a greater imperative. For this reason, survey respondents validated that both data security and data privacy are essential.

• More than 90% of respondents have a formal or partial policy on data security and data privacy.
• About two-thirds of manufacturers have a formal or partial policy on data quality.
• More than 60% have a corporate-wide plan, strategy or guidelines for data management, but only 15% follow the plan in its entirety.

How data is used: As manufacturers advance along their M4.0 journey, data is becoming their lifeblood, driving insights and decision-making. Yet the survey revealed a gap between available data sources and their utilization, a notable area for improvement as the industry looks toward the future.

• Spreadsheets are still king: 70% of manufacturers enter data to them manually, and 68% still use them to analyze data.
• 44% of manufacturing leaders say the amount of data they collect is double what it was two years ago, and they anticipate it will triple by 2030.
• While nearly 60% of manufacturers use data to understand and optimize projects, there is a shift toward using data to make predictions about operational performance, including machine performance, in the next decade.

Business impact: Most manufacturers leverage data to find ways to save money or promote business growth. However, less than half have a good understanding of the dollar value of their data.

• Only about 25% of manufacturers have high confidence that the right data is being collected.
• Most manufactures have only moderate confidence in their analytic capabilities.
• Top challenges include data that comes from different systems or in different formats (53%), data that is not easy to access (28%) and lack of skills to analyze data effectively (28%).
• However, despite those challenges, 95% of manufacturers say data makes for faster and/or higher-quality decision-making.

The bottom line: An overwhelming majority of manufacturers (86%) believe that the effective use of manufacturing data will be “essential” to their competitiveness. But to realize data’s potential, manufacturers must figure out how to organize and analyze their data effectively, ensure that their data is trustworthy and align their business strategy closely with their data strategy.

Explore the survey: Get a deeper look at the current state of data mastery in manufacturing. Click here to download your copy.