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What was once expensive and burdensome is now practical, affordable, and essential. 

TAKEAWAYS:
● AI is democratizing data governance, making it practical and impactful for middle market manufacturers.
● Clean data isn’t just technical hygiene—it’s a strategic advantage that drives operational efficiency and trust.
● Modern AI-powered platforms simplify governance, reduce errors, and bring governance directly into daily operations.   

AI is at the center of the modern manufacturing conversation. From predictive maintenance to generative design, manufacturers are racing to capture its potential. But without high-quality data, AI falls flat.

AI can only be as good as the data that powers it. If that data is messy, incomplete, or siloed, the results will be inaccurate—or even misleading. Poor data quality costs organizations an average of $12.9 million per year.

For years, governance was out of reach for most middle market manufacturers. It required major IT investments, specialized skills, and years of effort. It was often treated as compliance—not a competitive lever. Thanks to AI, that’s changed. Governance is now faster, easier, and cost-effective, transforming from a back-office burden to a frontline advantage.

Three Catalysts Driving Adoption

AI-Powered Discovery

Agentic AI workflow solutions can automate the heavy lifting of creating data pipelines, applying transformations, and documenting processes. What once required weeks of coding can now be done in days, freeing teams to focus on applying insights instead of preparing data.

Lower Barriers to Entry

AI-driven platforms uncover inconsistencies, reconcile mismatched records, and track data lineage with minimal manual intervention. Middle market manufacturers—who once lacked the resources for governance—can now access enterprise-grade tools at a fraction of the cost. The result: faster results, lower costs, and fewer errors.

Expanded Awareness

Governance is no longer confined to IT. Operations leaders and plant managers recognize that poor data leads to poor outcomes, whether in forecasting, scheduling, or supply chain performance. Clean data builds trust, accelerates decisions, and unites teams around a single version of the truth.

Why Middle Market Manufacturers Should Care Now

Margins are thinner, teams are leaner, and inefficiencies hurt more in the middle market. Bad data drives operational waste: duplicate work, flawed decisions, and costly rework. Historically, manufacturers filled gaps with intuition or tribal knowledge, but that no longer works in today’s fast-moving environment.

Governance enables fact-based decision-making. With governed data, manufacturers can pinpoint root causes, adapt to supply chain disruptions, and optimize production in real time. Without it, they risk reacting too late or misdiagnosing problems altogether.

Governance is now faster, easier, and cost-effective, transforming from a back-office burden to a frontline advantage.

AI only raises the stakes. Gartner predicts 60% of AI initiatives will fail to deliver business value by 2027. Other studies put that number closer to 80–85%. For middle market manufacturers, governance isn’t just IT hygiene—it’s a business-critical strategy.

From Theory to Practice: AI Makes It Work

Traditional governance stalled because it was slow, costly, and disconnected from daily work. AI has changed that. By embedding governance into workflows, AI allows manufacturers to manage data quality in real time.

Instead of hand-building scripts, AI enables teams to define desired outcomes and generate pipelines automatically. Errors are flagged early, and documentation is created instantly. Governance shifts from “fixing after the fact” to enabling usable, trusted data from the start.

But tools alone aren’t enough. The real payoff comes when governance is paired with process change. Clean data must actively inform decision-making. When governance and process improvements move together, companies unlock measurable gains in speed, quality, and cost.

Unlocking Fast Wins and Cultural Change

Many leaders assume governance takes years to show value. In reality, small pilots deliver impact fast. As soon as teams begin using governed data, they see fewer errors, shorter decision cycles, and more confidence in outcomes.

With governed data, manufacturers can pinpoint root causes, adapt to supply chain disruptions, and optimize production in real time.

The bigger challenge is culture. After years of unreliable systems, skepticism runs deep. Overcoming that requires leadership. Executives must position data as a strategic lever, not a side project, and build trust through quick wins. A pilot that reduces downtime or shortens reporting cycles can spark belief across the organization.

Trust builds momentum. With each success, adoption grows, skepticism fades, and governance becomes how the business works—not an IT mandate, but a cultural norm.

A Call to Action

For middle market manufacturers, this isn’t just a technology trend—it’s a turning point. Data governance isn’t a barrier; it’s a business enabler. It reduces waste, accelerates insights, and lays the foundation for AI-powered resilience.

The technology is ready. The tools are affordable. The ROI is proven. What’s left is a choice: keep patching data problems, or fix them for good.

It’s time to stop duct-taping data issues. Let’s build a foundation that enables AI, accelerates decisions, and drives performance. Start small. Prove value. Build trust. And position your business to lead with clean, governed data.  M

About the authors:

Annemieke De Groot is Data & AI Governance Lead at West Monroe.

Jeff Pehler is Managing Director, Consumer & Industrial Products at West Monroe.

Scott Saueressig is Senior Manager at West Monroe.

Robust data sharing in a collaborative data ecosystem (CDE) scales qualified data and widens access to untapped operational advantages for manufacturers. 

TAKEAWAYS:
● Smart Manufacturing leverages large volumes of industry-qualified data to orchestrate applications comprehensively at multiple operational scales, but data access remains a barrier.
● Data sharing combined with Data-first site strategies recognize the need to first process raw operational data into AI-ready data for any AI, machine learning, or digital twin application to work.
● Manufacturers and engaged factory staff can agree and execute on cross-site data processes, guardrails, and shared workforce training for qualified, scalable, and trusted data sharing.   

Smart Manufacturing (SM) defines the orchestration of advanced digital technologies used to construct scaled software systems. Data are used in artificial intelligence (AI), machine learning (ML), and digital twin (DT) applications to enable data-driven insights and decision-making, automation/autonomy, and scaled interoperability within and across physical and human control and management systems. This results in improved products, reduced energy and material usage, and enhanced productivity, responsiveness, and resilience in manufacturing operations, enterprises, and supply chains. The workforce becomes more effective, productive, and engaged.

Economic opportunities and barriers with data sharing have been explained in studies conducted since 2020.³ The potential for substantial operational value is significant (Table 1), but data access remains a barrier. Processing of manufacturing data is often not prioritized, and when it is, it’s rarely done well or consistently across applications. It remains largely closed for application, tool, and training development. Like mined minerals, raw operational data hold little value in manufacturing until the data are qualified, refined, concentrated, and processed in sufficient quantities.

Smart manufacturing that orchestrates and scales AI/ML/DT applications leverages large volumes of factory data to create AI-ready data, which are consistently and persistently contextualized, qualified, prepared, and engineered for various applications at multiple operational scales. Consistency in data processing is a key objective. A Data-first strategy emphasizes the need to convert raw operational data into AI-Ready data for any application to be effective. All manufacturers—small, medium, and large—have valuable data and contribute to a broader manufacturing ecosystem. We refer to collaborative factory/company sites that share data as a collaborative data ecosystem (CDE).

Table 1: Industry-Defined Points of Economic Value for Smart Manufacturing Collaborative Data Ecosystems (CDEs) that can Scale Data and AI

A Workshop to Benchmark the Value of Data Sharing

A workshop sponsored by the National Science Foundation (NSF) and supported by the National Institute of Standards and Technology (NIST) titled “Artificial Intelligence with Open and Scaled Data Sharing in the Semiconductor Industry,” aimed to benchmark the potential of scaled data sharing while addressing significant barriers. It brought together 32 factory engineers and data scientists from 12 semiconductor manufacturers. Additionally, 27 participants, including data scientists from academic institutions across the country, industry experts on information technology (IT) and operational technology (OT) infrastructure, specialists in price analysis and equipment building, and government leaders in advanced manufacturing contributed by challenging, proposing, and reviewing paths forward.

The workshop focused on existing technologies (no R&D) and benchmarking near-term benefits. A Seagate/UCLA project team benchmarked the economic value points related to the data processing and engineering necessary to build a virtual metrology application for enhancing productivity. Wafer production datasets from five etch machines at different sites, used for similar operations across different products, were qualified, categorized, prepared, and engineered into AI-ready data. A common data information model was developed for all five machine tools using the CESMII SM Profile^TM4 to encode the data model in a digitally standard form. Data information modeling was also demonstrated on chemical mechanical planarization (CMP) machines at three company sites.

Executing on Consistent Data Processing as a CDE

Executing as a CDE required a commitment to a governance structure that ensured trust in site qualifications, consistent data processes, security, IP protections, and model validations. Factories and companies needed to collaborate on data preparation and build AI/ML models while maintaining autonomy over their products and applications. Factory site data engineers and scientists had to work together on solutions. Governance was supported by a “mindset” that challenged conventional thinking. Adhering to eight execution principles was critical for sustaining the ecosystem effort (Table 2).

Table 2: Eight Key Execution Principles for Industry Data Sharing

Business Value Basis for the Ecosystem to Form

This coalition established the CDE as a “market-driven, business entity.” This study demonstrates a CDE that is a bottom-up, business-focused entity for factories to increase the value of site data in collaboration with other factories and companies. It creates new business, revenue, and service opportunities based on data value and the benefits of jointly preparing data and building models. Interest in the CDE began with a viable business opportunity. Identifying specific operational benefits was the crucial next step. The execution principles propelled the CDE forward.

An Overall Finding about Data Processing Consistency

We highlight the key finding that data preparation and refinement consistency are best achieved through a workflow of repeatable data processing steps, which include (counterclockwise): (1) eliminating contextual and formatting inconsistencies with a common data information model as a collaborative step; (2) ensuring consistent qualification (operational quality) and formatting (including categorization of key operational features) as on-site steps; (3) maximizing pooled data processing through a workflow of collaborative steps; and (4) site validation and deployment with shared but individually applied solutions and methods (Figure 1).

Maximizing collaboration (shown in blue) while minimizing inconsistencies from site steps (in black) was essential for data processing consistency. The figure also emphasizes that consistency involves consistently selecting and applying methods for each step. Entry into the cycle is the common data information model.

 Figure 1. Consistent and Collaborative Data Refinement and Model Building

Key Benchmark Performance Findings

SM and AI/ML/DT systems can be implemented in a cost-effective manner. The CDE process was benchmarked against processing data and building the ML model independently for each machine:

1. Batch run datasets from different sites were combined to create a qualified, consistently processed, and richer 100,000 batch run super dataset.
2. ML model performance using aggregated data for predicting wafer flatness pass or fail was 30 percent to 50 percent better than performance with siloed training.

Pooled processing could achieve 3x cost savings in staffing and avoid 4 full-time equivalents (FTEs) in increased headcount across all sites.

Factory-floor staff from various sites collaborated to create a common data information model for all machines, reflecting a shared expert understanding of machine operation. Building the common data information model facilitated co-developed methods to consistently qualify data, protect IP, categorize data, address security, and share training.  Workforce training should ultimately be guided by the business value of data sharing. However, initial on-the-job training programs on data processing are needed to drive the value of data for AI/ML/DT applications.

Every success in this demonstration project was driven by the value and availability of consistently processed data. Focusing on shared data processing and engineering facilitated algorithm development and validation. Better data could be produced without increasing headcount or service requirements by pooling factory site data. If data are qualified, consistent, scalable, and trusted, cross-operational advantages follow. Cross-site, cross-factory, and cross-company data sharing is doable. A sufficient intersection of individual values and ways to address risks and barriers can be found. There is a line of sight to shared data inventories categorized with process conditions. M

About the Authors

Sthitie Bom is Vice President of Factory Data, Analytics, and Applications at Seagate.

Jim Davis is UCLA Vice Provost IT (CIO/CTO) Emeritus.

Notes:

1. The content in this article is based upon work supported by the National Science Foundation (NSF) under Grant 2334590 and further supported by the National Institute of Standards and Technology (NIST). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or NIST.

2. The detailed NSF sponsored/NIST supported Workshop report is in process to be published; Workshop Organizing Committee: Sthitie Bom, Seagate Technology (co-chair); Jim Davis, UCLA (co-chair); Said Jahanmir, Office of Advanced Manufacturing, NIST; Bruce Kramer, Office of Advanced Manufacturing, NIST; Don Ufford, Office of Advanced Manufacturing (when work was done), NIST; Greg Vogl, Engineering Laboratory, NIST.

3. Towards Resilient Manufacturing Ecosystems through Artificial Intelligence – Symposium Report, NIST Advanced Manufacturing Series, NIST AMS 100-47, September 2022; Options for a National Plan for Smart Manufacturing; National Academies of Science, Engineering and Medicine, Consensus Study Report, 2024.

4. CESMII (Collaborative Ecosystems for Smart Manufacturing Innovation Institute) sponsored by DOE; See https://www.cesmii.org/technology/sm-profiles/ for further information on the CESMII SM Profiles and the associated SM interoperability platform.

Unlock the full potential of your manufacturing operations by building a data strategy that drives visibility, innovation, and competitive advantage. 

TAKEAWAYS:
● Creating a comprehensive manufacturing data strategy enables real-time decision-making, improves supply chain resilience, and enhances cross-functional collaboration.
● To build an effective data strategy, teams require tools including AI frameworks, analytics systems, and IIoT sensors.
● Both technological and cultural changes––including leadership commitment, organizational alignment, and iterative learning––are necessary to deliver data-driven manufacturing. 

Big data empowers manufacturers to make smarter decisions and take more impactful actions. With improved access to information and advanced analytical tools, companies are unlocking new levels of efficiency and insight across their operations.

However, realizing these benefits is not automatic. Many manufacturers still face significant data-related challenges that hinder collaboration and productivity. To overcome these obstacles, organizations need a clear and effective data strategy—one that enables them to capture, organize, and apply information at scale.

The Urgency of Data in Modern Manufacturing

Modern manufacturing processes are driven by connection. This falls under the broader umbrella of Industry 4.0, which prioritizes the creation of interconnected, intelligent systems capable of self-optimization.

These systems are essential for companies to navigate increasingly complex processes. For example, in global supply chains, manufacturers must manage multiple supply chains and logistics operations simultaneously to ensure that materials and components arrive on time and within budget.

Digital transformations also present both opportunities and challenges. The emergence of artificial intelligence (AI) frameworks powered by Industrial Internet of Things (IIoT) networks lays the foundation for improved productivity. Data underpins all these processes, making it the new currency for operational efficiency, agility, and innovation. To reliably collect this currency, however, companies must have comprehensive data strategies.

The Core Elements of a Manufacturing Data Strategy

Effective manufacturing data analytics strategies share four core elements: infrastructure, interoperability, governance, and security.

• Infrastructure: Infrastructure includes the integration of legacy tools (where possible), along with enterprise resource planning (ERP) solutions, IoT devices, and cloud platforms.
• Interoperability: Interoperability speaks to the standardization of data formats, data collection practices, and decision-making across plants, regions, and business partners.
• Governance: Governance refers to clear data ownership, quality standards, and manufacturing process integrity.
• Security: Data security involves the protection of intellectual property (IP) and ensuring compliance with local, state, and federal laws.

Together, these elements create a data environment that is accessible yet secure, providing the framework necessary for companies to find actionable insights.

Common Pitfalls in Creating a Data Strategy

Creating a data strategy also comes with potential pitfalls. The first is prioritizing quantity over quality. As companies recognize the critical role of data in delivering value, the temptation to collect as much data as possible grows. The problem? Not all data is relevant to every situation. Targeted efforts tend to produce better results.

The second common pitfall is failing to connect data collection with meaningful action. Data is a resource, not an operation. Without solutions that allow staff to access and apply data at scale, strategies have limited impact.

Driving Better Decisions with End-to-End Visibility

Visibility is essential for manufacturing firms to take effective action, but it is only possible if companies have access to accurate, real-time data. For instance, with real-time performance data from production equipment, businesses can anticipate maintenance needs and conduct proactive repairs to prevent downtime.

To capture and leverage real-time data, businesses need a combination of AI tools and data analytics technologies. Intelligent solutions excel at finding and capturing relevant data, while analytics applications provide context.

Using AI and analytics to drive visibility offers three key benefits:

• Proactive response: Predictive analytics can help companies improve inventory planning, reduce machine downtime, and anticipate market demand, thereby minimizing waste.
• Elimination of data silos: An AI-powered data strategy helps eliminate data silos across departments and partner networks, promoting interoperability.
• Ongoing ROI: Visibility gains from AI and big data analytics enhance operations such as inventory management and enable businesses to respond faster when disruptions occur.

Accelerating Innovation Through Actionable Insights

AI-enabled analysis can help manufacturers move beyond reactive operations. According to recent data in an article from Georgetown University, adopters of AI supply chain tools have enhanced service levels by 65 percent, and 70 percent of those using AI say that it delivers strong return on investment. This is because AI can now take on effort-intensive manual tasks, freeing up staff to explore new and innovative approaches to traditional manufacturing processes.

Key data sources for AI include

• Machine data
• Customer feedback
• R&D information

Common use cases for this data in manufacturing include digital twins, machine learning, and quality control automation. Digital twins are digital versions of physical products or components often used to simulate the behavior of physical objects, allowing companies to monitor performance and make informed decisions. Machine learning algorithms improve over time as they are exposed to more data. Finally, quality control automation leverages AI to streamline output evaluation, helping companies track product and data quality trends and take action to reduce material waste.

Creating a Culture That Prioritizes Data and Experimentation

Innovation and experimentation require cultural shifts. While data provides the fuel for change, it is only possible if manufacturing companies invest the time and effort needed to create sustainable cultural change. This requires three actions from leadership teams:

• Promote cross-functional collaboration
• Encourage experimentation and iterative prototyping
• Empower frontline teams with tools and technologies.

Roadmap for Building a Sustainable Data Strategy

While data sources and operational objectives differ across organizations, five steps are common in strategy building:

1. Assessment: Evaluate your current data collection practices, connectivity, and collaboration, and pinpoint any data silos. This helps identify valuable use cases.
2. Governance setup: Create your data governance framework. Decide where data will be stored, who will have access, which tools you will use, and how data will be tracked.
3. Data integration: With assessment and governance setup complete, begin integrating new approaches with current tools. Expect some pushback from staff and challenges with interoperability. Best bet? Slow and steady—address problems as they arise rather than trying to deploy too quickly.
4. Use case development: Define beneficial use cases, which may include improved inventory management, enhanced supply chain visibility, or proactive maintenance policies.
5. Scale: Complete steps 1–4 for several use cases. Once you are confident that the processes and policies work, start scaling your strategy across other networks and partner environments.

Pro tip: Focus on small wins powered by case-driven rollouts. These wins serve as proof-of-concept for your data strategy and are instrumental in creating frameworks that deliver value at scale.

Delivering Data Strategy: A Competitive Imperative

Data strategy for manufacturers is not just a nice-to-have—it is now a competitive imperative.

The advent of advanced IIoT, AI, and automation tools allows companies to collect, curate, and apply data in real time, empowering forward-thinking firms to discover new ways to improve operations and streamline supply chains.

In a manufacturing market defined by global instability, labor shortages, and rising demand volatility, companies that master data will lead the way in efficiency, sustainability, and innovation. M

About the author:

Eric Wrigley is general manager of predictive technologies at Advanced Technology Services.

While every new business starts with big dreams, only a few rise up to those lofty early expectations, and even fewer exceed them. While United Scrap Metal founder Marsha Serlin certainly had a vision for her company’s future, it’s hard to believe she knew the impact her fledgling business would have when she started it in 1978 with $200 and a rental truck.

While the company headquarters still sits at the same Cicero, IL, location where it first began, several expansions over the years have built it up to a 50-acre site. The company now has 10 additional locations in its portfolio and is spread over nine states, bringing in annual revenue of $725 million. MLC members took part in a tour at USM’s headquarters location on August 13, learning about these accomplishments and ambitions, as well as the operations and technology behind industrial recycling.

USM’s business is recycling scrap metal from both residential and commercial customers, producing material commodities for mills, foundries, casting and specialty markets. This includes ferrous and non-ferrous materials and some non-metallic materials such as paper, plastic and wood.

Risk management is imperative for any recycling business and USM puts a high emphasis on materials integrity, taking care not to accept anything that could harm their employees or customers. This includes not just dangerous substances, such as refrigerant left behind in air conditioning units, but also ensuring that materials don’t have dubious origins – catalytic convertors are not accepted, for example, due to their frequent theft. Materials are integrated into a digital tracking system at intake. The company utilizes a real-time inventory management platform that tracks location, process status, and so on for materials as they move through processing.

Like many manufacturers, USM is beginning to integrate AI into its operations, with some current use cases in customer service, fleet management and predictive maintenance. The company is making inroads on AI-driven data analysis to make informed decisions and uncover some of the “hidden links” that might lie within production data.

Beyond its operations, USM also emphasizes its culture and community service. The company places a high priority on continuous improvement and teamwork at every level of the organization. They are transparent on their goals to give team members an incentive to perform. While the company has grown by acquisition, they have only brought in organizations that were the right cultural fit.

This extends to outside of the organization as well; the company has turned away customers that don’t fit if it could result in a poor customer experience later on – for example, a company with materials that USM might not be able to process and sell. In these cases, USM says they have helped those potential customers find the appropriate vendors and businesses to fit their needs.

Philanthropy is also a significant part of USM’s culture, with the company holding long-standing relationships with Ronald McDonald House Charities, Special Olympics, United Way, American Red Cross, Habitat for Humanity and others.

As the company approaches its 50^th year, they continue with that same ambitious and plucky spirit that fueled its origins. They are expanding into a recently built warehouse at their headquarters location and seeking potential acquisitions for their ever-growing location portfolio. They look to build a coast-to-coast network and to capture new market share, all while providing excellent customer alignment and investing in new process innovations.

Most of all, they seek to hire and retain the best and brightest and to live by the values that have served them­­—and acted as a catalyst for success—for nearly five decades.

Learn more about MLC membership.

Nathanael Aguilar

Senior Director Operations
Regal Rexnord

www.regalrexnord.com

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

Ted Bill

Chief Executive Officer
Wire Experts Group

https://wireexperts.com/

https://www.linkedin.com/in/ted-bill-0b49694/

Rick Camacho

Chief Supply Chain Officer
SC Johnson

https://www.scjohnson.com/

https://www.linkedin.com/in/rick-camacho/

Rich Cammanaro

Chief Executive Officer
Tech Etch

https://techetch.com/

https://www.linkedin.com/in/richard-a-cammarano/

Brian Cuttica

Senior Vice President of Sales, North America
Tacton

https://www.tacton.com/

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

Rick Davis

Chief Manufacturing and R&D Officer
Morgan Foods

https://www.morganfoods.com/

https://www.linkedin.com/in/rick-davis-5105658/

Lisa Dietrich

EVP Chief Digital Information Officer
Lincoln Electric

www.lincolnelectric.com

https://www.linkedin.com/in/lisa-dietrich-766a561/

Chris Droney

Chief Operating Officer
Milo’s Tea

https://drinkmilos.com/

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

Aviva Fink

Head of Marketing
Axion Ray

www.axionray.com

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

Joe Laberge

Vice President of Information Technology
Husco

https://husco.com/

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

Kyle McMillan

Chief Information and Technology Officer
Plexus Corporation

https://www.plexus.com/en-us/

https://www.linkedin.com/in/mcmillan-kyle/

Brad Southwood

President – MacLean Fogg Component Solutions
MacLean-Fogg

www.macleanfogg.com

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

AI is offering manufacturing benefits to the bottom line while also providing a path toward expansion into new products and services 

TAKEAWAYS:
● AI and machine learning can unlock performance improvements by unraveling hidden patterns and allowing for data-driven decisions. workers with real-time information, guidance, and feedback.
● Operational benefits include improved quality, reliability, dynamic scheduling, optimized supply chains and enhanced product design.
● Successful AI deployment requires a strategic approach such as ensuring data readiness, developing skills, and planning to scale.  

Artificial intelligence is transforming manufacturing, equipping organizations with advanced tools to overcome challenges and accelerate performance. AI technologies like machine learning unlock opportunities to enhance quality, reduce downtime, meet sustainability goals, and stay competitive in today’s rapidly changing marketplace.

The Role of AI and ML in Manufacturing

AI simulates human intelligence for tasks such as decision-making and pattern recognition. Machine learning automates model building using data and through making self-improvements over time. These and other related technologies rapidly process large data sets, unravel hidden patterns, and enable factories to make data-driven decisions at scale, tackling core challenges in real time.

Key manufacturing hurdles, such as equipment breakdowns, schedule disruptions and inefficiencies, are now addressed with real-time monitoring, predictive analytics, and automated optimization thanks to AI and ML.

Key Applications in Manufacturing

Quality Control
AI-driven systems use cameras and sensors to analyze products in real time and compare those images to 3D models to rapidly identify defects that manual inspections might miss. Machine learning uses historical quality data to predict and prevent emerging quality issues, ensuring high precision and reducing costly rework while minimizing waste. In addition, AI is now being used for “predictive quality” ­ – using historical data, data analysis, and predictive algorithms to anticipate and prevent quality issues before they happen.

Predictive Maintenance
Sensors collect operational data (like vibration and temperature), while ML algorithms compare that operational data to historical operational data that preceded failures in the past to predict future failures before they happen. This proactive approach extends equipment life, lowers unplanned downtime, and helps maintenance teams shift from routine checks to targeted interventions, conserving resources and budget.

Production Scheduling
AI optimization improves production schedules by balancing resources, deadlines, and disruptions. It dynamically updates plans to keep production flowing efficiently, even during equipment failures or sudden order changes. Utilizing ML for scheduling extends this even further by analyzing the root causes of past schedule deviations and proactively suggest more informed plans to avoid future issues/delays.

Key manufacturing hurdles, such as equipment breakdowns, schedule disruptions and inefficiencies, are now addressed through AI and ML.

Sustainability
AI enhances the sustainability of operations by cutting energy use and waste through collecting data such as energy consumption and water usage and then using ML to analyze resource consumption, pinpointing opportunities for energy savings. AI can also be used to refine logistics and supply routes, reducing environmental footprint and transportation costs.

Supply Chain Optimization
AI offers an unprecedented ability to respond to changes and opportunities by analyzing real-time data, exploring scenarios within constraints, predicting disruptions, and recommending timely alternatives. By forecasting demand surges or material shortages, it equips manufacturers with the ability to pivot quickly, maintaining operational continuity and helping them meet their business goals.

Assisted Machining Programming
Programming for machining equipment like CNCs is labor-intensive, requires significant training and experience, and is critical for quality. AI can collect and examine historical data using ML to generate optimal tool paths in seconds, improving throughput and accuracy and extending tool life, especially helpful for less experienced workers.

Custom Product Design
Generative AI tools create innovative product designs by processing constraints such as cost and durability. This empowers manufacturers to offer personalized products in sectors with rising customization demands.

Benefits of Artificial Intelligence in Manufacturing

• Greater Productivity
  AI automates routine processes, enabling teams to focus on strategic work and enhancing overall throughput.
• Cost Savings
  AI optimizes resource usage, reduces employee time, minimizes downtime, and improves equipment reliability. These capabilities reduce repair expenses, labor costs, and waste, delivering significant cost savings.
• Improved Decision-Making
  Manufacturers use real-time analytics to adapt to disruptions, make quick strategic decisions, and allocate resources effectively.

A proactive approach extends equipment life, lowers unplanned downtime, and helps maintenance teams shift from routine checks to targeted interventions, conserving resources and budget.

• Consistent Quality
  Automated quality checks lead to fewer defects and recalls, building stronger customer trust and brand reputation.
• Stronger Sustainability
  Optimized energy use and material management advance sustainability goals and help satisfy increasing regulatory and consumer expectations.
• Innovation and Competitive Edge
  By leveraging AI, manufacturers foster innovation, whether through adaptive systems, quicker design iteration, or more agile supply chains.

Real-World Success Stories

Artificial Intelligence is already elevating performance across the manufacturing spectrum:

• Automotive: One manufacturer used automation to inspect more than 400 brackets and welding points, reducing quality inspection time by 75% and improving inspection accuracy by over 90%.
• Aerospace: Using AI-driven quality inspection, one aerospace manufacturer was able to reduce the risk of missed quality defects by 90% while reducing overall quality inspection time by 40%.
• Consumer Goods: A consumer packaged goods manufacturer is using AI-enabled logistics and supply chain planning to reduce sourcing and upstream transportation cost by 6-7% while reducing plan preparation time by 40%.

Other innovations such as factory layout design with generative AI and synthetic data for safer training continue to shape the future landscape.

Implementing Artificial Intelligence in Manufacturing

Success with AI requires a structured, strategic approach:

• Data Readiness
  Reliable AI depends on quality data. This requires an investment in sensors and IoT infrastructure to ensure robust, accessible operational data and, even more importantly, a clearly defined and strictly enforced data governance process to ensure the data collected is valid, applicable and accurate.
• Start with Pilot Projects
  Analyze current areas of cost or inefficacy so that you can then focus on high-impact areas to validate performance and demonstrate value early on. You may find areas like predictive maintenance or quality inspection can provide quick time to value, but don’t limit yourself to evaluating those areas.
• Develop Skills and Promote Collaboration
  Break down silos between IT, operations, and data teams by creating cross-functional teams to foster collaboration. Provide employees with training to effectively use AI systems, ensuring they feel confident in working alongside these technologies.

By starting small, leveraging data effectively, and planning for scalability, manufacturers can unlock the full potential of AI.

• Use External Expertise
  Partner with proven AI experts – service providers or consultants – to bridge knowledge gaps and accelerate solution deployment.
• Proceed with Transparency
  To increase employee buy-in, adopt AI systems that are easily explainable to your employees and that make decisions transparent and easy to understand.
• Plan for Scalability
  Choose AI solutions that can scale across multiple production lines, facilities, or supply chains, ensuring long-term value as your operations grow.
• Track Measurable Impact
  Demonstrate the value of AI by tracking key metrics, such as reduced downtime, higher output, or lower energy consumption, to gain executive support and secure further investment.

The Path Forward

Artificial intelligence is transforming manufacturing, addressing challenges in quality, productivity, cost, and sustainability. As AI and related technologies continue to evolve, manufacturers have an unprecedented opportunity to innovate and gain an edge in increasingly competitive markets. By starting small, leveraging data effectively, and planning for scalability, manufacturers can unlock the full potential of AI. The combination of advanced technology and human expertise offers limitless possibilities for progress. The time to embrace this transformation is now – those who act today will lead the industry tomorrow.  M

About the author:

Mike Bradford is Director, Strategic Business Development, at Dassault Systémes.

MLC’s Future of Manufacturing Project report assesses AI’s current state and future promise. 

What separates manufacturing’s AI leaders from the laggards? To find out, MLC’s Future of Manufacturing Project surveyed top operations leaders about their current use cases, deployment challenges and future aspirations for AI. The result was a comprehensive report, Shaping the AI-Powered Factory of the Future.

Some of the report’s key findings include:

• 68% of respondents said that AI will be foundational to future competitiveness.
• Lack of useful data is the top challenge to AI adoption.
• Individual contributors lead the majority of AI implementations, more frequently than company directives or tech providers.

Find out how manufacturers are measuring AI’s impact, their primary objectives for using AI, and what is hindering its broader adoption. This report can help you benchmark your company’s current AI usage and assist in creating an AI roadmap.

MLC members can access the report by logging into the MLC member portal and navigating to this link.  M

As artificial intelligence reshapes manufacturing competitiveness, organizations must act decisively or risk falling permanently behind their AI-enabled competitors. 

TAKEAWAYS:
● AI technologies are enterprise ready now, and the benefits are quantifiable. The gap between leaders and laggards is widening.
● Crowd-sourcing innovation at the individual contributor level is highly efficient for finding new solutions to evergreen problems.
● Don’t forget training and enablement – lack of training is an emerging key blocker in accelerating value.  

The Rapid Pace of AI Evolution

A few years ago, the adoption of machine learning capabilities by manufacturers was limited. Computer vision models for quality (e.g. defect detection) were a common use-case, but broad adoption was limited by a host of issues, among them: the complexity and cost of training models, the lack of well-structured and organized data, and general hesitance about the technologies themselves by line-of-business leaders due to lack of knowledge and skepticism of ML approaches. Comfort levels increased with exposure, but AI was often looked at as just part of the toolkit available for continuous improvement—not a game-changer.

The emergence of generative AI since late 2022 has increased excitement (and hype) but also hesitance. Generative AI is something different and powerful – at this point surely all have been exposed to consumer apps like ChatGPT, Claude and Perplexity. The potential for how impactful and transformative this technology could be was recognized early on, but proof-points were rare, and there were some very visible failures as well. In manufacturing the smart play has traditionally been to move slowly on new technologies; let others test it and mature until it is production ready.

Generative AI is Production-Ready

Over the last two years the gaps, weaknesses and concerns about generative AI—like hallucinations, security risks, intellectual property concerns and costs—have been identified and for the most part mitigated. Techniques like retrieval augmented generation using knowledge graphs, advances in capabilities like semantic understanding of diagrams/schematics, and now collaborative agents that can seek out information in other systems and synthesize it make generative AI able to get technical details correct. Cloud hyper-scalers provide secure infrastructure and managed services to build enterprise-ready AI applications without fear of their trade secrets and IP being exposed publicly. Costs for generative AI models have dropped significantly with Andreessen Horowitz noting the cost for equivalent performance is decreasing 10x every year (1000x reduction in three years). Generative AI is now enterprise- and production-ready.

AI Value Proof Points Are Here

Public proof points for how impactful AI can be are emerging and the results achieved are impressive in quality, maintenance and production. Beyond these pinpoint use-cases, evidence highlighting the broader impact of generative AI is emerging as well. Early studies showed generative AI could free up ~4 hours/week for employees (the “ugliest hours of work” according to BCG), but how this translated into business value was largely hypothetical. Now we’re seeing rigorous research into generative AI’s benefits. A recent study by P&G and Harvard compared the work of both individuals and teams with AI and without AI. They found that AI accelerated the speed of the work with higher quality outputs. Individuals with AI produced higher quality output faster than teams without AI. The emergence of the AI co-worker is here.

Rethinking Old Ways of Doing Things

Manufacturers are now seeing truly transformative applications. In maintenance, one manufacturer is using generative AI to take sensor data from the line, identify emerging issues automatically, suggest possible root-causes, and what actions to take to resolve the issue. It also identifies the parts needed for repairs, estimates the repair costs, determines if expediting needs to occur, and can automatically create both maintenance work order and purchase order in the ERP system. Future versions already in progress will recommend updates to production schedules to adapt for maintenance issues automatically (with human in the loop oversight).

The emergence of generative AI since late 2022 has increased excitement (and hype) but also hesitance.

In quality, an innovative approach using generative AI to compare just one reference image to a live image for defect detection and/or anomaly detection is showing equivalent accuracy results to traditional ML computer vision models without the heavy burden of training models (having a large number of images, labeling them, and building the models—which can be sensitive to differences in lighting, object alignment, etc.). This approach is proving to be much more flexible, less sensitive to factory floor conditions, and doesn’t require the heavy lift of traditional training workloads. It caught the attention of the CEO of an ~$80b industrial at the recent Hannover Messe, who knows the limits of traditional ML.

At the plant, generative AI is being used to gather the information for the leader’s start of shift meeting, identify the top issues and challenges automatically, and then surface them and potential fixes using the standard process plant management already uses. This shifts the burden on managers/supervisors from “administrivia” to unblocking production for the day. One head of manufacturing technology mentioned having the plant management team (production, maintenance, quality, safety, material management and HR) going from 1-2 hours each in prep time per day to less than 15 minutes in total as a group – allowing them to focus on fixing the most important issues of the day.

A Widening Gap Between AI Leaders and Laggards

The gap between leaders and laggards in adopting AI is becoming well-documented. A recent study by PWC identified AI top performers (who have AI-specific operating models, embed generative AI-powered capabilities throughout their organizations, and have strong AI governance and responsible AI practices) are more than 2x likely to realize value than laggards. Similarly, they identified industries that were best positioned to adopt AI have had revenue growth that has far exceeded others—27% vs. 8.5% in 2024. A recent EY study identified manufacturing as having the highest percentage of workers who viewed AI as a net positive, so the workers are ready.

Lead From the Front

But as Andy Jassy, CEO of Amazon says, “there is no compression algorithm for experience.” Manufacturing leaders (C-suite and VP level) have awareness but are not using AI themselves. According to a recent Manufacturing Leadership Council survey, only 4% of the C-suite, 4% of senior plant leadership, 7% of department leaders and 6% of floor supervisors use AI technology on a regular basis.

If manufacturing leaders want to realize the benefits of AI, they need experience using it. A $150b+ revenue automotive OEM recently shared that they opened access to generative AI models via a generative AI playground to all ~300,000 employees. After enablement training, they saw a 218% increase in experiments and exploration, which led to a 757% increase in production-deployed usage.

Studies found that AI accelerated the speed of work with higher quality outputs; individuals with AI produced higher quality output faster than teams without AI.

Exposure leads to results. Investing in hands-on experience from the top down is the most likely path to transformative new approaches to solving the consistent challenges of manufacturing. If leaders aren’t experiencing, innovating and learning, they will be poorly prepared for disruptive changes to traditional processes. As recently as 2023, manufacturers were expecting significant changes to the workforce from AI—retraining, upskilling, or reassigning—but 65% of manufacturers were not budgeting for AI training.

Manufacturing Stands at a Defining Moment. Act Now.

The choice lying before manufacturers is clear and needs to be made now: accelerate adoption of AI technologies and embrace the change from the top down, or risk losing business to competitors who do. Decisive action can be taken: 1) leaders, including the C-suite, need to use AI technologies every day. One technique that is highly effective is to make each leader and team in the organization answer “how can I use AI to improve”—not “if I can use AI”; 2) providing self-serve AI platforms for all employees, regardless of level, will enable learning capabilities of the technology and how it can help solve business problems. This is a powerful crowd-sourcing mechanism for innovation and problem solving; providing broad exposure, or as one manufacturer phrased it “making AI like oxygen to us”, is a strategic investment and needs to be budgeted; don’t neglect training and enablement. Providing a platform to experiment without also providing examples of the art of the possible and practical how-to guides for hands-on training will slow or limit ideation and therefore benefits; and 3/) encourage the entire organization to rethink their approaches. Work backwards from the problems they need to solve, not the current approach.

AI, and especially generative AI, is a once in a generation transformative technology wave that is just now starting to crest. This is not the time for hesitating and cautious advancement—this approach worked well in the past but will harm you now. Embrace the uplift AI can bring broadly now or risk your competitors doing so and gaining a generational advantage. M

About the author

Danny Smith is Principal Strategist, Artificial Intelligence, AWS Automotive and Manufacturing at Amazon Web Services.

AI overcomes traditional supply chain limits with real-time insights, automation, and predictive tools. 

TAKEAWAYS:
● AI provides proactive intelligence that can overcome visibility and resilience gaps in traditional supply chain data.
● AI enhances supply chain visibility by delivering real-time insight and automation, driving faster, smarter decisions across the supply chain with live data and predictive modeling.
● AI helps strengthen and optimize internal operations, leading to reduced downtime and improving overall operational resilience.  

The last decade has demonstrated why supply chain resiliency and visibility are critical. Manufacturing organizations across all sectors have experienced significant disruptions, not only from the COVID-19 pandemic, but also geopolitical instability, labor strikes and climate-related events. In today’s interconnected world, a problem in one region can impact businesses globally.

Merely reacting to issues as they emerge is no longer a winning strategy. Companies need to anticipate disruptions and pivot proactively if they want to compete in the marketplace. Predicting supply chain issues and being proactive are key to prolonged success.

Fortunately, there is a solution that can be integrated into any company’s operations that provides rapid decision-making, creative problem-solving and anticipatory insights that drive meaningful impact.

What Are Supply Chain Visibility and Resiliency?

Before understanding how AI can transform supply chain visibility and resiliency, it’s important to define these two concepts and their connection. Visibility refers to the availability of real-time data and transparency across various supply chain nodes, from current inventory stock levels to ETAs for in-progress deliveries. On the other hand, supply chain resilience is a company’s ability to respond, adapt and recover in the event of a disruption. The faster operations are restored, the more resilient its supply chain is.

These two concepts are closely connected because greater visibility allows companies to react in the event of a disruption. For example, an unexpected shortage of components in Location A can be overcome much faster if planners have access to stock levels in other locations. With greater visibility into inventory at Location B, they can reallocate faster to correct the shortage at Location A, improving the company’s overall supply chain resiliency.

Challenges with Traditional Supply Chains

While once sufficient, traditional supply chain models now lack the visibility and resiliency needed for success in today’s environment for several reasons. First, traditional models are often siloed, with each location or production stream isolated from each other. This means planners at one site may have little insight into what is happening at the others.

Another disadvantage of 20th century operations management is its reliance on reactive models in which companies wait for problems to occur before acting. This keeps them on the defensive, losing time playing catch-up. Outdated systems without real-time monitoring also leave data gaps, hindering problem-solving and supply chain efficiency. By the time an analyst has exported historic data out of their ERP or CMMS, it is often too late to avoid impacting production.

Returning to the earlier example, a sudden shortage of a critical part at Location A could halt production. Because the hypothetical company lacks full supply chain visibility, planners may not realize that Location B has unused inventory available. Without a proactive strategy, the Location A team must wait for the new shipment of the part. And without real-time monitoring, no one knows when it will arrive, leaving the production line idle and triggering a company-wide domino effect.

How AI Enhances Supply Chain Visibility

Integrating AI into supply chains can profoundly impact visibility. Some key ways in which AI technology can improve global supply chain visibility include:

• Machine learning models can be trained to provide constant demand forecasting, shipment tracking and anomaly detection. This gives planners advanced insights to anticipate and identify issues before they escalate. For example, a consistently underperforming supplier can be flagged early, allowing contingencies to be established before disruptions occur.
• Computer vision systems integrated with AI can monitor warehouse inventory and track parts or product movement, even when usage is unreported. As stock levels of critical components dwindle, automated reordering systems can help prevent unexpected shortages.
• IoT-enabled equipment within production facilities can interface with AI platforms to collect and analyze data in real time, leading to better-informed predictions and decisions.

Using AI to Build a More Resilient Supply Chain

AI also serves many important functions in helping companies build more resilient supply chain operations:

• AI-driven simulations and digital twins let organizations stress-test various supply chain scenarios and ensure they are prepared. For instance, a manufacturer can simulate a production cycle and insert a disruption into the simulation to see likely outcomes.
• Predictive risk management modeling offers insights into probable scenarios like extreme weather patterns, geopolitical risks and transportation delays. Accurately predicting a major disruption allows time to prepare alternate plans.
• AI enables automated re-routing and decision-making based on live conditions. For instance, shipments can be redirected to minimize disruption from events like hurricanes or port shutdowns.

Connecting AI-Driven Supply Chain Resiliency to Industrial Maintenance and MRO Optimization

As important as it is to watch out for supply chain disruptions that originate outside the company, manufacturers also must be prepared for internal disruptions. Equipment outages can be just as devastating for manufacturing operations as weather patterns or part-sourcing issues.

Fortunately, AI plays a critical role in preventing these internal disruptions. Today’s IoT-enabled equipment uses sensors to feed real-time data on machine health, asset performance, and parts usage. Advanced AI platforms analyze this data to predict failures, allowing maintenance teams to plan preventive maintenance and keep machinery running longer.

This creates a closed-loop feedback system encompassing asset conditions, MRO inventory and supply chain response. It enables on-demand part ordering, optimized MRO inventory and fewer emergency repairs. By forecasting spare part usage from operational data trends, AI connects asset performance and field data with supply systems.

Implementing AI in Your Supply Chain: Considerations and Tips

If you want to leverage the power of AI and machine learning to make your supply chain more resilient and visible, keep the following in mind:

• Start by assessing data quality and integration readiness. Poor data quality has historically masked problems, but large language models are becoming increasingly adept at seeing through poor data quality.
• Choose platforms that support modular AI adoption, so you can test performance in certain areas before scaling enterprise wide.
• Build internal capabilities or choose a partner that has experience with AI to speed up adoption, especially helpful for smaller manufacturers.

Why AI Is No Longer Optional

Forward-thinking manufacturers cannot afford to adopt a passive stance on AI integration. AI is now essential for enhancing supply chain visibility and resilience, empowering businesses to navigate disruptions more efficiently than ever.

About the author:

Edwin Good is the MRO & Supply Chain Director at Advanced Technology Services, Inc.

MLC’s signature event broke records again this year as manufacturers gathered to accelerate their journeys to Manufacturing 4.0. 

The Manufacturing Leadership Council hosted the 21st iteration of its signature Rethink event in June at the JW Marriott hotel in Marco Island, Florida. Nearly 500 manufacturing executives, an in-person 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 Celanese, Siemens, Procter & Gamble, The Hershey Company, Eaton, Whirlpool, Anheuser-Busch InBev, Peterbilt, Husco, and Milo’s Tea, among others.

In a special session, 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 160 companies and individuals were honored for their achievements in digital manufacturing.

Following are selected scenes from the 21st Rethink. Photos by David Bohrer, Senior Director, Photography, at the NAM. M

About the author:

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