Summary
In its “Smart Manufacturing Dictionary,” MESA International defines smart manufacturing as “…the endeavor to design, deploy and manage enterprise manufacturing processes, operations and systems that enable proactive management of the manufacturing enterprise through informed, timely (as close to real time as possible), in-depth decision execution. Systems with smart manufacturing capabilities are realized through the application of advanced information, communication and manufacturing process technologies to create new and/or extend existing manufacturing system components. These components integrate synergistically to create new or extend existing manufacturing systems that possess the desired advanced automation, analysis and integration capabilities.”
Smart manufacturing is another name for Industry 4.0, Industry 5.0 or digital transformation. It is intended to help meet business challenges and changes using digital means. It is typically a journey that includes many projects. Smart manufacturing is generally intended to improve these issues:
Efficiencies: yield, uptime, capacity utilization and quality
Agility
Decision-making data access
Knowledge retention
Supporting less-skilled workers
Enabling new business models.
Technologies enable and support new processes and the people in the company. MESA’s model (Figure 1) includes lifecycles, cross-lifecycle threads and enabling technologies. Every discipline in the company and every process may be affected. It is a journey for the entire manufacturing enterprise.
The current state of smart manufacturing
Rockwell Automation’s 10th annual “State of Smart Manufacturing Report” highlights how companies are turning to smart manufacturing technologies to manage risks, improve performance and support their workforce. The report also examines the adoption of emerging technologies including artificial intelligence (AI), machine learning (ML) and cloud based systems.
According to the report, which is based on feedback from 1,560 respondents, 81% of manufacturers say external and internal pressures are accelerating digital transformation, with cloud/SaaS [software as a service]; AI; cybersecurity; and quality management ranking as the top areas of smart manufacturing technology investments. The report said 95% of responding manufacturers have invested in—or plan to invest in—AI/ML over the next five years.
Organizations investing in generative and causal AI increased 12% year-over-year, which signals a maturing approach to advanced technologies beyond experimentation. Cybersecurity ranks as the second biggest external risk, with 49% of manufacturers planning to use AI for cybersecurity in 2025—up from 40% in 2024.
In addition, 48% of manufacturers plan to repurpose or hire additional workers due to smart manufacturing investments. Also, 41% are using AI and automation to help close the skills gap and address labor shortages.
Quality control remains the top AI use case for the second year in a row, with 50% planning to apply AI/ML to support product quality in 2025.
The report reflects a broader movement toward more efficient and adaptive operations. Manufacturers are using smart technologies to strengthen supply chains, accelerate sustainability initiatives and make faster, more informed decisions. There has also been a 5% rise in the importance of analytical and AI skills for leaders, which shows that talent development and technical innovation must go hand in hand.
However, many manufacturers face challenges when implementing AI. Nearly half of respondents say the ability to apply AI is now an extremely important skill, up from just 10% last year, according to the Rockwell report.
Both the Rockwell Automation report and research from MESA International illustrate AI’s evolving role in smart manufacturing. Compared to previous survey results, more organizations plan to use AI/ML for cybersecurity in the next 12 months, highlighting the evolving role of advanced technologies in enhancing cybersecurity measures. AI/ML are also poised to transform supply chain management, with a third of respondents planning to use them for managing their supply chain (Figure 2).
Smart manufacturing characteristics
Regardless of the terminology—Industry 4.0, Industry 5.0, digital transformation or smart manufacturing—the characteristics are identified in ANSI/ISA-95.00.01-2000, Enterprise-Control System Integration. ISA-95, also known as ANSI/ISA-95 or IEC 62264, is an international set of standards aimed at integrating logistics systems with manufacturing control systems. It organizes technology and business processes into layers defined by activities taking place, and it outlines how an enterprise can set up an interface to communicate among these layers, according to the International Society of Automation (ISA).
ISA-95 is the most comprehensive definition of modern manufacturing information exchange in the world. Manufacturers rely on these standards to define, develop and integrate many complex systems and processes in Industry 4.0 and beyond.
The ISA-95 standards framework is widely accepted as essential to modern manufacturing. It relies on the Purdue Reference Model for computer-integrated manufacturing to describe network segmentation in industrial control systems. ISA-95 establishes an architecture based on this model that enterprises can apply regardless of the technology used. This equipment hierarchy model can also be applied across discrete, continuous and logistics industries, according to ISA.
ISA developed ISA-95 to create an abstract model for information exchange among manufacturing control functions and business functions in an enterprise. The ISA-95 standards framework defines the interface between these functions to build an exchange that is robust, safe and cost-effective. It also helps manufacturing personnel and information technology (IT) personnel collaborate by determining key terms for integration projects, reducing the risk, cost and errors associated with their implementation.
Technology has evolved since ISA-95 was established in the mid-1990s, but the standards framework presents an abstract model that accommodates a wide range of technologies and systems. Its scope prioritizes activities—not technologies—and its intended purpose as a tech-agnostic communication model remains relevant.
In the current state of Industry 4.0, the Internet of Things (IoT) and smart manufacturing, data flows are more distributed and the ISA-95 model originated as a hierarchy. Still, the levels allow practitioners to describe boundaries between systems, which is an essential step in integration projects. ISA-95 remains in wide use today among manufacturing enterprises as a reference architecture and as an effective way to drive interoperability.
Roadblocks to smart manufacturing
“Making Manufacturing Analytics and AI Matter” is the title of the 2025 edition of MESA International’s Analytics and Metrics that Matter research. Based on feedback from more than 420 manufacturing professionals, this year’s study reveals that AI is no longer just a buzzword—it’s a game-changer, which indicates that AI is necessarily a significant element of smart manufacturing. Despite challenges, manufacturers are seeing real, measurable value from AI and analytics initiatives.
MESA’s research explored what companies are doing, why and how. Responses are from global manufacturing companies. The companies investing in analytics and AI are gaining substantial benefits that matter because they are in areas that match the most common objectives of cost, efficiency/productivity, quality and error-proofing.
All of these respondents face significant challenges; 99% of them are investing in manufacturing operations, analytics and AI to address these challenges. “Those using analytics and AI longer tend to see benefits in more areas. Top performers doing better on operations metrics are also outperforming others on business metrics. What are they doing differently? More of them are using dashboards, analytics and AI. They also prioritize use cases based on business value,” according to the MESA research.
A characteristic identified by the MESA survey as significant for smart manufacturing performers is having a comprehensive digital view of the plant floor, which requires the integration of operational technology (OT) with real-time IT. Barriers to obtaining that view, according to MESA, include:
Poor data quality (60% of companies report this issue).
Inconsistent data and lack of governance (41%).
Difficulty accessing data quickly enough for real-time decisions (37%).
Poor data visualization tools.
Top performers are addressing these challenges by investing in dashboards, AI-driven analytics and digital twins to create more accurate and actionable insights. Top performers are defined by their ability to outperform their peers on overall equipment effectiveness (OEE), first-pass yield, throughput and capacity utilization. They also perform better on business metrics.
Pathway to smart manufacturing best practices
In its report, “Making Manufacturing Analytics and AI Matter,” MESA offers several recommendations: Smart manufacturing and analytics investments, a strong manufacturing data management foundation, frontline workforce empowerment, preparing for AI and following top performers’ examples.
Invest in smart manufacturing and analytics. Now is the time to invest in smart manufacturing and analytics solutions. Whether the need is for descriptive, predictive or supportive AI, they all are effective. Users who adopt these solutions gain even more benefits over time. Investments should be made in open, modern and analytics-ready or AI-infused software.
Build a robust manufacturing data management foundation. Users should drive toward data management. Start on focused AI and analytics projects with only the needed data. For most companies, this is a multi-faceted investment that includes people, processes and technology. Ensure that organizations such as IT and OT are ready to work closely together. In addition, create processes to improve data handling and governance. Use common data models and integration frameworks to ensure data is reliable, available and in context.
Give frontline workers all they need. To support frontline workers, provide access to job-relevant key performance indicators (KPIs). Deliver timely views into all the data the personnel need for tasks, or further, have analytics and AI deliver actionable insights based on data to them. Capture knowledge before the best employees retire. Use this valuable knowledge to guide and boost current and future workers’ performance and further improve KPIs. Educate personnel to counteract concerns and enable them to use analytics to their maximum extent and benefit.
Prepare for AI. Barriers for both predictive analytics or AI and supportive or GenAI tend to emerge during the implementation journey. Now is the time to start evaluating data for completeness, quality and context to identify what must be done to succeed with analytics models and algorithms. Educate employees and executives to minimize the risk of cultural resistance or lack of trust. Recognize that choosing a pilot use case may require considering the future rollout and prioritizing the most prevalent issues.
Follow top performers’ paths; they show the way on this journey. Invest in smart manufacturing, analytics and AI. Focus on getting data to operations personnel for decisions. Ensure use cases are based on business value. Seek vendor-delivered analytics that are specific (and relevant) to the industry or need. Be willing to experiment with analytics and AI.
A step toward smarter manufacturing
Users who seek to lower the total cost of ownership (TCO) for manufacturing IT architectures and manufacturing, as well as reduce supply chain operational costs, would do well to check into the methodologies and technical applications presented in the first annual ISA-95/MESA Best Practices book to get started on the right track. “The Hitchhiker’s Guide to Manufacturing Operations Management” for $24.99 is available from MESA International.
The book provides in-depth coverage on how users can apply ISA-95: Enterprise-Control Integration Standard to help lower TCO of MOM systems and their enterprise and plant interfaces. It consists of a series of related how-to white papers described in the context of ISA-95 models, definitions and data exchanges.
To be competitive, manufacturing operations activities must be highly interactive in the supply chain and enterprise processes for effective collaboration and competition. This is the domain of collaborative and flexible MOM system architectures. The book explains the business cases for using evolving ISA-95 methods to effectively design, implement, change and optimize the MOM business processes and supporting MOM system architectures within the distributed pull supply chains.
This feature originally appeared in the June/July issue of Automation.com Monthly.
About The Author
Jack Smith is senior contributing editor for Automation.com and Automation.com Monthly digital magazine, publications of ISA, the International Society of Automation. Jack is a senior member of ISA, as well as a member of IEEE. He has an AAS in Electrical/Electronic Engineering and experience in instrumentation, closed loop control, PLCs, complex automated test systems and test system design. Jack also has more than 20 years of experience as a journalist covering process, discrete and hybrid technologies.
Download the June/July issue of Automation.com Monthly
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