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Artificial Intelligence in Manufacturing

by Manufacture Nevada

As artificial intelligence (AI) and machine learning (ML) become more accessible, manufacturers are increasingly exploring these technologies to enhance productivity, improve precision, and optimize performance. By using fewer resources, reducing waste, and enabling better use of equipment and personnel, AI and ML are transforming the manufacturing landscape.

Smart Manufacturing (SM) provides the framework for applying these technologies in meaningful ways. AI systems are capable of recognizing, explaining, and predicting conditions in real time to support human and machine-based decisions. ML, a branch of AI, uses historical data to detect patterns, assess current conditions, and forecast future outcomes. Tools like feature recognition (for visual inspection), natural language processing (for operator-machine communication), and digital twins (virtual models of equipment or processes) are already being deployed in modern manufacturing environments. Even advanced language-based AI, such as ChatGPT, is being integrated to analyze data and automate communication workflows. These tools are most effective when manufacturers have access to quality data, a deep understanding of their operations, and the expertise to align AI solutions with real-world production challenges.

For manufacturers of all sizes, including small and medium-sized enterprises, adopting Smart Manufacturing and AI is becoming essential. These technologies improve factory efficiency, support supply chain resilience, and contribute to sustainability goals by making operations more adaptive and data-driven. As manufacturers embrace these tools, they unlock new opportunities for growth, competitiveness, and long-term success.

Smart Manufacturing (SM) and AI

Smart Manufacturing (SM) centers around ensuring the right data is available at the right time and place,empowering both people and machines to effectively control, manage, and optimize operations across the manufacturing landscape. True SM operates at scale, connecting equipment, facilities, supply chains, and entire industry ecosystems through interoperability. At the heart of this system is a factory's ability to consistently organize and manage its operational data. This data foundation is essential for the broader application of AI and machine learning (AI/ML).

When manufacturers commit to structured data practices, AI/ML solutions can be deployed to enhance operational visibility, improve forecasting, enable quicker and more proactive responses to issues, and drive performance on key metrics,often at a lower cost. Automation plays a critical role in this environment, leveraging data and predictive models in well-understood scenarios where confidence in the system allows human workers to shift from manual operation to oversight and strategic management.

What to Know Before Implementing AI

Smart Manufacturing (SM) is about seamlessly integrating and coordinating business, physical, and digital processes in real time. Artificial Intelligence (AI) enhances this by enabling manufacturers to learn from and apply data in increasingly impactful ways. For SM and AI initiatives to succeed, companies must adopt new business and operational mindsets,treating data collection, accessibility, and utilization as strategic assets.

Getting started with AI doesn’t require large-scale investments. In fact, beginning with simple, low-cost objectives, such as installing a few sensors to monitor existing or even legacy equipment, can deliver quick wins. These early applications not only provide measurable improvements but also lay the groundwork for broader AI adoption by helping manufacturers establish consistent naming conventions, data types, and practices for data reusability and scalability. A major hurdle remains in gathering enough of the right kind of data, there’s a big difference between recognizing a process issue and diagnosing or predicting it.

While the number of AI tools and algorithms can seem overwhelming, the best approach starts with clearly defining the problem and assessing the available data. The higher the quality and volume of data, the more reliable the outcome. Sharing data, though not yet common, can further strengthen solutions and reduce duplication of effort.

Applications of AI

Asset management and product quality are ideal entry points for implementing AI and Smart Manufacturing technologies.

For instance:

  • Condition monitoring can be achieved by capturing sets of measurements as snapshots in time to identify patterns, anomalies, or trends. These patterns can help determine when equipment maintenance is needed, signal deviations from normal operations, or predict whether a product will meet quality standards.
  • Predictive modeling helps establish relationships between input parameters and output results, allowing manufacturers to forecast product performance or operational metrics. A powerful tool in this category is the soft sensor, which transforms an offline measurement into a real-time, online one for quicker decision-making.
  • Operational mapping links inputs and outputs to streamline control and resource management. For example, aligning product requirements with raw material availability can help reduce delays caused by unexpected resupply needs.
  • Image-based feature recognition uses visual data to detect product defects, monitor material conditions, and assess equipment health. Similar to facial recognition technology, this approach identifies flaws or changes in operational conditions that may otherwise go unnoticed.

Creating AI Solutions

Choosing the right AI application can be overwhelming—there are countless possibilities, but no one-size-fits-all solution. Success doesn’t come from simply plugging in an algorithm and expecting it to work. Instead, manufacturers need a structured, iterative approach to avoid common pitfalls, such as building models that appear accurate but fail under real-world conditions.

The key is to start small and scale gradually. Early wins provide not only proof of concept but also valuable experience with the nuances of data management—understanding the condition, context, and structure of the data being used. Every AI project should follow a clear lifecycle, which includes:

  • Clearly defining a problem that can be solved with the available data
  • Gathering, contextualizing, and selecting relevant data and features
  • Preprocessing and cleaning data for consistency and reliability
  • Choosing an appropriate algorithm, splitting data into training, evaluation, and test sets, and tuning parameters through iterative training
  • Evaluating model performance using metrics like accuracy, precision, and recall
  • Deploying the model in phases to gain confidence and allow for real-world learning
  • Continuously refining the model as new data or operational changes arise

While many ready-to-use AI tools and cloud-based platforms exist, manufacturer involvement is essential—especially during the data preparation and model training phases. Outside providers, platforms, and consultants can be valuable, but their efforts must be grounded in the operational realities known by in-house experts. Beware of any AI solution that operates in a vacuum without your team’s input.

Infrastructure is another critical consideration. AI success requires systems for managing data across the full lifecycle. Cloud-based solutions can minimize upfront investment, but like algorithm selection, infrastructure decisions should be driven by the specific problem, data type, and internal capabilities. Finally, it’s important to recognize that individual machines or operations may not generate enough meaningful data on their own—data aggregation across systems or partners will often be necessary for training robust AI models.

How Manufacture Nevada Can Help

Manufacture Nevada supports manufacturers in adopting and scaling Smart Manufacturing (SM) and artificial intelligence (AI) strategies by focusing on practical, low-cost solutions that deliver early results and build internal capabilities. The organization guides businesses through the entire AI implementation process—from problem identification and quality data collection to algorithm selection and performance evaluation. Whether the goal is predictive maintenance, quality control, or operational optimization, Manufacture Nevada ensures that each solution is tailored to the manufacturer’s specific needs. Backed by a robust network of experts and resources, Manufacture Nevada helps turn data into actionable insights that drive efficiency, flexibility, and long-term competitiveness. Connect with our Business Advisors today to learn more.

Content from this blog was sourced from CMTC.

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