Fundamentals of
Smart Manufacturing

A significant collaboration in the Tooling U-SME and CESMII workforce partnership is the Fundamentals of Smart Manufacturing learning package that covers essential methodologies for today’s manufacturing ecosystem. SM impacts many job roles, from leadership, engineering and the frontline workforce, to educators training the next generation of manufacturers. These new resources set a common foundation and language that enables cross-disciplinary innovation, ensuring American manufacturing competitiveness.


Tooling U-SME works with thousands of companies, including more than half of all Fortune 500® manufacturers and nearly 1,000 educational institutions across the country. Tooling U-SME focuses on the design and distribution of learning resources, and tailoring solutions to the needs of the manufacturing community. With Tooling U-SME’s extensive training experience, its reach into industry and academia, and CESMII’s subject matter expertise and expertise in smart manufacturing technology and business practices, the CESMII and Tooling U-SME collaboration will expedite adoption and drive progress through transformational workforce development.


“Our vision, as two nonprofit organizations focused on creating a more productive and competitive manufacturing environment, is completely aligned around accelerating the transformation of the U.S. manufacturing workforce.” 

Jeannine Kunz

Chief Workforce Officer, Tooling U-SME

course offerings

Fill out the form below for more information on how to start your journey into Smart Manufacturing with CESMII & Tooling U-SME at a discount.

Introduction to Smart Manufacturing

    Introduction to Smart Manufacturing (IIoT 100)
    • Smart manufacturing explained.
    • Industry 4.0 in contrast to Industry 3.0 techniques.
    • Overview of smart manufacturing techniques including data capture, interoperability, information modeling, visualization, analysis, process control and optimization.
    • The role of enterprise systems and platforms.
    • How AI is enhancing SM capabilities.
    Introduction to Smart Business Strategy (IIoT 251) - Digital Enterprise
    • The Industrial IoT and Industry 4.0. Smart manufacturing technologies.
    • Digital enterprise strategy.
    • Considerations for data storage and maintenance.
    • Digital thread and digital twin initiatives.
    • Financial benefits of implementing a digital enterprise strategy.
    • Typical changes and challenges in digital enterprise strategy.
    Smart Business Strategy: Adopting Smart (IIoT 252)
    • Benefits and strategic advantages of smart manufacturing.

    • Smart tools enable data-driven continuous optimization.

    • Smart technologies for continuous optimization and predictive analytics. Servitization capabilities enabled by SM.

    • How manufacturers can begin adopting smart manufacturing tools.

    • Organizational cultural change to make the most of SM.

    • Manufacturing systems infrastructure updates for a smart facility.


    Smart Business Strategy: Data Management (IIoT 311)
    • The importance of data to a digital enterprise.

    • Data collection and maintenance.

    • Data for predictive analytics and continuous optimization.

    • Product data and lifecycle management software and its benefits. Cybersecurity considerations.

    Capturing & Organizing Data

      IIoT Infrastructure for Smart Manufacturing (IIoT 225)
      • The Industrial Internet of Things (IIoT) and the infrastructure in Smart Manufacturing.

      • How the convergence of OT (operational technology) and IT systems has allowed for more robust use of data in process control and automation, and wireless networks have increased mobility and connectivity on the factory floor.

      • Learn how cloud and edge computing technologies provide the tools for efficient data analytics and operations.

      Organizing Big Data for Smart Manufacturing (IIoT 227)
      • IIoT and SM methods increase the availabilty of data that needs to be sourced, stored, analyzed, and used to improve production.

      • Big data techniques handle large quantities of structured and unstructured data with technologies including databases, data warehouse, data lakes, and distributed ledgers. 

      • The course also covers data contextualization, exchange, and its use for visualization and analytics. 

      Introduction to the Industrial Internet of Things (IIoT 111)
      • The Industrial Internet of Things (IIoT) and its role in manufacturing.

      • How sensors, smart devices, and the data they create transforms factory operations. 

      • Introduction to how digital thread, digital twin and AI relate to IIoT.

      • Smart technology provides detailed real-time data for instructions and feedback, enabling manufacturers to improve quality and efficiency, and to anticipate supply chain and production needs.

      • Cyber-physical systems (CPS) and human-machine interfaces (HMI) are changing the way people interact with the growing network of technology in the workplace.

      • Concerns related to cybersecurity.

      Data Collection Fundamentals (IIoT 121)
      • Provides an overview of the types, structures, and uses of data in Industry 4.0.

      • Data collection using automated and manual methods.

      • Basics of data storage and analysis.

      • Understand the value and importance of the data being collected and appreciate the safety steps needed to protect it from internal and external threats.

      Data Collection: Inventory and Maintenance (IIoT 231)
      • An overview of the processes, strategies, and software for storing data, governance, and maintaining data quality in Industry 4.0.

      • Data management practices covers maintaining data up to date to ensure that it will continue to be useful and accessible.

      • Users will understand the value and importance of data exchange software and how data is used in Lean and Six Sigma methods to reduce defects and variation among products.

      Automatic Identification Technology (IIoT 141)
      • Provides an overview of the main methods used to automatically identify material and products, collect data, and record it in computer information systems, to improve tracking, inventory, flow and ultimately make improved operational decisions. Automatic identification technology includes barcode and RFID tags.

      Connecting Data, Platforms and Systems in Smart Manufacturing

        Introduction to Integrated Systems for Smart Manufacturing (IIoT 320)
        • Integrating system architecture layers for Smart Manufacturing including connected things, systems and teams.
        • Considerations for connectivity, security and scalability.
        • Transitioning to Smart Manufacturing.
        • Interoperability with information modeling.
        • Data exchange and APIs. Interfacing Human and Machine.
        • Orchestrating with workflow and low-code platforms.
        • Shared metrics and collaboration for team performance.


        Introduction to Integrating Manufacturing Systems Operations (IIoT 322)
        • Integrating manufacturing business functions and systems across enterprise departments and lifecycles including product, production, asset and supply chain lifecycles.
        • Managing work in process, production quality, and augmenting the workforce.
        • Digital culture and workforce skills for Smart Manufacturing.
        Introduction to Digital Networks (IIoT 221)
        • Basic concepts and components of digital networking technology.

        • Learn how digital networks enable connection of automation and production equipment to provide data to production systems and artificial intelligence platforms.

        • Methods to send, receive, and analyze data to optimize automated tasks.

        • Upfront considerations of cybersecurity risks allows for digital networks to securely connect devices to multiple layers of systems in edge and cloud computing.  

        Introduction to PLCs (IIoT 201)
        • Overview of programmable logic controllers (PLCs) and how they are used in manufacturing.

        • This class introduces the components of PLCs and their functions, provides basic information on the ladder logic programming language used in PLCs, and gives an overview of common internal relay instructions used in PLC programs.

        Introduction to Digital Thread (IIoT 242)
        • Introduction to the function, software applications, and current uses of digital threads in manufacturing.

        • Learn how digital threads form a communication framework within a smart factory to exchange data throughout the product lifecycle.

        • Understand challenges in implementing digital thread.

        • The digital thread encompasses data sharing between personnel, machines, and digital storage.

        • Explain how digital thread connects the supply chain with the manufacturer.

        Introduction to Digital Twin (IIoT 241)
        • Features, benefits, and current uses of digital twins in manufacturing.

        • Understand how digital twins use virtual models and smart sensors embedded in the physical assets to provide real-time design and performance insights, helping improve operations, develop better parts and products, and test parts and machines throughout production.

        • Describe the different types of digital twins and how they work with artificial intelligence (AI) and cloud computing.

        Providing Insights for Enhanced Decision Making

          Lean Smart Manufacturing Overview (Lean 280)
          • Introduction to Lean Smart Manufacturing which blends Smart Manufacturing and Lean techniques.
          • The course covers the need for more accurate and up-to-date data for improved decisions, problem solving and continuous improvement.
          • Describes common lean key performance indicators (KPIs) and visualization tools like Andon boards.
          • Several other applications of SM in Lean techniques include production flow control, Kanban, variation reduction, error proofing, and triggering workflow for immediate action.
          SPC Overview (Inspection 211)
          • Introduction to the purpose and concepts of statistical process control (SPC).

          • Understand commonly used control charts and recognize out-of-control signs, making them better equipped to contribute to quality control efforts at their facility.

          • The course describes different types of control charts, such as X bar, R, and P charts, and how these tools are used to determine if a process is in-control or out-of-control. 

          • Understand the use of SPC techniques to eliminate special cause variation and deliver quality products consistently.

          Continuous Process Improvement: Managing Flow (Lean 124)
          • Course on Lean Manufacturing terms and concepts including 5S, visual controls, value stream mapping, takt time, flow balancing, demand pull, and single unit production. 

          Continuous Process Improvement: Identifying and Eliminating Waste (Lean 125)
          • Course on Lean Manufacturing methods for identifying and eliminating the seven wastes including standardization, Andon, jidoka, total productive maintenance, defect prevention, PDCA, Keizen, gemba, A3, and DMAIC .

          Troubleshooting (Lean 181)
          • Overview of various methods and tools used for problem-solving and troubleshooting problems.

          • Common early warning signs of trouble.

          • Gathering data for analysis.

          • Techniques for finding the root cause of a problem.

          • Prioritizing solutions.

          • Testing solutions to make sure they offer long-term results.


          Automating Flow and Control

            Introduction to Smart Process Modeling and Optimization - Part 1 (IIoT 306)
            • Introduction to decision and process modeling, and multiple types of production optimization techniques.

            • Understand the advantages of dynamic techniques that use real-time actual production data versus methods that solely rely on estimated planned time and resource usage. 

            • Topics include simulation, value stream mapping, physics-based models, and constraint handling algorithms.

            Introduction to Smart Process Modeling and Optimization - Part 2 (IIoT 307)
            • Applying AI and machine learning techniques to Smart Manufacturing use cases including energy analysis to reduce waste, bottleneck analysis to improve flow, predictive analysis to optimize process quality and reduce machine downtime. 

            Augmented Worker (IIoT 150)
            • Understand the application and benefits of using technology to augment the capabilities of the manufacturing worker and overcome some challenges with manual tasks.

            • Technologies used to assist the worker include wearable sensors, augmented reality, and AI to improve consistency, safety and quality in production operations.  

            Applications for Robots (Robots 130)
            • Understand why we use industrial robots and their limitations.

            • Common applications of industrial robots including welding, material handling, spraying, fabrication, assembly, and inspection.

            Automated Systems and Control (Robotics 136)
            • This course identifies common methods of industrial automation.

            • It describes the available technologies and how they are applied in manufacturing including PLCs, CNC, CAD, CAM, SCADA, robots and information systems like MES and ERP. 


            Introduction to Machine Learning and Artificial Intelligence (IIoT 301)
            • Covers advances in the field of artificial intelligence (AI) and machine learning and its application in manufacturing.

            • Basic types of machine learning explained include supervised, unsupervised, and reinforcement machine learning.

            • Basic regression, classification, clustering and nerual network algorithms.


            Machine Learning and Artificial Intelligence Applications (IIoT 302)
            • Discusses strategies for applying machine learning (ML) and artificial intelligence (AI) capabilities to various manufacturing processes.
            • Understand how ML algorithms can help improve manufacturing processes throughout a product’s lifecycle including monitoring raw material use, reducing machine downtime, optimizing production processes and supply chain logistics, and improving the product design, quality, delivery and service for end customers.
            Vision Systems (Robotics 320)
            • Overview of industrial vision systems and how they are used in factory automation.
            • Common vision system characteristics, components, types, and applications, as well as their general operating process.
            • Describe scenarios where visions systems are used to monitor a production process and how the data is used for inspection, identification, material handling, and fabrication scenarios.
            Introduction to Collaborative Robots (Robots 275)
            • Course covers the four types of collaborative robots (cobots) as well as basic applications and safety considerations for cobots.

            • Types of cobots include safety-rated monitored stop, speed and separation, power and force limiting, and hand guiding cobots.

            • Users will also understand the benefits of automating tasks with cobots compared to traditional robot automation.

            • Understand which types of cobots are appropriate for specific applications including material handling, machine tending, processing, finishing and quality inspection.


            Introduction to the Smart Supply Chain (Supply Chain 120)
            • Introduction to how Smart Manufacturing and IIoT improves supply chain management.
            • Data and data devices in the supply chain. Understand how Smart Manufacturing techniques work in shipping hubs, warehouses, fleet management, material handling, and improves value and interactions for customers.


              Cybersecurity Tools and Methods (IIoT 205)
              • Course covers the various solutions and layers of cybersecurity available for Smart Manufacturing.
              • Covers the cybersecurity standards used to address multiple layers of risk.
              • Cybersecurity methods include physical, technical, and administrative tools.
              • Course covers considerations for the establishment of a resilient cybersecurity program.
              Cybersecurity for Manufacturing Basics (IIoT 101)
              • Covers the foundational concepts of cybersecurity as it relates to the manufacturing sector including typical risks of malware and hacking and the new risks introduced by Industrial IoT and Industry 4.0 technologies.

              Cybersecurity for Manufacturing: Malware Overview (IIoT 102)
              • Manufacturers should be aware of vulnerabilities and able to recognize malware threats.

              • Covers different types of malware including computer worms, viruses, and how criminal hackers use spyware, Trojans, and ransomware to attack digital networks.

              • Also covers ways to manipulate users into performing actions that plant malware using phishing and social engineering tactics.

              • Users will understand the basic strategies of criminal hackers and ways to defend against them.

              Cybersecurity for Manufacturing: Hacking Overview (IIoT 201)
              • Understand the cyber threats posed by hackers as well as the tools and strategies to defend against these threats.

              • Explores the various types of hackers, some common hacking passive and active methods, and strategies for defending against hacking.

              • Hackers are generally classified based on their level of skill and their motivations for hacking.

              Cybersecurity for Manufacturing: Wireless Networks (IIoT 202)
              • Introduces common risks associated with using wireless networks in manufacturing including wireless local area networks (WLANs) and wireless personal area networks (WPANs).

              • Security risks include wardriving, piggybacking, and evil twin attacks.

              • Outdated security protocols can allow criminal hackers to easily access digital information through wireless devices.

              • Users will understand these risks and effective ways to make these networks more secure.

              * Classes with an asterisk are under development and coming soon.

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