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.

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.

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.

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. 

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.

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.

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.

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.

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.  

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 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.

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.

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.

Course on Lean Manufacturing terms and concepts including 5S, visual controls, value stream mapping, takt time, flow balancing, demand pull, and single unit production. 

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 .

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.

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.

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. 

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.  

Understand why we use industrial robots and their limitations.

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

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. 

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.

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.

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.

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.

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.

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.