Rockwell Automation – 90% of manufacturers say digital transformation is now essential, according to new global study

EMR Analysis

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More information on the 11th annual “State of Smart Manufacturing Report” by Rockwell Automation: https://www.rockwellautomation.com/en-us/capabilities/digital-transformation/state-of-smart-manufacturing.html + 

• Digital transformation is non-negotiable
  • Digital transformation is no longer a strategic priority; it’s a strategic necessity. Leaders aren’t piloting anymore. They’re scaling and executing.
• AI is the top driver of business outcomes
  • AI has moved beyond hype and now delivers on outcomes, especially when scaled responsibly and embedded into production functions.
• Resilience and security are now requirements
  • Manufacturers are more digitally exposed than ever before, making resilience and security essential to a production environment, not just IT add-ons.

 

 

 

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EMR Additional Notes:

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.

 

 

• Industrial Automation:
  • Industrial Automation (umbrella term) is the use of technologies such as computer software and robotics to control machinery and processes which replace human beings in performing specific functions. The functions are primarily centered on manufacturing, quality control and material handling processes.
    • Process Automation / Manufacturing:
      • Process automation (based on the nature of the raw materials and final product) is defined as the use of software and technologies to automate business processes and functions in order to accomplish defined organizational goals, such as producing a product, hiring and onboarding an employee, or providing customer service.
      • Process manufacturing utilizes chemical, physical and compositional changes to convert raw material or feedstock into a product. Process manufacturing includes industries such as cement and glass, chemicals, electric power generation, food and beverage, life sciences, metals and mining, oil and gas, pulp and paper, refining, and water and wastewater. Process manufacturing includes both continuous and batch processes.
    • Discrete Automation / Manufacturing:
      • Discrete automation (focusing on individual, quantifiable parts and products) is the production of parts that are of a quantifiable nature. That may include cell phones, soda bottles, automobiles, airplanes, toys, etc. As you know, an automobile contains many, many parts. The parts required for an automobile are also quantifiable in nature.
      • Discrete manufacturing processes include the production of individual parts as well as their assembly into a final product. Discrete manufacturing examples include automobiles, appliances, and consumer electronics.
  • Types of Automation Systems (by flexibility):
    • Fixed Automation:
      • Most basic, least flexible type of automation, ideal for high-volume, unchanging production.
      • Fixed automation systems are utilized in high volume production settings that have dedicated equipment. The equipment has fixed operation sets and is designed to perform efficiently with the operation sets. This type of automation is mainly used in discrete mass production and continuous flow systems like paint shops, distillation processes, transfer lines and conveyors. All these processes rely on mechanized machinery to perform their fixed and repetitive operations to achieve high production volumes.
    • Programmable Automation:
      • Next level of flexibility, where the system can be reprogrammed, but with a significant effort.
      • Programmable automation systems facilitate changeable operation sequences and machine configuration using electronic controls. With programmable automation, non-trivial programming efforts are required to reprogram sequence and machine operations. Since production processes are not changed often, programmable automation systems tend to be less expensive in the long run. This type of system is mainly used in low job variety and medium-to-high product volume settings. It may also be used in mass production settings like paper mills and steel rolling mills.
    • Flexible Automation:
      • Most advanced type of automation based on flexibility, allowing for easy, high-level changes without major reprogramming.
      • Flexible automation systems are utilized in computer-controlled flexible manufacturing systems. Human operators enter high-level commands in the form of computer codes that identify products and their location in the system’s sequence to trigger automatic lower-level changes. Every production machine receives instructions from a human-operated computer. The instructions trigger the loading and unloading of necessary tools before carrying out their computer-instructed processes. Once processing is completed, the end products are transferred to the next machine automatically. Flexible industrial automation is used in batch processes and job shops with high product varieties and low-to-medium job volumes.
  • Advanced and Integrated Concepts (most complex):
    • Integrated Automation:
      • Takes flexible automation to the next level by explaining how an entire plant’s processes, from manufacturing to business operations, are linked under a single computer-controlled system.
      • Integrated industrial automation involves the total automation of manufacturing plants where all processes function under digital information processing coordination and computer control. It comprises technologies like:
        • Computer-aided process planning
        • Computer-supported design and manufacturing
        • Flexible machine systems
        • Computer numerical control machine tools
        • Automated material handling systems, like robots
        • Automatic storage and retrieval systems
        • Computerized production and scheduling control
        • Automated conveyors and cranes
      • Additionally, an integrated automation system can integrate a business system via a common database. That is, it supports the full integration of management operations and processes using communication and information technologies. Such technologies are utilized in computer integrated manufacturing and advanced process automation systems.
    • Smart Manufacturing (SM):
      • Modern evolution of automation, driven by data and connectivity.
      • Technology-driven approach that utilizes Internet-connected machinery to monitor the production process. The goal of SM is to identify opportunities for automating operations and use data analytics to improve manufacturing performance.
      • An example of what the cloud can do for smart manufacturing is the Volkswagen Industrial Cloud, which combines all data from 122 Volkswagen Group facilities and processes it in real time to make improvements.
    • Hybrid Automation / Manufacturing:
      • Combines different approaches, showing how both additive and subtractive manufacturing can be integrated into one process. It also introduces the “hybrid” method for implementing automation projects.
      • The Hybrid Automation Method follows two guiding principles: Implementing robust automation solutions that are easy and affordable for organisations to maintain. Realising process efficiency rapidly by reducing project overheads and time-to-value.
      • Hybrid manufacturing is a combination of additive manufacturing (AM) and subtractive manufacturing within the same machine.
    • Additive Manufacturing (AM):
      • Key technology of one of the core components of the “hybrid” approach.
      • Additive manufacturing is the process of creating an object by building it one layer at a time. It is the opposite of subtractive manufacturing, in which an object is created by cutting away at a solid block of material until the final product is complete.
      • Operators across a variety of different manufacturing industries utilize additive manufacturing in various ways. For instance: Medical device manufacturers use 3D printing to develop high variance products such as dental implants.
      • The term “additive manufacturing” refers to the creation of objects by “adding” material. Therefore, 3D printing is a form of additive manufacturing. When an object is created by adding material — as opposed to removing material — it’s considered additive manufacturing.

 

 

• Cybersecurity: 
  • Computer security, cybersecurity, or information technology security is the protection of computer systems and networks from information disclosure, theft of or damage to their hardware, software, or electronic data, as well as from the disruption or misdirection of the services they provide.

 

 

• Information Technology (IT) & Operational Technology (OT):
  • Information Technology (IT): 
    • Refers to anything related to computer technology, including hardware and software. Your email, for example, falls under the IT umbrella. IT forms the technological backbone of most organizations and companies by managing data, communications, and business processes. These devices and programs have little autonomy and are updated frequently.
  • Operational Technology (OT): 
    • Refers to the hardware and software used to change, monitor, or control physical devices, processes, and events within a company or organization. This form of technology is most commonly used in industrial settings, where these systems are engineered for safety, reliability, and precision control. An example of OT includes SCADA (Supervisory Control and Data Acquisition).
  • => The main difference between OT and IT devices:  OT devices control the physical world, while IT systems manage data.