Brian McMorris

Brian McMorris

President at Futura Automation, LLC
Since the beginning of mankind, somewhere in central Africa about 2 million years ago, there has been a steady, if not logarithmic, progression towards reducing the amount of human labor required for output gained, thereby improving the quality of human life (assuming less work equals more quality). This week to prove the point, I have asked “ChatGPT” for some assistance researching this article. And why not use AI to assist tedious research (blending in my own thoughts and experiences)?

The journey towards fully automated manufacturing of goods, eventually eliminates the requirement for human work, something I like to call “The Workless Week”.  Progress towards this “goal” is intricately linked with the evolution of artificial intelligence (AI) and robotics. This narrative will explore the key milestones and the role of AI in achieving this ambitious vision. I have for over a decade maintained a historical timeline of robotics and automation on our company website. It is updated annually:

1. Pre-Industrial Revolution (Before 18th Century)

Before the Industrial Revolution, manufacturing processes were largely manual and labor-intensive. From before the time “homo erectus” first appearance in prehistoric central Africa about 2 million years ago, ancient craftsmen and artisans relied on hand tools and laborious manual technique to produce goods (even cave drawings count). However, even in these early stages, rudimentary forms of automation began to emerge. For instance, levers, wheels, waterwheels, gears and pulleys were used to power simple machinery, automating certain aspects of textile production and grain milling.

Automatons, essentially mechanical coiled spring-, water- or wind-driven complex machines that re-enacted some aspect of life, came into existence several thousand years ago in the Egyptian empire. A more recent example is the 14th century astronomical clock in the Strasbourg (France) Cathedral (which I have personally visited and been amazed!). Because of the complexity and limited tooling and materials prior to the 1800s, it was only kings, emperors and popes who could afford to commission complex automatons. Here, in the following video, are several examples of early automatons.

2. Early Industrial Revolution (Late 18th to Early 19th Century)

The Industrial Revolution marked a significant turning point in manufacturing. Innovations such as the spinning jenny, water frame, and steam engine revolutionized textile manufacturing, increasing productivity and reducing the reliance on tedious manual labor. In 1799, Eli Whitney’s invention of the cotton gin automated the separation of cotton fibers from seeds, streamlining the production process.

Eli Whitney’s Cotton Gin circa 1790

3. Mass Production (Late 19th to Early 20th Century)

The late 19th and early 20th centuries saw the rise of mass production techniques pioneered by visionaries like Henry Ford. Ford’s implementation of the assembly line in automotive manufacturing drastically reduced production times and costs, setting the stage for further automation. By breaking down complex tasks into simpler, repetitive motions, the assembly line made it possible to employ semi-skilled workers to perform specialized tasks.

Henry Ford early auto production line

4. Automation and Robotics (Mid to Late 20th Century)

The mid-20th century witnessed significant advancements in automation and robotics. The introduction of computer numerical control (CNC) machines revolutionized machining processes, allowing for precise and automated control of machine tools. The CNC machine laid the foundation for Industrial Robots in terms of the required precision components (closed loop servo motors and position feedback encoders) and the control software (G and M codes). Robots began to enter factories, performing tasks such as welding, painting, and assembly with speed and precision. General Motors’ installation of the first industrial robot, the hydraulically operated Unimate, in 1961 marked a pivotal moment in the history of automation.

An early hydraulic Unimate robot

5. Computerization and CAD/CAM (1970s to 1980s)

In 1971 the first microprocessor, the 4004, was introduced by Intel. The 1970s and 1980s saw the widespread adoption of computerization in manufacturing. The development of computer-aided design (CAD) and computer-aided manufacturing (CAM) systems revolutionized product design and production planning. CAD/CAM systems enabled designers to create digital models of products, which could be directly translated into instructions for automated manufacturing processes. This integration of computer technology further streamlined production and increased efficiency.

The first microprocessor, the Intel 4004

6. Rise of Programmable Logic Controllers (PLCs) (1980s)

With development of the microprocessor and inexpensive / ubiquitous computing and control languages like Fortran and “C” an industrialized and specialized automation computer came into existence. The 1980s witnessed the rise of programmable logic controllers (PLCs), which revolutionized industrial automation. PLCs replaced traditional electromechanical relay systems with digital control, allowing for more flexible and reliable automation of manufacturing processes. Software was developed that emulated relay logic known as “ladder diagrams”. PLCs could be programmed to control machinery and equipment, monitor inputs from sensors, and execute logic-based tasks, reducing the need for manual intervention in factory operations.

7. Internet of Things (IoT) and Smart Manufacturing (2000s)

The 21st century brought about the convergence of physical and digital technologies in manufacturing. The Internet of Things (IoT) facilitated the connectivity of devices and equipment on the factory floor, enabling real-time monitoring and control of production processes. Smart sensors embedded in machinery and products provided valuable data insights, allowing for predictive maintenance, quality control, and optimization of production workflows. This era of smart manufacturing laid the foundation for more intelligent and autonomous production systems.

8. Advancements in Artificial Intelligence (AI) and Machine Learning (2010s)

The 2010s and 20s have witnessed significant advancements in artificial intelligence (AI) and “Machine Learning”, further enhancing the capabilities of automated manufacturing systems. AI algorithms enabled machines to learn from data, adapt to changing conditions, and make decisions autonomously. Machine learning algorithms optimized production schedules, predictive maintenance, and quality control processes, improving efficiency and reducing downtime. Collaborative robots, or cobots, emerged as a new generation of robots designed to work alongside humans, enhancing productivity and flexibility on the factory floor.

AI algorithms are now in many cases taught by computer “kinematically accurate” simulations of how real world variables might interact (lighting (for vision), color, weight, mechanical motion, gripping force/pressure, tactile feedback, etc). The ability to teach a robot from a simulation speeds the training effort for new tasks, which is critical in manufacturing to achieve higher degrees of customization at less unit cost. Following is a video Oxford Institute of Computer Science overviewing the state of robotics / AI interaction and the reason why AI is so challenging to apply to multi-axial robots in a variety of unknown environments.

Role of Artificial Intelligence in Achieving Fully Automated Manufacturing

Artificial intelligence (AI) plays a pivotal role in driving the transition towards fully automated manufacturing. Its integration into manufacturing processes enables machines to perform complex tasks with greater efficiency, accuracy, and autonomy. Here’s how AI contributes to the realization of this vision:

1. Predictive Maintenance: AI-powered predictive maintenance systems analyze equipment sensor data to detect potential faults and anomalies before they lead to breakdowns. By predicting when machinery is likely to fail, manufacturers can schedule maintenance proactively, minimizing downtime and maximizing productivity.

2. Quality Control: AI algorithms analyze real-time data from production processes to identify defects and deviations from quality standards. Machine vision systems equipped with AI can inspect products with precision and speed, ensuring that only high-quality items reach the market.

3. Production Optimization: AI optimizes production schedules and resource allocation based on factors such as demand forecasts, machine availability, and raw material availability. Machine learning algorithms continuously learn from production data to identify bottlenecks, inefficiencies, and opportunities for improvement, enabling manufacturers to optimize their operations for maximum efficiency and cost-effectiveness.

4. Autonomous Robots: AI-powered robots, or autonomous robots, are capable of performing tasks traditionally done by humans with minimal supervision. These robots can navigate complex environments, manipulate objects, and adapt to changing conditions autonomously, making them ideal for tasks such as assembly, picking, packing, and material handling.

5. Adaptive Manufacturing: AI enables adaptive manufacturing systems that can respond dynamically to changes in demand, supply chain disruptions, and market conditions. These systems can reconfigure production processes, adjust product designs, and optimize workflows in real-time to meet changing requirements, ensuring agility and resilience in the face of uncertainty.

6. Human-Machine Collaboration: AI facilitates human-machine collaboration, where humans and machines work together synergistically to achieve common goals. Collaborative robots, or cobots, are designed to work alongside humans in shared workspaces, enhancing productivity, safety, and flexibility on the factory floor. AI algorithms enable seamless interaction and coordination between humans and cobots, enabling them to collaborate effectively on tasks requiring both cognitive and physical capabilities.

7. Decision Support Systems: AI-powered decision support systems provide manufacturers with actionable insights and recommendations based on data analytics and predictive modeling. These systems help managers make informed decisions about production planning, resource allocation, inventory management, and supply chain optimization, enabling them to optimize performance and drive business success.

8. Continuous Improvement: AI facilitates continuous improvement initiatives by analyzing production data to identify trends, patterns, and opportunities for optimization. Machine learning algorithms learn from historical data to develop predictive models and prescriptive recommendations for process improvement, enabling manufacturers to drive innovation and stay ahead of the competition.

Challenges and Considerations

While the potential benefits of AI in achieving fully automated manufacturing are immense, several challenges and considerations must be addressed:

1. Data Quality and Security: AI algorithms rely on high-quality data for training and decision-making. Ensuring the accuracy, reliability, and security of data is essential to the effectiveness and trustworthiness of AI systems in manufacturing.

2. Ethical and Social Implications: The widespread adoption of AI and automation in manufacturing raises ethical and social concerns related to job displacement, workforce reskilling, future sources of income, the human need for competition, personal fulfillment, privacy, and algorithmic bias. It is essential to address these concerns proactively and responsibly to ensure that AI-driven automation benefits society as a whole.

3. Integration and Interoperability: Integrating AI technologies into existing manufacturing systems and processes requires careful planning and coordination. Ensuring interoperability between different AI systems, equipment, and software platforms is crucial to achieving seamless integration and maximizing the value of AI in manufacturing.

4. Regulatory and Legal Frameworks: As AI becomes more pervasive in manufacturing, regulatory and legal frameworks must evolve to address issues such as safety, liability, intellectual property rights, and data privacy. Clear and transparent regulations are essential to fostering trust, accountability, and responsible innovation in AI-driven automation.

5. Skills and Training: The widespread adoption of AI and automation in manufacturing will require a skilled workforce capable of designing, implementing, and maintaining AI systems. Investing in education, training, and workforce development programs is critical to equipping workers with the skills and knowledge needed to succeed in the era of AI-driven automation.


There is an old saying, popularized by Oscar Wilde in 1889, that “Life Imitates Art”. In 2017 I published an article on how the Hanna-Barbera animated cartoon, “Jetsons”, is accurately predicting the future: As with “Spacely Sprockets”, where George Jetson ostensibly worked (though he never seemed to be working), our own future looks ahead to a vision of fully automated manufacturing, completely eliminating the requirement for human work. This outlook remains a tantalizing, perhaps inevitable, prospect.

Continued advancements in robotics, AI, nanotechnology, and materials science may eventually lead to the development of fully autonomous manufacturing systems capable of operating without human intervention. These systems would be highly flexible, adaptive, and resilient, able to respond to changing market demands and production requirements in real-time. The only input would be capital since ultimately, all materials can also be produced by AI controlled processes using mining and milling machines and systems designed and built by AI controlled robots.

The realization of fully automated manufacturing would represent a paradigm shift in the nature of work, raising profound questions about the future of employment, the economy, society and personal life fulfillment. With the advent of an increasing number of manufactured human components (ceramic and metal joint replacements, heart pacemakers, eye lenses, neurological stimulators) it is reasonable to speculate that robots, AI and human life will merge at some not too distant date creating a “cyborg society”.