The Rise of AI in Manufacturing: A Brief History
- Çağkan Ekici
- Mar 28, 2023
- 6 min read
Updated: 1 day ago
Explore how Artificial Intelligence has evolved in manufacturing, as we learn about its history and accomplishments.
What Constitutes Artificial Intelligence?
Artificial Intelligence (AI) has become a buzzword across a wide range of sectors, from automotive to textile, and for good reason. However, the complex terminology surrounding AI can be hard to grasp for those unfamiliar with the subject. To begin with, it's important to define AI as the science of programming computers to make human-like decisions, essentially simulating human intelligence. While we encounter AI regularly in our daily lives through smartphones and social media, it has also had a significant impact on manufacturing operations.
Classifying the Cognition Spectrum: AI Types
There are three general categories of AI, each based on the level of capability:
1. Artificial Narrow Intelligence (ANI) is currently the only type in existence and focuses on simulating human behavior to complete specific tasks.
2. Artificial General Intelligence (AGI) would be able to think like humans, but it is still a theoretical concept as machines lack consciousness.
3. Artificial Superintelligence (ASI) would be able to develop beliefs and an understanding of human nature, posing ethical issues for some.
As each of the three types of AI is defined above, Artificial Narrow Intelligence (ANI) is the sole type that is used in commercial life. ANI can perform a specific task effectively without human intervention. Examples of ANI include language translation and image recognition. Tools like Siri, Google Translate, and ChatGPT, they do exhibit impressive capabilities, such as problem-solving, translation, and summarization, but all of these rely on statistical prediction, not true reasoning or awareness.
Even though the potential benefits and risks of AI are frequently debated, its uses are rapidly expanding and there is still much to be accomplished.
Back to the Beginning
AI has been a topic of interest for researchers and developers since the mid-20th century. The history of AI is marked by significant breakthroughs in the field of computer science. Let’s explore the journey of AI with the highlights from its history.
The 1950s, The Birth
The idea of AI was first introduced by Alan Turing in 1950 through his Turing test, which aimed to determine if machines could think. Later in the decade, John McCarthy coined the term "Artificial Intelligence" at Dartmouth College, along with other founders such as Marvin Minsky, Allen Newell, and Herbert A. Simon.
Late 1960s, AI Winter
While there were significant advancements in chess-playing computers, AI experienced a decline in the late 1960s, referred to as the "AI Winter," due to funding and general interest diminishing.
Late 1978s, SCARA
Manufacturing began to incorporate aspects of AI into the industry despite the little growth that happened during this period. SCARA, an assembly line robotic arm, was developed in 1978 and was utilized in manufacturing. Yet, there was little significant progress in AI until the late 1990s.
Late 1990s, The Champion
By the late 1990s, AI began to experience a resurgence with a computer beating the chess world champion, which marked the end of the AI Winter.
Since then, AI has continued to make advancements, and the manufacturing industry has found ways to optimize operations with its help. AI provides benefits such as greater efficiency, lower costs, improved quality, and reduced downtime for manufacturers. Machine learning, an umbrella term for AI, enables data collection, interpretation, and decision-making to be done with ease, allowing for optimized operations and increased efficiency and productivity.
In the 2000s, The Popularity of AI
Products, movies, innovations, and more:
· KISMET, which was a robot with a human-shaped face, was developed by Professor Cynthia Breazeal in 2000.
· The sci-fi film A.I. Artificial Intelligence, directed by Steven Spielberg, is released in 2001.
· Google secretly developed a driverless car in 2009. By 2014, it passed Nevada’s self-driving test.
2010 to Present, Unprecedented Success of AI
AI has become embedded in our day-to-day existence. Companies have started to increase their investment in AI-based technologies.
Revolutionizing Manufacturing with AI
Today, the role of artificial intelligence (AI) in manufacturing has evolved from a promising tool into a core enabler of what we now call smart factories. Far beyond basic automation or data‑logging, modern AI systems, often combined with digital twins, machine learning, and cognitive automation, are reshaping entire production ecosystems.
What’s New — How AI Delivers Value in Modern Manufacturing
Predictive Maintenance & Uptime Maximization: AI‑driven predictive maintenance has become a cornerstone: machine‑learning models now forecast equipment failures with increasingly high accuracy.
Although performance varies depending on the use case, many predictive maintenance applications reported accuracy levels typically ranging between 85% and 98%, with several studies demonstrating significant improvements in machine uptime and failure detection capabilities.
Automated, AI‑Powered Quality Control: Advanced systems using computer‑vision and deep‑learning are transforming quality assurance. These inspect every unit in real time, catch microscopic defects or subtle deviations often undetectable by human inspectors, and ensure consistent product quality.
Smart Supply Chains & Demand Forecasting: AI is no longer just on the factory floor: it’s integrated across supply chains. From demand forecasting and inventory optimization to logistics scheduling and risk mitigation, AI enables more resilient, responsive, and lean operations.
Real‑Time Process Optimization & Flexible Production: Through the convergence of AI, IoT, and digital‑twin technologies, manufacturers can simulate, monitor, and optimize production processes continuously. AI has enabled data gathering to be easier with multiple alternatives ,including computer vision systems. This enables adaptive resource allocation, dynamic scheduling, downtime reduction and fast reaction to changing conditions, boosting productivity and reducing waste.
Strategic Decision Support & New Business Models: AI is evolving beyond operational support: advanced frameworks now integrate domain knowledge, data, and ML models to support comprehensive industrial decision‑making, shifting manufacturing toward more strategic, data‑driven operations.
Bigger Picture: Why AI Matters
The convergence of global competitive pressure, demand for customization, sustainability requirements, and supply‑chain volatility is forcing manufacturers to evolve, and AI offers a way forward.
Moreover, modern AI implementations aren’t just experiments or pilot projects; they deliver measurable ROI: lower maintenance costs, fewer defects and reworks, more efficient inventory and supply‑chain operations, improved throughput, and overall operational resilience.
Taken together, AI has become the backbone of “smart manufacturing”: companies that adopt it strategically gain a significant competitive advantage, while those that don’t risk being outpaced.
Not a Magic Wand: Challenges & Considerations
Data quality and infrastructure readiness: AI’s effectiveness depends heavily on high‑quality data.
Integration complexity: Combining traditional manufacturing systems with AI, digital twins, supply‑chain platforms, and decision‑support tools requires careful planning.
Human & organizational factors: Workforce adaptation, training, and change management remain critical.
Scalability & customization limits: Not all AI solutions work equally well across every manufacturing context; what works for one production line may not translate directly to another.
Conclusion: Turning AI Promise into Production Impact - With Khenda
AI has moved beyond theory in manufacturing — it’s now a decisive tool for reducing waste, increasing agility, and accelerating continuous improvement. But for real impact, technology must be fast to deploy, affordable to scale, and grounded in real-world shop floor needs.
Khenda offers two complementary AI systems that deliver exactly that:
Continuous Improvement(CI) Platform: Automates time studies using video analytics, helping engineers complete analyses 95% faster and enabling factories to unlock up to 36% efficiency gains, without any hardware integration.
Vision MES: Captures real-time production data directly from the shop floor using only cameras. With zero PLC dependency, it provides instant visibility into downtime, interventions, and compliance events. In some deployments, Vision MES has delivered up to $200K in annual gains per station by reducing chronic micro-stoppages and unplanned downtime.
Together, these systems turn everyday video footage into a rich stream of actionable insights, enabling manufacturers to digitize fast, improve continuously, and scale confidently.
To learn more about improve production with AI and cost-effective offerings, visit the Khenda.
14-day free trial of Khenda's CI Platform: Register or Request a Demo Meeting
Learn more about real time production monitoring: Vision MES
FAQ
How is AI used in manufacturing today?
Modern factories use AI for:
Predictive maintenance to reduce unplanned downtime
Automated quality inspection using computer vision
Process optimization via digital twins and real-time analytics
Supply chain forecasting and labor efficiency tracking
Why is video analytics emerging as a game-changer in manufacturing?
Video analytics combines computer vision and AI to extract insights directly from camera footage. Unlike traditional sensor-based systems, it enables:
Real-time detection of machine downtime and operator interventions
Compliance monitoring (PPE, hygiene)
Time and motion studies without manual data collection
References
Benhanifia, A., Ben Cheikh, Z., Oliveira, P. M., Valente, A., & Lima, J. (2025). Systematic review of predictive maintenance practices in the manufacturing sector. Intelligent Systems with Applications, 26, 200501.