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Posted by Javeria Rahim
The manufacturing industry has been notorious for slow adoption of technological advancement when compared to other sectors. It can be due to legacy equipment, the fear of disrupting operations or lack of skilled labor. However, this is not the case with artificial intelligence (AI).
The market for artificial intelligence in manufacturing is expected to touch $20.8 billion in 2028 from $3.8 billion in 2023. A 45.6% growth is not implausible given the rapid commoditization of AI and how accessible it is becoming for manufacturers of all sizes.
One cannot help but wonder why AI is becoming an area of interest for manufacturers? Is it because it is simply ‘trending’ these days or is it because AI has the potential to transform manufacturing like never before?
Many manufacturing industries are adopting AI; some notable ones include aerospace, automotive, pharmaceutical and defense, with automotive leading the charge and expected to hit $7 billion by 2027. Not a surprising figure as AI is quite helpful in reducing defects and increasing quality of products as well as improving processes.
Another example of AI in manufacturing is for the electronics industry. Nvidia, the world’s biggest designer of computer chips, uses AI to place transistors on chips, which not only impacts the chips’ speed and power consumption but also reduces cost.
So, how exactly is AI adding value to the manufacturing sector? According to the State of AI in Manufacturing report 2024, here are the top use cases for AI in manufacturing:
1. AI and Process Optimization
One of the most important use cases of AI in manufacturing is for process optimization. According to McKinsey, 70% of manufacturers are already using or plan to use AI for process optimization. To see the impact of AI on process optimization, let’s take an example of a workstation that must be optimized. Traditionally, engineers and managers measure all the metrics and use their knowledge and experience of the manufacturing process to identify bottlenecks and areas for improvement. In some cases, it is done through Excel sheets as well. However, with AI a workstation can be optimized in no time.
AI-powered continuous improvement solutions such as Kaizen Copilot use computer vision and advanced AI algorithms to conduct time and motion studies and recommend the best station design. Unlike traditional methods, it also allows industrial engineers to balance lines for maximum efficiency within minutes.
2. AI and Safety
It is amazing to see what AI can do to improve the safety of factory floors. If you look at traditional ergonomics, it was mostly reactive. It relied on identifying and addressing ergonomic issues after they arise, often through employee surveys, incident reports, or physical evaluations by ergonomists. Moreover, assessments were based on snapshots in time and often missed underlying issues.
AI powered ergonomics solutions, on the other hand, use computer vision and sensors to continuously monitor work environments and identify potential ergonomic risks before they cause injuries. Moreover, these solutions provide targeted recommendations for individual workers based on their specific tasks, postures, and risk factors.
3. AI and Quality control
When it comes to ensuring quality in manufacturing, it is not just about meeting compliance standards; it is also about reducing warranty costs, recalls and scrap costs. AI can do wonders by continuously monitoring production lines, identifying defects and inconsistencies that human inspectors might miss.
AI-powered solutions such as Assembly Copilot uses advanced computer vision and AI algorithms to identify in real-time when a worker has missed a step or made a mistake and alerts them through monitors, projected instructions, and signal towers, so that the error can be fixed before it is too late.
The primary objectives of manufacturers are to achieve throughput, quality, and cost targets while ensuring a safe work environment. However, this is becoming increasingly difficult due to process complexity, variability in demand from customers, and the labor shortage plaguing the industry. However, AI is now addressing these challenges.
According to the 2024 State of AI in Manufacturing report, manufacturers who have adopted AI see benefits in three major areas: increased productivity, improved efficiency and production efficiency.
Here are some more benefits of AI:
AI improves every aspect of manufacturing ranging from process optimization to quality control. AI can help identify bottlenecks, optimize production scheduling, and even predict maintenance needs, which ultimately leads to smoother operations, reduced downtime, and minimized waste. The best part about AI is that instead of only addressing symptoms, AI also pinpoints the root cause, leading to more effective and lasting solutions for improved efficiency.
According to a study by Deloitte, the US manufacturing industry could create as much as 3.8 million jobs between 2024 and 2033 but 1.9 million of these jobs could go unfilled if workforce challenges are not sorted. One of the reasons why there is a skill shortage in the manufacturing industry is because there is an increase in demand for digital skills as operations are becoming more complex due to smart connected devices.
AI is more than capable of addressing the skill shortage because it not only makes the job easier for people with little to no technical skills, but it also increases their productivity, so essentially you experience the output of 4 workers instead of one. Additionally, solutions like the Assembly Copilot make it easier to onboard fresh talent on the production line with real-time projected guidance and feedback.
According to a research conducted by Aptean, manufacturers that have adopted AI are seeing growth in their revenue due to reduced manufacturing costs.
AI can help reduce costs in several ways: it can help you reduce labor costs by making complex tasks easier and quicker to do. Moreover, it reduces material costs by eliminating rework and quality issues. Moreover, the predictive nature of AI helps increase equipment uptime and efficiency. AI also helps manufacturers be proactive rather than reactive, flagging potential risks and issues and providing suggestions on how to overcome them.
One of the most notable benefits of implementing AI in manufacturing is its ability to distill data driven insights. Traditional manufacturing relied heavily on human experience and intuition. Today, the manufacturing industry generates massive amounts of data and AI excels at analyzing large, and complex datasets, uncovering hidden patterns, and generating data-driven insights.
Measuring the ROI from AI in manufacturing can be tricky because AI’s benefits can encompass both financial gains and less tangible improvements. However, manufacturers can track various metrics to assess the value of their AI investment. Here’s a breakdown of couple of key approaches:
AI in manufacturing often conjures up an image of robots taking over factories. However, AI is not here to replace humans, rather it is here to complement them.
For example, AI is better at processing massive datasets and automating mundane tasks with unwavering precision, while humans are better at developing innovative solutions, adapting to unforeseen circumstances, handling delicate tasks, intricate assembly processes, and conducting quality control checks that require human touch and judgment.
Despite the numerous benefits that AI has to offer and the increasing adoption interest, some manufacturers can be still hesitant about AI. Research suggests that the top four reasons why manufacturers are reluctant to leverage AI is because of lack of internal expertise and knowledge, difficulty integrating with existing systems, high implementation costs and unclear expectations of ROI.
While these are reasonable concerns, AI is becoming more affordable and common. Solutions like Kaizen Copilot and Assembly Copilot, for example, are easy to use so users can get started without any extensive training. Moreover, they work with ordinary tools and easily integrate with existing MES and other systems, so there is no complex implementation that disrupts operations.
The integration of AI in manufacturing is more than just a passing trend; it represents a significant leap towards greater efficiency, safety, and quality. As the market for AI in manufacturing is projected to grow substantially, manufacturers are increasingly recognizing the transformative potential of AI-driven solutions.
By optimizing processes, enhancing safety protocols, and improving quality control, AI is addressing some of the most pressing challenges in the industry. Despite initial barriers to adoption, such as implementation costs and integration difficulties, the benefits of AI — ranging from cost savings to bridging the labor shortage gap — are compelling. Many manufacturers have already adopted or are thinking of adopting AI in their operations. It is only a matter of time before even those who are skeptical are convinced that advantages will outweigh any hesitations they may have.
Brandin and his team help our customers with setting up the Pathfinder platform and getting up-to-speed on all the features and functionalities, in addition to assisting them with standardizing their processes where needed.
Brandin is a seasoned manufacturing leader, who has spent more than a decade designing and setting up assembly lines in both the US and China, optimizing them, and working with human operators to maximize productivity and quality.
Jesse works closely with our marquis customers to understand their needs, and figures out the best solution that immediately adds dollars to their bottom line.
Jesse has two decades of enterprise sales experience. He has held sales leadership roles at pioneering technology startups.
Andrey takes inventions and converts them into innovations. He leads our product engineering team to turn our scientific prototypes into robust customer-facing products.
Andrey co-founded and successfully exited a startup to immigrate to the US. He spent his next eight years building the core data science infrastructure for Amazon’s supply chain planning team, and the CV/ML infrastructure for Microsoft’s HoloLens organization.
Zeeshan focuses on delighting our customers by understanding their needs and defining product and strategy to meet and exceed these needs. He works closely with our sales leadership to grow our customer base.
Zeeshan was a scientific leader at Microsoft’s HoloLens division, where he invented AI technologies to assist frontline workers across industries. Earlier, he shipped IoT platforms and visual defect detection software for manufacturers at Siemens and MVTec GmbH.
Huy takes our product roadmap and figures out what’s achievable with today’s AI advances, and what we need to invent to solve our customers’ pain points.
Huy led teams of scientists and engineers at NEC and invented technologies for driver assistance systems that are part of 500,000+ Honda cars. He is an inventor on more than 20 US patents, and his research papers are cited by every lab from MIT, Stanford, and CMU to Apple, Google, and Baidu.
