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Posted by Saif Khan
In many factories, time slips away fast. You might watch a worker repeat a task over and over, trying to track every second, but hidden inefficiencies often stay hidden. This is a common frustration for industrial engineers doing standard manufacturing time study.
For years, time studies have been key to good operations. Still, they can be slow, subjective, and easily influenced by personal opinions. What if you could watch, analyze, and improve processes with accuracy, cutting down the time from weeks to minutes?
That’s where AI time studies come into play.
A standard manufacturing time study means watching workers, noting each movement, sorting them as useful or not, and then averaging the data to set a standard. This method is pretty old.
The problem isn’t that the method is wrong; it just doesn’t fit how fast things change in manufacturing now. Product lines change quicker, there’s more custom work, and supply chains are less stable. You can’t waste weeks studying a process while a new variable appears with the next shift.
Even worse, manual time studies can be unconsciously biased. Two engineers might see the same work differently. Workers might also speed up when they know they’re being watched. This leads to unreliable info, and then unreliable decisions.
This unreliability results in missed chances for improvement, bad layouts, and uneven workloads.
Artificial intelligence is changing how factories measure time and motion. Instead of watching by hand, AI systems use cameras and machine learning to automatically spot and sort actions in a video.
With systems like Retrocausal’s Kaizen Copilot, an engineer just films a workstation cycle with a phone. The system then reviews the video, frame by frame, figuring out each task, how long it took, and if it was helpful or not.
The whole thing gets done in minutes, not days or weeks.
It’s not just automation, it’s making things better. The AI assists the engineer.
AI time study uses cameras. The software watches video, knows human actions, and breaks them into steps.
Each step gets automatically labeled, tool use, walking, assembling, or waiting. The AI figures out how long each move lasts, giving a complete cycle time analysis.
The result is a clear look at what’s helpful and what’s not. Engineers can see issues, downtime, and wasted motion.
This also helps with balancing lines and planning layouts. With correct time data, factories can quickly adjust workloads, change pace, and change workstations to match what’s needed.
When talking about making manufacturing better, you’ll hear about things like work measurement, cycle time analysis, standard work, and ongoing improvement. AI time studies pull it together.
By adding body position checks and motion tracking, AI does more than just measure time—it helps keep workers safe. A bad workstation layout might cause too much bending or reaching, and the system can spot and flag that.
Also, using data to improve processes means every fix is based on facts, not guesses.
Traditional time studies often have problems. Engineers trust their results, but they only see a small picture.
Imagine two workers putting together the same part. One might pause a bit longer now and then because of tiredness or where the tools are. A person watching might miss that. But AI sees every small difference. Those little pauses add up to lost time over a shift or a week.
By getting rid of human bias, AI time studies show patterns that weren’t visible before.
Getting info is one thing, understanding it is another. That’s where AI systems are useful.
With tools like Kaizen Copilot, engineers can:
This turns video into practical info. Engineers can spend more time planning improvements and less time on paperwork.
A good look at manufacturing has to involve speed, safety, and comfort.
AI systems check body positioning using models like REBA and RULA. They spot bad positions and suggest fixes to prevent injuries.
A parts maker used AI video to find uncomfortable reaching patterns that slowed work. After changing the workstation, they improved speed and made workers less tired. Everyone benefited.
By watching speed and checking body positioning, AI is a partner in making ongoing improvements.
Here’s what it costs.
A manual time study can take an engineer several days. If you have many stations, that can waste weeks. During that time, process problems keep costing money.
AI cuts it to minutes. The savings add up, not just in work hours, but in faster choices and less downtime.
Also, because it doesn’t need special equipment, most factories can use these systems. Usually, a phone is all you need.
AI in manufacturing meets doubt. Some fear it will replace human thinking or invade privacy.
These systems think about privacy and openness. Facial blurring and secure cloud servers keep workers anonymous.
AI doesn’t replace people, it makes them better. It helps engineers make smarter, faster, and fairer choices based on actual facts.
Trust grows as teams see improvements in speed and safety.
Think about a factory where every station is planned well, every move is tracked, and every risk is taken care of before it causes a problem. That’s what AI time studies can do.
Instead of reacting to problems, you expect them. Instead of guessing at answers, you plan with confidence.
It’s going from measuring to mastering manufacturing.
As AI gets better, time studies will do more than just watch and analyze. Future systems will use data to guess when processes will wander off track or when tiredness will affect work.
They’ll mix data from sensors, machine learning, and real-time feedback to create systems that improve on their own.
The factory gets lively, it learns, adapts, and always gets better.
A manufacturing time study is now a tool for understanding and improving how people, processes, and machines work.
AI makes that understanding faster, deeper, and more correct.
For engineers and leaders, it’s more than a boost. The question is no longer how long a task takes but how we can help our people and systems do their best.
That’s what AI can do: turn facts into knowledge and observation into opportunity.
See AI Time Studies in Action
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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.
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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.
