Get insightful content delivered right to your inbox!
Posted by Saif Khan
Industrial engineers know the feeling of watching hours of workstation footage, trying to capture movements accurately to set time standards. It’s boring, easy to make mistakes, and wastes time.
For years, motion time systems like MODAPTS analysis have been key for work measurement in manufacturing. But with today’s fast production, the old way can’t keep up. That’s where AI-powered tools come in. By handling observation, classification, and calculation, AI helps engineers do MODAPTS analysis faster, more accurately, and with less effort.
MODAPTS, or Modular Arrangement of Predetermined Time Standards, measures the time for human motions in industrial work. Instead of using a stopwatch, MODAPTS uses codes for basic body motions like reach, move, or bend, each with a time value in MODs.
The idea is simple: knowing the basic movements in a task lets you predict how long it will take. This helps create labor standards that improve efficiency and ergonomics.
MODAPTS analysis has been a trusted way to develop standard work, time and motion studies, and process optimization frameworks. But doing it by hand makes it slow and subjective.
That’s the problem. Engineers want the precision of MODAPTS analysis but not the huge workload.
Think of an engineer looking at a complex assembly task. The worker reaches, positions, aligns, secures, and inspects, in under 20 seconds. Turning that into MODAPTS analysis means noting each movement, guessing time values, and documenting everything.
This can take hours for one workstation cycle. Also, people can be biased. Two engineers might see the same motion differently, leading to standards that don’t match.
Many factories still use videos, spreadsheets, and manual coding. These work, but aren’t right for today’s production speed. Manufacturers with different products can’t spend a week on motion times for each line change.
That’s where AI-driven tools make a big difference.
AI automates and adds accuracy to a process that used to be all manual. Instead of relying on an engineer’s view, computer vision and machine learning can spot human movements from video.
AI systems, like Retrocausal’s Kaizen Copilot, use a smartphone video of a workstation and break it into steps automatically. Then, the software finds each motion, reach, grab, move, release, and turns them into MODAPTS analysis codes in minutes.
This is more than just speed, it changes everything.
AI gets rid of data entry, makes sure motion is interpreted the same way each time, and removes the subjectivity that frustrated engineers. It also creates a record, making it easy to compare results, run line balancing simulations, or share reports.
AI turns MODAPTS analysis from a hard task into part of continuous improvement.
Here’s how the AI-powered MODAPTS analysis process works:
What used to take a day now happens fast. And since the analysis uses computer vision, the precision doesn’t depend on who’s doing the coding.
When done by hand, small mistakes in motion can become expensive. A few MODs off can spread across hundreds of cycles, messing up productivity and labor cost models.
AI tools lower these risks. By using motion libraries and algorithms, they create results that are accurate and repeatable. For companies doing lean manufacturing or Six Sigma, this is helpful.
Also, the AI doesn’t just copy human observation, it makes it better. It can spot things that people miss, like small delays or long reach distances. This makes MODAPTS analysis faster and smarter.
A faster analysis helps, but it’s better when part of a continuous improvement strategy. With AI-powered MODAPTS analysis tools, engineers can see value-added time, rebalance lines, and redesign workstations.
Retrocausal’s platform, for example, combines MODAPTS analysis, time and motion studies, and line balancing into one system. This means an engineer can use one video and get information across productivity, quality, and safety.
This creates a feedback loop. Each improvement can be measured and improved using the same platform. As data is collected, the AI learns, improving its predictions and giving better tips for work measurement and ergonomic analysis.
Human judgment is powerful and flawed. In engineering, that’s true. Engineers bring understanding and experience to process design, but judgment can’t beat the scale of machine computation.
AI doesn’t replace engineers, it helps them. By automating tasks, it lets engineers focus on better analysis, innovation, and working together. Instead of measuring every reach, they can spend time designing better workstations or planning improvements that change the bottom line.
This makes engineering work better. The hours spent coding motions can be used for problem-solving and process optimization.
MODAPTS analysis is just the start. As AI systems get better, they’re putting methods, MODAPTS, MTM, MOST, together. Imagine using a video and switching between work measurement models to compare results.
AI platforms also bring abilities like ergonomic risk detection and failure mode analysis. These tools measure and understand motion, spotting patterns that suggest tiredness or strain.
AI is helping engineers see what was always there.
MODAPTS analysis has always been about improving productivity and worker well-being. But when the method is slow, it doesn’t work.
AI fixes that. It keeps the power of MODAPTS analysis while removing the manual work. The result is faster decisions, better standardization, and greater ability to handle production challenges.
With manufacturers being asked to do more with less, AI-powered tools like Kaizen Copilot change what’s possible.
They turn data into insight, insight into action, and action into measurable results.
Every factory depends on human motion. The hands that assemble and lift are key. MODAPTS analysis gives engineers the language to describe that motion. AI gives them the speed to act on it.
The partnership between human and AI isn’t about replacing expertise, it’s about expanding it. With AI accelerating MODAPTS analysis, the future of industrial engineering looks faster, smarter, and more human.
Schedule a Demo Today!
See how AI can transform your MODAPTS analysis in action. Schedule a free demo today and experience how Kaizen Copilot speeds up work measurement, improves accuracy, and helps your team achieve continuous improvement, without the manual effort.
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.
