Posted by Sadia Waseem
Can AI algorithims really measure what engineers have been doing for decades?
This misconception was quite common when manufacturers were first introduced to AI-powered time studies. However, an industrial safety manufacturer increased 5% production while eliminating 20% waste through AI-powered time studies.
“It’s not about machines replacing humans, but machines augmenting humans,” explains Robin Bordoli, technology executive and AI expert. This perspective changes conventional time studies from standard time tools to analytical ones that facilitate engineers rather than replacing them.
At General Motors, conventional time studies showed assembly lines meeting standard cycle time targets, but AI-based video analytics discovered workers consistently spent extra seconds searching for tools due to far placements.
This represents the fundamental difference between traditional and AI approaches. While manual time studies are useful in measuring task durations, they cannot effectively measure inefficiencies or complex parts like transition between processes.
In this article, we’ll explore AI-powered time studies in-depth focusing on their benefits, and a practical implementation approach that delivers results without disrupting operations.
The concept of AI-Powered time studies might seem complex, but they operate on basic principles that transform traditional manufacturing analysis. While traditional time studies required tools like clipboards or stopwatches, AI-powered time eliminates the need of these tools analyzing processes in real time.
1. The Evolution of Data Collection:
Traditional time studies rely on an engineer physically observing and timing processes, a method with limitations. AI-powered time studies eliminate the need for manual data collection through:
Computer Vision:
The core of AI-powered time studies is computer vision technology that analyzes video of manufacturing processes. This includes standard cameras that are placed above workstations to capture every movement without disrupting the production. While human observers often face the “Hawthorne effect” where workers change behavior when being observed, cameras become virtually invisible to workers after initial installation.
IoT Sensors:
Smart sensors embedded in equipment and workstations form the second layer of data collection. These sensors monitor machine uptime and downtime, cycle time, and utilization rates without human intervention. Sensors can detect when a machine starts or stops, when materials are picked up or placed down, and even force applied during assembly operations.
Wearable Technology:
Some advanced AI data collection include wearable devices that operators use during their work. Modern wearables are lightweight and feel like nothing during working. These devices can track specific arm or hand movements with more accuracy than cameras, especially in crowded or partially obscured workspaces.
Manufacturers using these technologies have saved time eliminating manual data collection. This led engineers to focus on problem-solving and complex tasks.
2. Processing the Data:
After collecting data through discussed methods, AI technology process the raw information through advanced algorithms:
Automated Activity Classification:
In traditional time studies, after intensive data collection, engineers have to categorize each task accordingly. AI systems perform this classification automatically and separate all recorded activities into three key categories: value-added, non-value-added, and necessary non-value-added activities. This part is quite time consuming if done manually.
Pattern Recognition:
Manual time studies are also prone to human fatigue. As a result, only a few cycles can be analyzed during each shift. AI-powered systems can capture and analyze days or weeks of process. These large samples make it easy to identify patterns that remained unidentified in limited samples. The patterns can be efficiency variations between shifts, gradual declines in performance as the week progresses, or correlations between specific operators and quality outcomes.
Multi-dimensional Analysis:
Apart from analyzing cycle time of process, AI-powered time studies also focused on multiple dimensions of performance. These systems also take into account movement paths, process sequence adherence, operator-machine interactions, and quality-critical motions that affect product outcomes.
These advanced processing capabilities transform subjective observation into objective, data-driven analysis.
3. Transforming Data into Insights:
The final process of AI-powered time studies involves converting large data into clear, actionable information through specialized processing:
Visual Performance Mapping:
The data is transformed into graphical representations so that it can be interpreted easily. These visualizations might represent process flow, comparisons of different operators performing the same task, or distribution of value-added and non-value-added activities.
Root Cause Identification:
AI-powered time studies also detect performance issues apart from cycle time calculations. If cycle time exceeds standard time, the system determines issues occurring during that specific shift or time to identify the root cause of high cycle time.
Digital Process Simulation:
Using collected data, AI-Powered time studies systems developed digital models of current production processes. Engineers can manipulate these virtual representations to predict the impact of potential changes like workstation relocations, fixture modifications, or task rebalancing between stations. The simulation provides evidence-based answers before physical changes disrupt actual production.
Overall, AI-powered time studies are not only automating the data collection, processing, and analyzing but also expanding the scope, depth, and reliability of manufacturing process analysis.
After exploring the working mechanism of AI-driven time studies, let’s see their impact on the real manufacturing environment. Following are the proven benefits associated with AI-driven time studies:
1.Increased Accuracy and Efficiency
Traditional time studies depend on human observation, which can lead to unavoidable inaccuracies and personal biases. AI-powered time studies mitigate this challenge through automated and precise measurement. Research published in the Journal of Cleaner Production demonstrates this impact, with Neural Networks improving production efficiency by 50% in energy consumption. AI ability to capture activities with millisecond precision, and analyze operations without bias improve the overall accuracy of observed data. This precision transforms raw data into reliable insights, directly improving process efficiency and eliminating guesswork.
2. Real-Time Insights and Predictive Capabilities
Traditional time studies provide only a static view of the process, it’s beyond the human capability to present on the production floor continuously and conduct time studies. This can cause missing aspects of the process or important details. AI-powered time studies constantly monitor the process without even blinking which makes the analysis reliable and realistic. Reinforcement Learning has demonstrated this value by reducing cycle time by 20-40% in certain applications through improved real-time scheduling, according to IEEE Transactions on Reliability. This real-time analysis enables manufacturers to identify and address inefficiencies immediately rather than waiting for periodic studies to reveal problems.
3. Cost Savings
The impact of AI-powered time studies extends beyond time-saving and real-time insights. Due to dynamic data collection, these systems reveal hidden optimization opportunities that traditional methods cannot detect. By implementing these opportunities, manufacturers can achieve significant cost reductions across operations. IEEE Transactions on Systems, Man, and Cybernetics reported that AI-driven optimization reduced energy consumption by 10.2% in a study optimizing production schedules. These cost reductions come through multiple channels like optimized labor utilization, reduced energy consumption, decreased material waste, lower quality costs, and improved equipment utilization.
4. Waste Reduction and Quality Improvement
The most significant benefit of AI-powered time studies is their capability to identify waste while simultaneously improving product quality. A case study demonstrated this impact, with AI-driven anomalous cycle detection doubling the output of a production line for a Tier 2 automotive supplier while reducing waste. AI-driven time studies continuously highlight waste in real-time, in the form of motion, waiting, overprocessing, or defects. By identifying quality-critical variations in how tasks are performed, these systems prevent defects before they occur rather than catching them during inspection. This proactive approach to quality, combined with the targeted elimination of inefficiencies, leads to improvements that traditional methods often miss entirely.
5. Scalability and Adaptability
Manufacturing operations are constantly becoming more complex with high product variety. Complexities can be due to frequent changeover, and changing market demands. Under these circumstances, manufacturers need a dynamic system, like AI-powered time studies that adapts to the production environment. The International Journal of Advanced Manufacturing Technology reported that AI Optimization could handle large optimization problems with calculation times as low as 479 seconds, even for complex instances. This scalability allows manufacturers to analyze multiple workers, processes, or production lines simultaneously rather than being limited to isolated studies.
These five key benefits demonstrate why AI-powered time studies represent a fundamental advancement rather than simply an incremental improvement over traditional methods
The compelling benefits of AI-powered time studies makes this technology attractive for manufacturers, but achieving these advantages requires proper implementation. A structured approach helps ensure successful adoption while minimizing disruption to current production.
1.Identifying Processes for Analysis:
The implementation process start with right selection of manufacturing processes for initial study:
Focus on High-Impact Areas: Start with processes which are already analyzed and have baselines, inefficiencies, bottlenecks, or high labor content. Look for operations where even small improvements would lead to significant returns due to high volume or critical path status. Manufacturing lines with significant walking distances, multiple handoffs, or frequent delays make an ideal choice of initial implementation.
Consider Measurement Challenges: Some processes are naturally more suitable for AI-powered analysis than others. Operations that have visible movements, measurable steps, and defined cycle boundaries produced the most immediate insights.
Set Clear Objectives: Define specific goals for your time study implementation. It can be reducing cycle time, improving ergonomics, balancing workload, or identifying waste. Clear objectives help focus the implementation and provide metrics for measuring successful results.
Tools like Kaizen Copilot simplify this initial phase by allowing manufacturers to start with any clearly visible process without any additional tools or setup.
2. Data Collection and Preparation:
Once target processes are identified, the next step involves capturing the necessary data:
Video Recording Setup: Use your smartphones to capture the entire process without disrupting your current production. For optimal results, ensure adequate lighting, clear sightlines to all work areas, and minimal visual obstructions. Unlike traditional time studies that require multiple samples, a single good video capturing several complete cycles can provide sufficient data for comprehensive analysis.
Simplified Implementation: Modern AI solutions like Kaizen Copilot help manufacturers to avoid the complex setup traditionally required for manual time studies. The process becomes as simple as recording a video and uploading it to the software. The software automatically detects work elements without any extensive manual configuration.
Process Context: AI systems handle most of the analysis automatically, but providing basic information of process could improve results. This can be details about standard procedures, critical operations, or major process concerns. The information ensures that the analysis focuses on what matters most for your specific manufacturing environment.
This simplified data collection approach represents a fundamental shift from traditional time studies, where engineers spent days gathering data before analysis could even begin.
3. Analysis and Insight Generation:
With data collected, the AI system processes the video to generate actionable insights:
Automated Time Classification: AI systems automatically classify the observed process into value-added, non-value-added, and necessary non-value-added activities. This classification helps identify which activity is not contributing to any customer value. Kaizen Copilot provides detailed visualizations of this breakdown which makes it easy to identify improvement opportunities.
Multi-cycle Pattern Recognition: Beyond basic time measurement, AI systems identify patterns across multiple cycles that identify hidden inefficiencies. These patterns might include inconsistent techniques between operators, gradual fatigue effects, or unnecessary movements. The system detects these subtle variations without requiring multiple time studies or different observers.
Prioritized Improvement Suggestions: AI systems do not only provide basic analysis. Modern AI solutions like Kaizen Copilot generate specific improvement recommendations. These might include workstation layout changes, tool placement optimization, or process sequence modifications that would be difficult to identify through traditional methods.
The transformation of video data into actionable insights happens in minutes rather than the days or weeks traditionally required by manual time studies.
Through this structured implementation framework, manufacturers can successfully transition to AI-powered time studies without affecting their production and attaining maximum benefits. This approach transforms a traditionally complex industrial engineering practice into a practical, everyday tool that facilitates industrial engineers to drive continuous improvement.
AI-powered time studies can lead to significant benefits when implemented properly with the right AI tools. Before implementation, there are several important challenges and considerations that should be taken into account for successful implementation.
1.Technical Expertise Requirements:
AI systems require specialized knowledge that many manufacturing teams lack. This knowledge gap can slow down adoption and limit potential benefits. Kaizen Copilot offers a user-friendly interface and automated analysis that don’t require AI expertise or any coding experience. Its one-click analysis features automatically detect work elements. This eliminates the need for any additional systems and makes the technology accessible to everyone without any formal background.
2. Change Management Concerns:
Workers often worry that time studies will eliminate their jobs or impose unrealistic standards. This concern is particularly true with AI-based systems that may seem mysterious or threatening. Kaizen Copilot focuses on process improvement rather than individual performance evaluation. Its non-intrusive video analysis does not cause any disruption to existing processes, like traditional time studies that involve observers with stopwatches monitoring workers directly.
3. Implementation Costs and ROI:
AI-powered time studies require investment in technology and employee time for training and adaptation. This direct cost can be a barrier, especially for smaller manufacturers. Kaizen Copilot’s cloud-based solution justified these costs through results delivered in hours rather than months. The system works with smartphone videos that eliminate the requirement for any specialized hardware equipment.
4. Measurement Accuracy and Validation:
AI-powered analysis provides accurate, reliable results essential for effective decisions. AI systems must correctly differentiate between value-added and non-value-added activities since wrong classifications can lead to misguided improvement efforts. Kaizen Copilot’s computer vision algorithms are trained specifically for complex manufacturing environments to provide clear visualizations of time classification that makes the analysis process accurate.
5. Ergonomic Assessment Limitations:
Traditional time studies focus on efficiency and cycle times, without any consideration of the physical impact of work on operators. This narrow focus can lead to improvements that increase productivity at the cost of worker well-being. Kaizen Copilot also includes ergonomic assessment tools that analyze worker movements to identify potential injury risks and physical strain. The additional dimension helps manufacturers balance efficiency improvement with operator health and safety.
Addressing these challenges with appropriate strategies and solutions like Kaizen Copilot, manufacturers can successfully adopt AI-powered time studies with their full potential.
AI-powered time studies represent a fundamental advancement in the optimization of the manufacturing process. These systems identify bottlenecks that traditional methods miss, through their advanced data collection and analysis capabilities. The benefits of AI-powered time studies are observed across multiple dimensions, like improved accuracy, real-time insights, cost saving, and systematic waste reduction. While implementation challenges exist, solutions like Kaizen Copilot have made this technology accessible to manufacturers of all sizes through user-friendly interfaces, non-intrusive methods, and ergonomic assessments that traditional approaches lack. With the growing complexity of the manufacturing environment, AI-powered time studies empower organizations to achieve sustainable productivity improvements.
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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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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.
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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.
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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.
