Automated time studies use AI, computer vision, and video analysis to measure how long manufacturing tasks take and identify opportunities to improve process efficiency. Unlike traditional stopwatch time studies, which rely on manual observation and timing, automated time and motion analysis helps industrial engineers capture, classify, and compare work activities with less manual effort.
DeepHow uses Physical AI to turn video of manufacturing operations into structured process insights, helping teams analyze cycle times, identify non-value-added activities, understand process variation, and establish more accurate baselines for continuous improvement.
DeepHow automates key parts of the traditional time study process by transforming recorded manufacturing activity into measurable insights about work execution, cycle times, and process efficiency.
Record production processes, assembly operations, and repetitive manufacturing tasks to capture how work is actually performed.
Physical AI and computer vision analyze recorded activity to identify process steps, motions, and work sequences, reducing the need for manual observation and classification.
Analyze task durations, compare production cycles, and identify differences in how work is performed across operators, shifts, products, or operating conditions.
Reveal unnecessary motion, waiting, repeated handling, and other non-value-added activities that may be limiting productivity or extending cycle times.
Use structured time and motion analysis to support line balancing, standard work optimization, process redesign, and continuous improvement initiatives.
Traditional time studies provide valuable information about manufacturing performance, but manual observation, stopwatch timing, and spreadsheet-based analysis can make them difficult to scale across production operations.
Industrial engineers spend significant time observing processes, recording cycle times, and manually documenting work activities.
How DeepHow helps: Automate portions of video analysis, activity classification, and cycle-time measurement so engineers can focus more time on identifying and implementing improvements.
A handful of observed cycles may not represent how a process performs across different shifts, production conditions, or product variants.
How DeepHow helps: Analyze recorded production activity across more cycles and operating conditions to build a more complete picture of process performance.
Unnecessary movement, repeated handling, waiting, and inefficient work sequences can accumulate into significant production losses.
How DeepHow helps: Use AI-powered motion analysis to identify non-value-added activities and reveal opportunities to reduce wasted time.
Uneven workloads between connected workstations can create bottlenecks, waiting, and underutilized production capacity.
How DeepHow helps: Compare cycle times and activity across workstations to support more effective line balancing and workload distribution.
Without consistent before-and-after measurements, teams may struggle to determine whether a process change actually improved performance.
How DeepHow helps: Establish measurable process baselines and compare execution before and after changes to validate improvements.
Automated time and motion studies help manufacturers reduce manual analysis effort, improve process visibility, and make more informed decisions about productivity, labor utilization, and production capacity.
Measure and compare task durations across production cycles to understand process performance and identify variation.
Classify work activities and identify unnecessary movements, repeated actions, and opportunities to eliminate non-value-added work.
Compare workstation cycle times and workload distribution to identify bottlenecks and opportunities to improve production flow.
Understand how work execution and cycle times differ across shifts, product variants, and operating conditions.
Use observed production activity to identify more efficient work sequences and support the development of standardized processes.
Compare before-and-after time study results to determine whether Lean initiatives, workstation changes, or process improvements delivered measurable benefits.
Reduce the manual effort required to analyze production processes, allowing engineering teams to study more operations and spend more time improving them.
Identify recoverable production capacity by reducing wasted motion, unnecessary waiting, and inefficient work sequences.
Automated time studies help manufacturers analyze and improve work across assembly lines, discrete manufacturing, automotive production, aerospace, electronics, medical devices, packaging, and other industrial environments. Industrial engineers and continuous improvement teams use AI-powered time and motion analysis to measure cycle times, identify wasted motion, improve line balancing, optimize standard work, evaluate workstation layouts, compare production methods, and validate Lean manufacturing improvements.
By transforming recorded work into structured operational data, DeepHow helps manufacturers understand how production tasks are performed, where time is being lost, and which process changes can improve productivity, labor efficiency, and throughput.
Learn more about Time and Motion Analysis
Turn manufacturing video into actionable time and motion intelligence. Automate cycle-time analysis, uncover wasted motion, and improve productivity with DeepHow's Physical AI.
Request a Demo
