Imagine that you don’t have people to just stand and observe and take notes over three shifts a day for a week or so. What if you could position a few cameras in strategic locations. The video is captured and run through analytics. Engineers, operators, and managers would not have to manually parse through hours of video. They would be presented with data visualization designed to help them get to root causes of problems, assist worker ergonomics, improve safety, and boost productivity.
That is what the Sensable solution does.
Imagine another scenario. You are an operator on a production line. You have been trying to point out bottlenecks to production on your machine. Then engineers install streaming video pointing not just at a specific point on you or the machine but with wide enough scope to see the larger process. The video analytics point out the bottleneck. Voila. Vindicated. Proof in the data.
The video is not for spying on employees. It is designed to help them. Just what true digital transformation is—an aid to decision making and continuous improvement.
Key spots:
- Missed throughput targets—station utilization lower than expected, unplanned downtimes more than planned
- Low process efficiency—cycle time variability, too many interruptions
- Low operations visibility—safety challenges due to best practices violations, missed inspection or assembly steps
Use cases:
- Manage work area or assembly line—real-time feedback, identify bottlenecks, performance reports by shift/day, remote visibility-ideal for managing off shifts
- Perform long duration time studies—data-driven Kaizen setup/changeover analysis, run/analyzed over weeks, compare across time and facilities, store metrics for Kaizen, perform SMED analysis in large areas
- Identify missed inspection steps with 360 degree analysis—rapidly identify root cause of defects, search for video clips associated with product assembly
- Achieve healthier, safer, well trained workforce—capture near misses and best practice violations, capture the impact of fatigue by measuring throughput at beginning and end of shift, capture and share the best practices for training
- Build realistic engineering standards—capture data for the entire shift or multiple shifts before creating a standard to be enforced
