VISENSE is universally applicable and is not limited to a particular industry
By diagnosing mechanical observable machine errors and digitalizing the processes surrounding incident root cause analysis, VISENSE enables different industries to cut the time needed to identify and resolve errors by up to 50%.
Widespread pain points on shop floors
Today, reducing unplanned machine downtime is still a difficult process, resulting in months of inefficient machine operation. Therefore, the time spent from identifying to resolving machine errors is critical. Delays are driven by three major factors:
NO DEDICATED PROCEDURE
Troubleshooting today is still largely a very manual process that is performed by machine experts.
There is a lack of standardized, digitized tools and processes to resolve machine error efficiently.
TIME-CONSUMING
Machine observation is very time-consuming and requires expensive well-trained employees.
LACK OF DATA
Manufacturers have no sufficient data basis that can be used for root cause analysis, turning it into a guessing game.
Machine error messages are too generic
No way to see errors happening without standing next to the machine
What is the optimal
use-case for VISENSE?
Problematic Machines
Machine with a low availability
Machine Ramp-ups
Commissioning of new or newly calibrated machines
Machine Requirments
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Discrete manufacturing
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(Visually) observable processes
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(Partially) automated processes
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Serial production
Ideal Requirments
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Medium to high production volumes
Impact of VISENSE
We offer digitalization and support in error root cause identification, ramp-up processes, information exchange, learnings capturing, detailed machine monitoring, and error resolution.
9'900
hours of capacity freed up among operators, maintenance and planners per year.
1/2
man-year saved during ramp-up phase of a new or re-calibrated machine.
50%
reduction of time spent on error type and root cause identification.
international travels of machine experts saved per year.
50%
reduction of time needed for reporting through automation.