CONTROL & AUTOMATION
WAREHOUSE ROBOTS
INSPEKTO
IMPROVING OPERATIONAL EFFICIENCY
Traditional machine vision solutions wreak
havoc on operational efficiency and the
industry is ready for change. As the late
Japanese industrial engineer Shigeo Shingo
once said, “Improvement usually means
doing something that we have never done
before." Here Miki Gotlieb, VP of Operations
at Inspekto, explains how Autonomous
Machine Vision impacts operational
efficiency in a way that has never been seen
before.
Traditional machine vision solutions, which
have been around since the 1980s, are
implemented in a project-based approach.
The process requires the selection and
acquisition of numerous components,
including cameras, lenses, lighting, filters,
computing platform and software from
a range of suppliers. The QA manager is
therefore subject to the lead times of the
vendor of each piece of equipment — while
the resulting overall lead time is governed by
the long lead item (LLI).
Once the integrator receives the
components, they can then integrate them
with one another, alongside the tedious task
of preparing the software selected and then
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building a hard-engineered solution on the
shop floor, before beginning the software
training process.
For a simple application, implementation
can take several weeks to a few months. For
a more complex solution, typical time frames
are several months and, in some cases, go up
to an entire year.
In addition to these lengthy waits, since
traditional machine vision solutions are
specifically designed for a location on the
production line, it is nearly impossible to
move the solution from one location to
another. Alongside this, the diagnostic tools
for a traditional machine vision solution are
often unique too, which means they are an
expensive investment.
In most cases the manufacturer is unable to
diagnose a fault themselves, due to a lack of
machine-vision specific knowledge and must
instead rely on the integrator for assistance.
This inability to address the issue
immediately in-house delays manufacturing,
which may have to be halted while the fault
is addressed.
One way to reduce the downtime should a
machine vision solution become faulty is to
stockpile spare components.
A factory with several system configurations
will keep spares of all unique system parts
in order to achieve a decent mean time to
repair (MTTR) and minimise the downtime in
case of a malfunction.
This increases costs and effort for the parts
which must be maintained and managed.