“Collaborating with the client in the electrical engineering
industry was a great opportunity for KFactory to show-
case our expertise and provide them with a solution that
addressed their need for real-time monitoring and data
automation. By partnering with us, the client was able to
improve their overall activity, and by being an early adopter
of technology, they have gained an edge over their competi-
tors.” Vlad Cazan, co-founder and Sales Lead at KFactory
It was kept in mind that, due to
the large number of product
categories, there were no pictures
of all products; thus, the images
captured in real time are compared
to the known product database.
If the products are not detected,
the line supervisor is notified,
who can identify the product code
linked with the unknown product
photos with the help of a local
tablet application. Then, using
the platform’s sophisticated
infrastructure in Microsoft Azure
and algorithms, we retrain the
new model to recognize the newly
added product categories in
minutes.
The model is employed
immediately in production, which
means that the time between
detecting an unknown product
and starting to recognize it
automatically is only a maximum of
30 minutes.
The local application allows
operators to logon and classify
actual defects, eliminating all
paperwork required for quality
monitoring and keeping track of
actions per employee.
The Results
The system has been successfully
implemented inside the client
organization, with all roles, from
operators to supervisors and
managers, seamlessly using it.
The degree of automatic product
recognition is more than 99%,
which is an outstanding result.
The managers have complete
visibility over the process: reports
are sent automatically after each
shift, and the business analytics
platform is fed daily with new data,
updating KPIs and breaking down
daily activities, improving overall
efficiency and productivity.
New product categories are
added daily, creating an image
database that is becoming more
precise every moment.
Due to automation, manual data
collection is reduced to zero, and
potential errors are eliminated.
Conclusion
This is a successful case
involving the use of Artificial
Intelligence in manufacturing.
The quick feedback loop built by
KFactory is a plus, making it one
of the few software platforms
in the worldwide market that is
introducing and teaching new
categories in near real-time,
expanding the platform’s value
to enterprises with short-series
manufacturing and manual
assembly procedures.
By partnering with KFactory, the
Client successfully improved its
overall activity, and by proving
itself as an early adopter of
technology, it gained market
leverage over its competitors.
Finally, this case study highlights the
power of computer vision in tackling
manufacturing-related challenges.
Companies can increase their
productivity and remain competitive
in today’s fast-changing industrial
world by embracing cutting-edge
technologies.
KFactory I 2