COMPUTER
VISION USED
TO IMPROVE
EFFICIENCY
OF MANUAL
ASSEMBLY
LINES
Case study
“By implementing a system that can precisely identify and
track every product without impacting operator work, we were
able to overcome the challenges of no real-time visibility and
no centralized data on efficiency. Real-time image capture and
automated classification, coupled with backend automation
and data consolidation, have allowed us to create a secure,
reliable, and scalable solution that has drastically improved
our production and quality processes.”
Adrian Dima, Co-founder and Technical Lead at KFactory
The client wanted to improve the
speed and quality control of their
manual assembly lines, so they
looked for a solution that would
let them monitor the production
in real time and automate data
collection and reporting.
Challenges
The customer has thousands
of product categories, some
of which differ just by color or
the brand printed on them. The
production is done in small
batches that are repeated at
regular intervals of weeks or
months.
Data on production and quality
was collected on paper, then on
Excel, with reports arriving in
the ERP system with delays and
potential inaccuracies
There was no centralized and
reliable data on efficiency, and
using the in-hand procedure
yielded no findings. Defects were
often hand-noted on paper.
Overall, there was no real-time
visibility into the performance
and quality processes of the
assembly lines, which inhibited
decision-making and limited the
optimization potential.
The Solution
KFactory created a system
that can precisely identify and
track every product as it is
manufactured while having no
impact on actual operator work.
A cutting-edge computer vision
model that employs cameras
strategically positioned along
the line to capture real-time
images of the products was
successfully developed.
The backend infrastructure
is Microsoft Azure, which
provides the security and power
required to support a real-time
manufacturing process such as
this one.
IMPROVING MANUAL
ASSEMBLY LINES EFFICIENCY
AND QUALITY CONTROL
WITH COMPUTER VISION
The customer is a successful German business operating
in the electrical engineering industry, specializing in
the creation and distribution of aesthetic and functional
connection points, connector strips, power distribution
options, and high-performance power distribution units
for various applications.
1 I KFactory
“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
Regus - Bucharest, Sun
Business Centre, 391 Vacaresti
Street, 3rd floor, Bucharest,
Romania 040055
T. +40 374 460 028
E. office@kfactory.eu
www.kfactory.eu