MAINTENANCE OCTOBER 2019
BLINDED
BY THE LIGHT
Manufacturers can easily fall into the
trap of assuming a sea of green lights on
the shopfl oor means everything is OK.
However, harnessing data means even
a green light can be improved upon
HBY CHRIS BECK
ow often do you fi nd products being
delayed coming out of production
because of a quality issue? And while
you’re focused on eliminating those
defects, your throughput falls away.
It’s a diffi cult balancing act, and one
that’s important to get right – nobody wants a
reputation for low-quality, late products hanging
over them.
As a result, error-proofi ng has become a vital
part of the production process. Increasingly,
data is becoming more and more important in
allowing manufacturers to predict where errors
will occur and combat them before they cause
disruption to the shopfl oor.
Swedish engineering giant Atlas Copco will be
a familiar name to many, through its multitude
of divisions. The recently restructured Industrial
Technique division in the UK saw a merger of
the company’s Tools and Industrial Assembly
Solutions business centres.
General manager, James McAllister, tells
MM how the company is aligning itself at the
forefront of the quality management space.
“We’re in a world undergoing constant change
that is being driven by innovation,” he says.
“Whether you’re making cars, aircraft or a whole
host of other products, the challenges facing
manufacturers are always the same: produce
high-quality products at a lower cost, but now
with an added element of customisation for
the customer. Production systems themselves
are becoming more complex with the rise of
Industry 4.0 and automation. And in the middle
of all that, operators still have to interact with
the systems and technology on the shopfl oor.”
Understanding needs
Atlas Copco has developed a number of tools
to identify strategies for improving quality,
depending on the requirements of the factory in
question. Each factory, it says, will have varying
needs, depending on factors including size,
output and fl exibility:
BASIC NEEDS
Manufacture between 100
production units a day;
Rarely make changes to the
production line;
Batch build;
Low regulatory requirements;
Limited use of temporary
staff and/or apprentices.
COMPLEX NEEDS
Manufacture between 100-
500 production units a day;
Annual/bi-annual changes to
the production line;
Build to sequence;
Moderate regulatory
requirements;
Some use of temporary staff
and/or apprentices;
Low to medium screw/bolt
count per unit;
Starting to adopt Lean
Manufacturing.
VERY COMPLEX NEEDS
Manufacture over 500
production units a day;
Regular monthly/quarterly
changes to the production line;
Build to order;
High regulatory
requirements;
High use of temporary staff
and/or apprentices;
High screw/bolt count per
unit;
Lean Manufacturing is an
integral part of the production
process;
Elements of assembly are
safety critical;
Production KPIs are
measured and audited;
xxxxx
Quality KPIs are measured
and audited.
These needs change as a site
evolves – a manufacturer with
Basic Needs today may have
more Complex Needs two years
from now. The quality of an
assembled product is aff ected
by several factors, including
operator infl uence, use of
incorrect fasteners, missing
components, omitted fi xings
and using the wrong parts.
Atlas Copco has identifi ed
seven steps that manufacturers
can take towards securing
product quality in assembly
(see chart below right). These
steps build on one another and,
depending on where you sit on
the list of needs, will see you
join at diff erent points.
Atlas Copco has solutions
available for companies at
every stage of the journey,
says McAllister. “We want
to be involved at every stage
of the production cycle and
provide traceable, error-proof
solutions. As we move towards
Industry 4.0 we have a number
of diff erent initiatives around
collaborative robots and
reaction-free tooling but with
the addition of usable data.”
Choosing the right data
This data is increasingly vital
for manufacturers. However,
warns Michael Pritchard, Atlas
Copco’s technical manager, it’s
important not to be blinded by
assuming all data is the best it
can be. “Everyone is capable of
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