Manufacturing Today - May/June 2017 - 15
SUNDEEP SANGHAVI | COLUMN BY
CEO Sundeep Sanghavi co-founded DataRPM with the goal of
providing a platform that delivers hyper-fast cognitive data products to organizations challenged by the volume, velocity and variety
of their big data and machine learning. Learn more at DataRPM.
com. Sanghavi can be reached at email@example.com.
MAY/JUNE 2017 manufacturing-today.com
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The fact is that, even with factory
floors - as well as vehicle fleets - being filled with sensors, questions still
need to be asked. Even in the age of
Industrial Internet of Things (IIoT)
- that is, factory equipment that can
communicate with sensors and computers - this does not render human
overseers obsolete. Not yet at least.
IIoT can and will be used to speed
up factory processes, making production more manageable and more
But, of course, while sensors can help
humans interact with machines, the
rise of cognitive predictive (and preventative) maintenance and the need
to have a workforce ask the Five Whys
will also come to pass.
New machines with their thinking
capabilities can employ this interrogation technique across thousands of
factories and millions of sensors. This
is, of course, not possible with a human workforce.
Machines can also come up with
an answer that requires brief and
Machines are an aid,
not a replacement
The future marches on
M F G TOMORROW
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scalable. This also means finding the
solutions to problems that cause machines to breakdown or otherwise fail
in their primary function.
However, while the impact of this
speed cannot be denied, the machines will require human workers
to at least ask the Five Whys when
considering any problem. The sensors may detect and report faults,
but they will not be asking their own
questions. Rather, the humans on the
factory floor will be able to ask machines this vital series of questions,
and will be able to expect faster responses that more accurately report
what is causing problems.
A LFAB INC.
B UY AMERICAN
ly developed in the 1950s, a century after Toyoda's death - can be employed
alongside data to stop and prevent
the prevalence of recalls. Sadly, this is
yet to be properly implemented. Just
last year companies like Samsung and
Takata, an airbag manufacturer, were
caught up in high-profile recalls that
led to injuries and deaths.
Getting to the bottom of recalls
requires a lot of pertinent question
asking. Effective questions, asked -
and answered - quickly, can restore
consumer trust in a brand that has
just had to issue a very serious warning about a product that it has sold. It
can, in the case of recalls like Takata,
non-technical approval from a human supervisor, so that maintenance
teams know what questions are being asked to reach the end conclusion. For example, General Electric
has hundreds of factories across the
globe and for the company to be able
to ask all the questions it needs to
ask, there's just no physical way that
it could have human teams achieve
this. Now machines can do that for
GE, providing a brief and actionable
solutions in the process.
There are, of course, some limitations to the Five Whys; no system is
perfect, nothing is flawless. In using
this system, it can, at times, be difficult to distinguish between causal factors and root causes. At times, there
is also a lack of rigor in deducting,
where advocates of the system are not
required - and subsequently do not -
test for the sufficiency of root causes
generated by this method of industrial soul searching. The trick to solving
this problem, and ensuring the Five
Whys remains relevant, is to keep it
grounded in observation, not deduction. This, combined with skillful use
of the system, will keep the method a
viable one to aid technology now and
in the future.
As sensor use continues to grow
and proliferate, the benefits of machines using the Five Whys will be felt
across the industry the whole world
over. Sensors and factory equipment
will combine over the IIoT, making
advanced warnings and predictive
maintenance better and more advanced. This will result in safer factories, and more protected workforces.
Here's to asking why. mt