were created to instruct the operators to
“wash the board” – pull the board after the
solder paste application, wash off the paste,
and try again. Sequential failures were sent
to quality inspectors.
This manufacturer doesn’t share the financial impact, but the line is now profitable, and it continues with that business.
Creating an Analytic Culture
Here is how to increase your team’s comfort level with data- driven analytics:
• Challenge the team to get out of their comfort zone. Engineering cultures will feel
awkward at first integrating a data-driven approach, but engineers respond well
to results and learn fast. One successful
project will invigorate the entire organization. Think beyond first principles and
follow the data. You might be amazed at
what you find.
• Start with a valuable problem. It’s worth
repeating and is a rule to live by. Go find
a sponsor! With an issue! Risk-taking
business leaders with P&L responsibility
are best. They will tell their peers of successes and lead the rest of the organization by example.
• Treat your data as a highly valuable asset,
especially in-situ data. Collect it, store it
in accessible and analytic-ready stores,
and leave it unstructured. If you think
you have a great data source in your data
warehouse, be prepared for some work.
Chances are it’s been structured, aggre-
gated, and cleansed to the point of lim-
ited value. Investigate source systems
like historians, line side equipment, and
SCADA/DCS systems. Usually you have
more data than you think; it just might be
hard to get to. A common data integra-
tion tool is the USB drive. Assume you
will have to contextualize the in-situ data
with other data sources like MES and
• Invest in your people. Staff data-driven
teams appropriately. A mix of domain
knowledge from line engineers, master
black belt process experts, data processers from IT, data-driven quantitative
analyst/statistician/data scientist and
line of business make a good core team
to get things done. Remember, data science is the skill you probably need to invest in the most but it’s not the answer
by itself. Consider a partner strategy to
bridge the gap while you build up data
• Establish a process. Because you want
your data-driven teams to crank out
work on a repeatable, reliable process,
think of building an analytics assembly
line. Follow the process and invest in an
integrated analytics platform. Don’t
build every analysis by hand – you don’t
build your products that way, why build
your analytics that way?
Finally, always think about deployment
from the start. The best insights provided
by your analytic assembly line won’t provide any value unless the insights are deployed in the real world. Continue to drive
for automation. Manual insights deployed
by humans over time are a great start. Automated real-time execution of analytic-driven standard operating procedures gets
to the value quicker and more consistently
than any other mechanism.
If manufacturers can acknowledge
their discomfort in adopting data-driven
analytic approaches, get beyond the resistance, and set up their teams for success,
their investments in M4.0 infrastructure
will yield real results – and beyond. It’s
time to dive in. M
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every analysis by hand.
ucts that way.