4 Ways Data is Transforming
Data Rich, Information Poor
More than ever, the manufacturing industry is challenged with corralling and
understanding massive amounts of data to drive operational efficiencies,
higher levels of service, and support.
Like it or not, manufacturing is moving faster, and the good-enough status
quo is fading as old business systems no longer supply decision-makers with
“There’s a lot of resistance coming from
manufacturing. We resist change because
we don’t know what we don’t know. So
transforming the mess of data that we
routinely collect into a useful asset really
has to be a process. It has to be a very
very focused and considered process.”
Dan Meier, operations manager at Photoronics, Inc.
In today’s marketplace, exploring the impact and interplay across production
efficiency, product quality, customer demand, and service excellence simply isn't
possible without meaningful analytics.
Here are four ways leading manufacturers are
revolutionizing their industry with data:
2. Enhancing Sales
1. Improving Production, Plant Performance
and Product with Self-Service
People within manufacturing have traditionally accessed data insights via static reports
from enterprise applications and business intelligence tools, all managed and used only
by the IT department. This old way, predominantly designed and built in the 1990s, is
generally complex, inflexible, and time-consuming.
Because the best analytics implementations are user-created dashboards running on
top of IT-managed infrastructure, optimization for self-service is key.
Self-service analytics will empower individual manufacturing employees and
entire organizations alike to see and understand data across the demand chain,
within production operations and throughout the entire service life cycle.
With added visibility into operational performance, employees will be able to
monitor data throughout the entire organization and apply it to strive for
continuous business and process improvements via the philosophies of six
sigma or lean principles.
Self-service also supports the implementation of the DMAIC framework to
support a data-driven improvement cycle allowing an individual to explore and
identify the root cause of product defects or bottlenecks.
Tesla Motors Designs, a major manufacturer of electric vehicles and powertrain
components, found that self-service analytics empowered its employees to
explore their own data—and contributed greatly to their discoveries about
production improvement and stabilization.
“Once you start giving access to data to people, they start asking more
questions. And there’s the ability to kind of dig deeper. If you're trying to root-
cause some nagging issue that's been kind of hurting production for weeks or
months, looking into that data and seeing things we've never seen before has
been a big win.” Will Bishop, senior test engineer at Tesla.
This dashboard is an example of process
and production analysis where users can
explore the performance of a few dozen
orders filled by a manufacturer using two
Users can browse through the orders
that were filled on the left and see how
well the production actually ran by
bringing together several data sets on a
single dashboard. The bullet graph depicts
the major metrics that affect production
(setup, downtime, run speed). Note how
the differences in variance trend between
the two machines. Why is Machine 123
running better than Machine 456?
Click the dashboard to interact with this
visualization and find out.
This manufacturing dashboard lets users
choose a facility by clicking on the map to
see detailed defect information for that
facility in the top right view.
In the lower right view you can see
production (bars) and defects (circles) at
that facility over time. Notice how high
defect counts generally lead to lower
production levels in the days immediately
following the defect. Click the dashboard
to interact with this visualization and learn
2. Enhancing Sales and Operations Planning
with Data Blending and Forecasting
Big improvements in manufacturing must start from the source—the supply chain.
It’s critical that every supply chain professional be able to deliver goods
and services using disparate information systems on tight deadlines.
Manufacturers today are faced with many sources of data: workforce and order planning
from the ERP system, order information from MES systems, time and attendance logs,
alarm and production data from different equipment manufacturers, and various PLC
and SCADA systems. Linking these islands of information is key for understanding the
big picture and decision-making.
In this visualization, a forecasting model
can expose critical problems and
opportunities with an easy-to-use
calculation window. Users can choose
various modeling methods such as
“Aggressive”, “Deterministic”, or “Cost
Optimization.” These are bespoke
calculations and can be built by your
organization, allowing for flexibility to
model the specifics of your business.
3. Mobilizing Supply Chain with
Even more revolutionary in supply chain operations is the ability to see and understand
what is happening with data in real-time, and from a mobile device.
Manufacturing data is constantly changing but immediately relevant. Using data at the
right time is vital to a more profitable operation.
The good news?
Mobile business intelligence is finally providing information when and where it’s needed
to make fast, business-critical decisions.
The Coca-Cola Bottling Co. Consolidated (CCBCC), the largest independent
Coca-Cola bottling firm in the U.S., has workers on the ground interacting and
collaborating with visual dashboards from anywhere—even truck drivers in
The CCBCC solved a huge bottleneck in their supply chain, due to limited
report availability, by providing leaders and more than 800 employees with daily
dashboard updates on mobile devices.
This daily Field Operations Dashboard is
primarily used by truck drivers making the
actual case and product deliveries.
Truck drivers need to know where the
most profitable, most efficient deliveries
can be made, and not spend days and
hours solving that puzzle.
This dashboard is broken down into
various areas of need such as invoices,
delivery performance, and timelines for
product delivery. It also has the ability to
drill down into underlying data to explore
specific information in territories and
“With the ability to be mobile, now
delivery workers have the option to be
on the truck, looking at their iPad,
understanding how they’re doing when
they are driving to a certain location or
route they are on.”
Kevin King, director of reporting and analytics at CCBCC
4. Listening, Interpreting and Reacting to
Customer Feedback Faster
At the end of the day, the desires and needs of the customer matter. Manufacturers
need to collect customer data by listening to many different channels such as social
media, call centers and customer surveys.
When finding insights from customer information, speed to action is fundamental.
Trane, a global leader in air conditioning systems and equipment, made the shift
from exclusively using spreadsheets to integrating self-service data
visualizations with customer service data to significantly improve their speed to
insight. The turn around times from data insights to customer happiness are
now 10 to 100 faster.
“We need to listen, and we need to interpret that data. And we want to quickly
react in two ways. We want to react directly by responding to our customers.
We also want to react as a business and strategically determine what is
important to our customers.” Michael Nealy, senior customer analyst from
With data visualization, Trane tracks key metrics to score customer feedback
faster. They’re able to dig into their data to find answers, for example, do
technicians arrive on time in a certain region?
Sales employees, manufacturing employees and executives can all see very
quickly the top and bottom performing districts, as well as drill down to the
underlying data to find out why.
“We’re able to identify opportunities to figure out which of those sales offices
and districts could do better. And our customers are having a better experience
as a result,” Nealy said.
Integrating data visualization into your manufacturing systems and processes is
easier than you think.
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