There is a lot of buzz about the topic of data visualization
in the manufacturing industry. Much of the discussion is centered around the
necessity of data and the ability to visualize that data in a meaningful way to
achieve “Smart Manufacturing.”
But what is Data Visualization?
To some, it is a technology. Software that enables
a person to turn copious amount of data into a graphical representation with
the premise that it will improve comprehension of a point of view.
To others, it is an art form. Using artistic talent
to represent data.
Yet to some, it is a process that one follows to gather
and analyze data to inform decision-making.
To me, it is the “Ah Ha” moment, the confluence of
all of the above. That moment when the data has been mined, the story has been
built, the graphical and pictorial representation has been created, and the exercise
has revealed an answer or outcome you could not reach before.
So, data visualization is key to helping manufacturers
to use data and analytics in smart decision-making. Right?
Yes, but here’s the problem: the house of cards
hinges on an important concept before anyone can begin to visualize data… context.
Visualizing data, or presenting it in a pictorial or graphical format, without
context and analysis provides marginal value. It generally provides a one
dimensional view without the complete picture.
To better utilize the art, craft and process of data
visualization we need to start by setting the scene and determining what
question we are trying to answer, or better yet what story we are trying to
tell. If you simply ask, “which of my manufacturing lines is performing best?”
there are many ways you can answer that question with different data. Is speed
the best measure of performance? Cost? Accuracy? The answer is none of the
above. To truly measure performance and efficiency you need to analyze all
available data, with context, together.
Building context is extremely important for
downstream clarity. To build effective context, you need:
·
a way to collect multi-dimensional
data, not just data streams coming off individual tools or sensors.
·
a means to determine when is the
right time to collect the data validation and a place to store that data and
provide early visibility.
·
visibility that enables you to act
upon that data and alter your approach if needed.
To think about the problem from another
perspective, let’s look at the life cycle of data – from the moment it is
produced to the moment it is “realized.” Where does visualization fit in? How do
tools and technology assist? Where do we build context?
In the early stages data is produced. Decisions are
made about what data to collect, i.e. temperature, humidity levels, power draw,
up-time, down-time, etc. At the point of collection, we have the opportunity to
provide additional context. What else was happening in the environment when the
data was collected? Who was running the machine, what recipes of batches were
being made? Now, this collected data starts its transformation into actionable
business intelligence.
As we are collecting data, we have the opportunity
to look out for other unique events – are we picking up fluctuations in
operating parameters, what is happening with other machines running the same
recipes or batches, what is going on outside the plant like an abrupt change in
the weather? These unique events provide us yet another level of dimensionality
to our data.
Now we store the data and begin to analyze. What
trends are we seeing, what anomalies are beginning to surface? We start to
prove or refine our ideas or hypotheses. We test our assumptions and validate
our approach.
Do we need to make changes? If so, let’s adapt our
platform or approach and incorporate those changes quickly and efficiently,
with minimal impact to operations. Let’s begin the process again, executing the
new plan – make the feedback, analysis, and action loop continuous.
Where does contextualization fit in? It is in every stage of this life cycle -
helping ensure we have the right data. Setting up visualization enables
decision makers to see analysis so they identify new patterns, drill down into
charts and graphs for more detail and interactively analyze variable data. When
used this way, data visualization can inform almost any decision related to the
manufacturing process, from inventory management to maintenance scheduling to
human resource allocation.
Together, effective data visualization, and a Smart
Manufacturing technology platform can get the right data, in the right format,
in the right hands, at the right time, to take the right actions.
