Rapid Insights and Operational Intelligence for the shop-floor
By
Gopal GopalKrishnan, P.E., Solution Architect, Partners and Strategic Alliances, OSIsoft,
LLC. Member, MESA International Technical and Education Committee
Not all big-data is hype. Self-service BI with its slice
and dice user experience came along just as big-data was gaining
mindshare. And, when targeted to specific use cases, self-service BI with
data analytics can yield rapid insights – big data or not.
This is Part 2 of a blog post series
describing the use of self-service BI and data analytics from real-life scenarios
in MOM
(manufacturing operations management).
This use case comes from one of Georgia Pacific’s (GP) paper
machine operation.
To read Part 1 click here: http://blog.mesa.org/2015/02/what-if-expedia-showed-your.html#links.
To read Part 1 click here: http://blog.mesa.org/2015/02/what-if-expedia-showed-your.html#links.
If you are not familiar with how paper is made, please see this interactive video
tour. The data and analysis described below is for
the “Papermaking” step in the video tour.
Here, several chemicals are added to the feed stock to influence its properties
such as whiteness, strength, and others.
Variability in Chemicals Consumed
The quantity of chemicals consumed for each product grade run varied considerably. Business was keen to get a quantitative understanding of this variability – sliced and shown by product SKU, by grade run, by feed-stock, by day, by month, by shift, and so on.
A data infrastructure for MOM helps
capture time-series sensor data such as flows, temperatures, pressures, machine
speed, etc. Then you can shape them with the correct context with relational
data such as product SKUs, recipes (feed-stock blend percent for virgin pulp,
brown stock, etc.), and quality targets (basis weight aka paper density, whiteness,
strength, etc.). And then you are ready
for the slice and dice user experience in the self-service BI tools.
The drill-down helps to hone in on the answers for questions such as:
•
Did I follow the recipe? Show me by grade…
•
How often, how much, and when do I deviate from recipe?
•
How does brown stock in the feed affect whiteness and strength?
•
How does refiner energy consumed affect the strength?
•
When and where did I use the least amount of chemicals?
•
What and when is my best production run?
Rapid Insight
The visuals with aggregated values offer
a bird’s eye view of all the production data for multiple years and across
multiple production lines and SKUs, and across a variety of feeds and quality
targets – and you can methodically drill down to specifics with an Expedia-like user
experience mentioned in Part 1.
The GP video is in this rapid insights
using data analytics talk. The chemical
usage variability will be of interest to every paper mill manager. If you are in a different industry, the take-away
from this use case is how the data infrastructure and the self-service analytics
tools enable rapid insights for operational intelligence.
###
Gopal
GopalKrishnan,
P.E. has been involved in several roles at OSIsoft and has been working with the PI
System since the mid-1990s, in software development, technical and sales
support and field services. Attached to the Philadelphia office, he is
currently a Solution Architect in the Partners & Strategic Alliances
Group. Previously, he was a Product Manager with a focus on Enterprise
and Asset Integration and PI System data access.
Gopal has a Master's in engineering, continuing education in business administration and is a registered professional engineer in Pennsylvania. He is also active in the MESA Technical Committee and the Education Committee and the MESA Continuous Process Industry Special Interest Group and active in topics such as data mining, energy efficiency, manufacturing intelligence, sustainability, including green and sustainability initiatives in facilities and data centers.
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