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Featured Research
Using
Lakes to Monitor the Health of the Planet
Using Sensors and Cross-Disciplinary
Teams to Understand Complex Ecosystems Quickly
By Patricia B. Seybold, August 7, 2008
NETTING IT OUT
How
can you create a virtual, yet real-world model of your business? How can
you monitor all the inputs and variables in a complex end-to-end process?
Whether your business involves running airlines, keeping supermarket shelves
stocked, providing cost-effective insurance, delivering electric power,
or manufacturing and selling apparel, perhaps there’s a new way to
think about monitoring and modeling your business and its ecosystem.
This case study
highlights some current best practices in environmental engineering—practices
that may also be useful to forward-thinking business strategists. The best
practices are:
1. Use multiple,
distributed sensors to capture real-time feeds about physical phenomena.
2. Build a
virtual model based on these real-time data feeds to understand what’s
going on and how things interrelate.
3. Use metadata
frameworks that others are using to model similar phenomena so that you
can compare and detect patterns.
4. Instrument
quickly. Don’t spend years; spend days.
5. Build cross-disciplinary,
cross-cultural teams to work on high-learning, high-performance, time-bounded
projects.
We have yet to find anyone who is modeling their business in this way, but
it seems logical that if scientists and engineers can sense, detect, model,
and control real-world ecosystems, we should be able to do the same for
our business ecosystems—most of which are made up of physical people
doing physical processes.
Sensors
in Use for Environmental Studies
“Illustration
of a multi-scale sensing system. Multi-scale sensing systems can share
data among different platforms for efficient use of sensing resources.
For example, low resolution images mounted on a robot moving between
trees can communicate measurements to static or mobile sensors below
to identify areas requiring higher resolution measurements. Illustration:
J. Fisher, UC Merced.” Figure 2.5, page 17 published in 2007 in a CENS
white paper entitled: Distributed Sensing Systems for Water Quality
Assessment and Management.
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