Business Dynamics in KPI Space. Some thoughts on how business analytics can benefit from using principles of classical physics
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2017
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Abstract
The biggest problem with the methods of machine learning used today in
business analytics is that they do not generalize well and often fail when
applied to new data. One of the possible approaches to this problem is to
enrich these methods (which are almost exclusively based on statistical
algorithms) with some intrinsically deterministic add-ons borrowed from
theoretical physics. The idea proposed in this note is to divide the set of Key
Performance Indicators (KPIs) characterizing an individual business into the
following two distinct groups: 1) highly volatile KPIs mostly determined by
external factors and thus poorly controllable by a business, and 2) relatively
stable KPIs identified and controlled by a business itself. It looks like,
whereas the dynamics of the first group can, as before, be studied using
statistical methods, for studying and optimizing the dynamics of the second
group it is better to use deterministic principles similar to the Principle of
Least Action of classical mechanics. Such approach opens a whole bunch of new
interesting opportunities in business analytics, with numerous practical
applications including diverse aspects of operational and strategic planning,
change management, ROI optimization, etc. Uncovering and utilizing dynamical
laws of the controllable KPIs would also allow one to use dynamical invariants
of business as the most natural sets of risk and performance indicators, and
facilitate business growth by using effects of parametric resonance with
natural business cycles.
| Reference Key |
ushveridze2017business
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|---|---|
| Authors | Alex Ushveridze |
| Journal | arXiv |
| Year | 2017 |
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