Business Dynamics in KPI Space. Some thoughts on how business analytics can benefit from using principles of classical physics
Alex Ushveridze

TL;DR
This paper proposes integrating principles from classical physics into business analytics by classifying KPIs into volatile and controllable groups, enabling deterministic modeling of controllable KPIs for improved strategic decision-making.
Contribution
It introduces a novel approach to business analytics by applying deterministic physics principles to controllable KPIs, enhancing predictive and operational capabilities.
Findings
Deterministic modeling of controllable KPIs is feasible.
Physics-inspired methods can improve business performance analysis.
Potential for new risk and performance indicators based on dynamical invariants.
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…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
