High Complexity is the Biggest Threat to a Modern Business. We Measure and Manage Complexity. Since 2005.
Excessive Complexity = Risk
As the 2008 crisis has shown, conventional means of risk assessment, management and rating are not suited for a complex and turbulent economy. They are subjective and produce results that can be manipulated. There is a pressing need to devise more modern, objective and science-based means of dealing with uncertainty and complexity. This is exactly what we do and what we offer.
Quantitative Complexity Management (QCM) is not just about a new and advanced means of dealing with risk in a turbulent economy. It is also a new platform on which to unify corporate strategy and risk management, breaking with the old paradigms that have contributed so eloquently to the 2008 planetary economy meltdown.
Science, not Opinions
While it is widely recognized that uncontrolled growth of complexity is an impediment towards sustainable development, there is a proliferation of the so-called metrics and 'definitions' of complexity that have little to do with serious science. Many of the definitions often refer to complexity as a 'twilight zone between chaos and order'. Clearly, such definitions do not lend themselves to any practical use.
A serious metric must be bounded. If a metric is to respect the laws of physics, it cannot, for example, assume infinite values. Complexity is a function which links the functionality of a system with entropy, a measure of disorder as well as of information. Complexity is not about counting products, parts or interconnections between components. Moreover, a serious mertic has units. We measure complexity in 'complexity bits', or cbits. What about the others?
Complexity is a fundamental property of every system, just like energy. It can be measured, hence it can be managed. It is not a process. It is not about self-organization or swarms of birds. It is not order on the edge of chaos. Complexity is a physical dimension, an attribute of all systems, natural and man-made.
Science has made the greatest leaps forward when scientists were able to generalize and to perform synthesis. Speaking of 'complex systems' does science little good as it goes in the direction of fragmentation. In Nature there exist systems and processes. Some are highly complex, some less. Some have high energy, some don't. There is no such thing as 'complex systems'.
The complexity metric by Ontonix is applicable to all systems. The metric is universal. It is natural. This is why we can measure the complexity of a Balance Sheet, a traffic system, an energy distribution grid, an assembly line, or a hospitalized patient, taking into account thousands of variables and all the interactions between them.
Solutions For Turbulent Times
In a world dominated by turbulence and inter-dependency, fragility and complexity are the main new factors which are impacting the global economy and driving financial performance. While the unprecedented challenges affecting the global economy are a source of both opportunities and threats, traditional Business Intelligence technology is unable to capture the new drivers of value creation. The ´New Normal´ of a more uncertain world requires a different kind of approach and a new set of analytical tools.
Conventional Business Intelligence techniques and tools have been devised in a world that no longer exists. Limited by linear thinking and distorted by visions of efficient markets which are in equilibrium, traditional analytics is removed from reality. The state of the global economy is eloquent proof.
Our solutions have been crafted specifically for a turbulent and global economy, in which nothing stays in equilibrium and where things change with the speed of the internet. Every day.
Quantitaive Complexity Management, when applied to a business, becomes an efficient tool for:
- understanding better business dynamics
- pinpointing concentrations of fragility and vulnerability
- reducing the impact of inefficiencies
- improving profitability
- delivering a holistic "CAT-scan" of a business
- extracting new knowledge from data
But Quantitative Complexity Management establishes also the grounds for a new and modern means of managing, assessing and rating risk. The new science is based on model-free methods, which allow us to concentrate on solving real problems not on building exotic mathematical constructs.
QCM goes beyond just risk. It is also about a modern approach to corporate strategy, to decision-making and management. QCM is a therapy. it is a new lifestyle. It is a new way to run a business.
Complexity X Uncertainty = Fragility™
The above equation is the Principle of Fragility, which has been coined by Ontonix in 2005. It explains why in an uncertain environment, such as the global economy, a highly sophisticated and complex business model is more exposed, hence less sustainable. As the uncertainty and turbulence of the economy increase, simpler businesses are preferable as they are more resilient.
Over the past few decades new technologies have been accelerating the growth of complexity to levels which are threatening not just the sustainability of our global society but the governability of its critical infrastructures. This is because one cannot design a highly complex system without taking complexity into account!
In order to counter the negative effects of rapid growth of complexity we first need to perform an in-depth analysis of your business or process. To that end we process all data - manufacturing, sales, financials, etc. - which corporations already have and store in their ERP systems. We resort to supercomputers to process hundreds of thousands of variables to deliver a truly systemic and holistic reflection of a business and of its state of health. This requires new technology, beyond statistics, neural nets, cluster analysis, Bayesian methods or Monte Carlo Simulation. Our tools are based on a radically innovative model-free approach which allows us to solve problems that are beyond the reach of traditional mathematics.
Serious Science Starts When You Begin To Measure
Examples of Complexity Maps, which reveal the structure of complexity and its key contributors.