In part 1 we explained why small and medium businesses or SMEs needed risk analytics. In part 2 we described a few common risk related issues that affect SMEs. In this concluding part, we will describe a very basic scenario that relates to the twin problems of capacity utilization and demand uncertainty.

## Affordable Risk Analytics - Demand Uncertainty with ModelRisk: Part 3

Posted by Bala Deshpande on Wed, Oct 05, 2011 @ 10:00 AM

Tags: risk management, uncertainty, risk analytics

Tags: predictive analytics, SME analytics, advanced business analytics, systems thinking, uncertainty

## Risk management in 60 seconds: Insights from Entropy

Posted by Bala Deshpande on Thu, Mar 03, 2011 @ 10:49 AM

This **flash video** explains in a minute how entropy can work for measuring risk and uncertainty for business analytics problems. You can continue reading below or simply watch the video.

Imagine a box that can contain one of three colored balls inside - red, yellow and blue. Without opening the box, if you were to guess what colored ball is inside, you are basically dealing with uncertainty. Now what is the highest number of "yes"/"no" questions that can be asked to reduce this uncertainty?

Is it red? No.

Is it yellow? No.

Then it must be blue. That is *two *questions. If there was a fourth color, green, then the highest number of (yes/no) questions is *three*. If you extend this reasoning, it can be mathematically shown that the maximum number of binary questions needed to reduce uncertainty is essentially **log (T)** where the log is taken to base 2 and T is the number of possible outcomes. (ex: If you have only 1 outcome, then log (1) = 0 which means there is no uncertainty)! If there are T events with equal probability of occurrence then T = 1/P.

Claude Shannon used this idea to define entropy as *log (1/P)* or **-log P** where P is the probability of an event occurring. If the probability for all events is not identical, we need a weighted expression and thus entropy, H

H = -Summation (pilog pi)

Tags: advanced business analytics, entropy, uncertainty, information theory

## World Economic Forum needs a primer on risk analytics

Posted by Bala Deshpande on Thu, Oct 21, 2010 @ 04:00 PM

The venerable World Economic Forum recently published their 5th annual "Global Risks" report. While clearly this is a very timely analysis, we see a few problems with their report.

Tags: risk management, complexity analysis, global economic fragility, risk, uncertainty

## Supply chain complexity increase continues unabated

Posted by Bala Deshpande on Thu, Sep 30, 2010 @ 11:03 AM

In a recent survey conducted by GXS - an e-commerce and B2B integration services company, 84% of more than 800 respondents from the logistics industry said that they expect * supply chain complexity* to increase in the next three years. Furthermore experts who ran the survey indicated that the leaders will be the ones who can "master and manage this complexity".

Tags: complexity, business analytics software, business intelligence tools, uncertainty, measurement, supply chain complexity, critical complexity

## 2 pitfalls to beware of with traditional risk management - part 1

Posted by Bala Deshpande on Tue, Sep 28, 2010 @ 02:07 PM

Traditional risk analysis involves developing what are known as "ordinal scoring" scales. For example, this requires getting executives to answer questions like "what is the likelihood that the next major database upgrade will be delayed", "what is the impact of the hurricane season on the availability of medicines for our troops" etc.

Tags: data mining tools, business analytics software, business intelligence tools, risk management, risk, uncertainty, complexity management

Tags: complexity, risk management, uncertainty