## Sunday, October 7, 2012

### How Cloud Computing is an Example of Finance Principles

There are certain principles that we see appearing over and over again when performing finance work. Since they are common to many situations, we can view them as “building blocks” to a complete finance and treasury practice.
One of these is the “Pooling Principle”. Let’s look into this a little more closely.
What is the Pooling Principle?
The key element involved in the Pooling Principle is variability. Without variability, pooling does not much matter. However, under certain conditions, variability can be reduced if we can create certain combinations.
Simply speaking, if we add 2 + 2, and we get 4, then the Pooling Principle has not manifested itself. On the other hand, if we can add 2 + 2 and get 3, then we have witnessed the Pooling Principle in action.
Following are some illustrative examples.
 Figure A
Example 1
Let’s say that we own a house worth ²100,000 (for new readers, the symbol ² stands for Treasury Café Monetary Units, or TCMU’s, freely convertible into any currency at any exchange rate you choose). In addition, assume we know that the chances of a house having a fire in our town is 1% annually. If we want to make sure we can cover 100% of the cost of the house for the maximum number of fires that may occur over the next 20 years, how much cash do we need to reserve to cover that possibility?
The statistical way to answer this question is to use the binomial probability distribution. Figure A shows the equation we use to calculate how many events happen (x) in a given number of “trials” (n), with a certain probability (p). In our case the number of fires over this time period is x, 20 years is n, and 1% is p.
 Figure B
Figure B shows the results of Formula A in Excel (there is also a function in Excel called “BINOMDIST” which can be used as an alternative to entering the equation manually).
These results show that 3 fires during the 20 year span is within the realm of probability (we get the value of 3 as this is the first time in the cumulative column that 100% is reached), so the amount of funds we need to set-aside is ²300,000 (²100,000 value x 3 fires).
Let’s finally suppose that there are 100 houses in our town, each averaging the same value, and everyone targets the same level of reserves for their houses. Collectively the citizens of our town will have to set aside ²30,000,000.
Enter Pooling
Now let’s apply the pooling principle. If all 100 people participate in a “pool”, where each individual’s funds goes into the pool and funds are paid out to those who have a fire in the next 20 years, what is the amount of funds each person must contribute?
 Figure C
In this case, the number of homes is 100 (n), the probability of a fire is 1% (p), and we use the binomial distribution to establish how many fires occur each year (x). Figure C shows the results.
The point at which the cumulative probabilities equal 1 is 6 homes, so the pool must have the capacity to cover 6 fires every year, or ²600,000. The period under consideration is 20 years, so the total amount of reserve required is ²12,000,000.
Since there are 100 people in the pool, each individual’s contribution is therefore ²120,000, 60% less than the ²300,000 requirement if the pool did not exist. Two plus two no longer equals 4!
You might think this example looks like insurance, and you would be right.
Cloud Computing
 Figure D
As a second example, let’s assume that our company has 1 server running our finance and accounting software. The processors in the server can process 10 process requests per second. Process requests generated by employee’s use of the software arrive at the server at a rate of 8 per second.
 Figure E
This situation can be analyzed using a Queuing Model. Figure D shows the main equations that characterize the operating system according to this model. Using those equations, our server situation is shown in the first column of data in Figure E.
We now assume that we can move this computer processing environment “into the Cloud”, which is just a fancy way of saying that the servers holding and running the software are in many other places – and all of them somewhere other than our facility.
We further assume that 99 companies identical to ours sign up for this cloud service as well. If the Cloud provider’s infrastructure has 1 server for each company, than there will be 100 servers, and if our firm sent 8 process requests per second, the Cloud users as a whole are therefore sending 800 process requests per second.  The second column of Figure E shows the operating characteristics under this set of assumptions.
Comparing the first and second columns, average time per server and utilization remain the same, but the other measures – wait time, total time in system, average number of customers (a “customer” being a process request in this case) have all gone down. This means the operation has improved. Two plus two no longer equals 4!
One way to measure this improvement in a slightly different manner is to reduce the number of servers in the cloud until one of the operating metrics is equal to what it had been prior to moving to the cloud.
In the first column of Figure E, the average number of “customers” (i.e. processing requests) waiting in the system was 3.2, whereas in the second it is close to 0.08. By adjusting the number of servers, we can get back closer to the “pre-cloud” number. If we go to 88 servers, the metric is a little better, and if we go to 87 servers, it is a little worse. The third column in Figure E shows the case with 88 servers. Most of the operating characteristics are still improved over the single server case.
In essence, whereas under the situation where we were operating the software “in-house” and had to purchase a server, under the cloud situation our needs only require 88% of a server, so the costs to the cloud provider (due to less capital investment) are 12% less. The pooling principle is one of the elements that drives the economic advantage of the Cloud business model (but by no means the only one!)
How You Can Apply the Pooling Principle
The following list of questions might indicate areas where implementing the Pooling Principle can improve your function or business:
Operations
Can you combine certain activities common to different departments? The Shared Service Center, for example, relies on the pooling principle.
Can you combine similar activities with other organizations? The economics of outsourcing rely in part on the pooling principle. Similarly, some forms of strategic alliance and joint venture employ the benefits of pooling as part of the rationale.
Strategic Acivity
What activities and costs can be pooled through mergers and acquisitions? One of the benefits of M&A activity are “synergies” that can be created through business combination. Many synergies are driven by the pooling principle.
Can we use the pooling potential in our business model to create growth opportunities? This question was certainly considered by many of those now providing Cloud Computing activities.
These are just a sample of questions that might provide insight.
As we discussed in the beginning, the pooling principle relies on variability. The more you hunt for where this is a factor, the more opportunities you will find.
Is it in sales, operations, finances, or marketing? Is it manifested in the value chain of your industry? Where is variability itself changing? Either increasing, in which case pooling will become more valuable, or decreasing, where the value of in-place pooling structures might no longer be compelling.
Key Takeaways
The pooling principle is a significant economic driver in many internal and external facets of your business. Be ever alert to where variability manifests itself, and carefully consider how you might create opportunity under this situation via pooling.
Questions
·         Where are the primary benefits of pooling in your business or function?
·         Where else do you think pooling might create value?