And its impact on decision making
Part 1 of a series on using T, I and OE for key decision making
Challenging widely accepted paradigms creates new opportunities
The terminology in Physics does not use words with dramatic intensity. However a certain incident in the late 19th century was so embarrassing that it was called “The Catastrophe in the ultraviolet” and by that caught my imagination. The story is about radiation emitted from a black box and the mathematical equations, according to the knowledge of that time, showed that the radiation should be infinite. Well, it was easy to see that this is NOT the case. What eventually solved the riddle was the discovery, understood through Quantum Theory, that the frequency of the emitted radiation is not continuous but discrete. As it turns out discrete functions behave very differently from continuous functions.
There is a tendency in the social science circles to assume that the main functions, describing the behavior of key variables, like capacity or the cost of capacity, are continuous.
I claim that all cost functions in reality are discrete. This is most certainly true when we speak about the cost of capacity.
All organizations spend their overhead expenses on providing enough capacity that is required for the business. The usual way is to purchase a certain fixed amount of capacity, like space for storage or offices, a machine capable of processing a certain quantity per hour and employees who agree to work N hours every week.
The cost of providing that capacity is fixed whether you actually use all that capacity or only part of it.
This means that using 25% of the available fixed amount of capacity, or using 85% of that quantity costs exactly the same! This is a basic non-linear behavior and its impact on the decision what to do with the capacity at hand is HUGE.
Once all the available capacity is used then new options of using additional capacity open.
But, the principle of being able to purchase capacity only in certain fix sizes is still on.
An employee might agree to work another hour, but usually not a part of an hour. So, if you need just 34 minutes of overtime the cost is one hour of overtime, which is also considerably more expensive than the relative cost of a regular hour.
So, when we look on the behavior of the cost of capacity we realize the following behavior:
The initial cost is HIGH. Then it becomes zero (0) until a certain load is reached. Then the cost jumps by another fixed amount. Using more capacity the cost is zero until the next fixed point.
This actual behavior is quite different from the current practice of associating the average cost to any use of capacity.
This is the kernel of the TOC challenge at cost accounting!
So, the simple principle of cost accounting is invalid in our reality. This use of the average cost of capacity has led all the way to the fiction of cost-per-unit.
Do we really need “per unit” measures to support sales decisions?
We still believe in simplicity, but reject the wrong simplicity. What could be simpler than have a way to measure the direct impact of a decision on the bottom-line?
Let’s now look on another realization:
There is no hope in hell to use all the available capacity!
This is certainly in direct clash with the common paradigms.
There are three causes for being unable to use all the available capacity to generate value:
- TOC has demonstrated the need for protective capacity to provide good and reliable delivery performance.
- The market demand fluctuates in a faster pace than our ability to adjust the available capacity.
- Capacity is purchased only by certain sizes. This is similar to what has been already stated above.
What are the ramifications for decision making?
When a new market opportunity pops-up we need to consider the state of the capacity usage of every resource. When there is enough excess capacity the usage is FREE! When the additional load penetrates into the protective capacity then there is need to carefully check the cost of Additional capacity or the ramifications of giving up some existing sales.
This is very different generic approach than the existing management accounting tools!
Next post would explain more on how to calculate the impact of an opportunity on the bottom-line, without using any “per-unit” kind of measure that would force us to use averages and get a distorted answer.