Throughput (T) and Operating Expenses (OE), and the Capacity critical connection – the key for decision making

T represents the added value generated by the organization. Operating expenses represent the financial cost required to provide the capacity for all the required resources with the appropriate capabilities, required for generating the value to customers that would be able to generate the T.

Confused? Read it again, this comprises most of the truly required data for managerial decisions.  The division between T, which is focused on sales data and OE focused on the internal resources, is of immense simplifying value to all managerial decisions.

Here is a rough diagram:

I apologize for the poor graphics; I’m not very good with the use of graphical tools.

OE is just the cost for providing capacity. The goal is to have Throughput (T) much bigger than OE and then find the way to grow T faster than OE.  That should be the sole objective of every single decision taken any manager in the organization.  There might be difficulties to do the analysis, but the objective is the same. T for business organizations is defined as Revenues minus the Truly-Variable-Costs (TVC).  The truly variable costs are those that occur with every single sale.  So, T is the added value as measured by the customers, who are willing to pay the price. But, the value for customers includes also what others, who are not part of the organization, have contributed.

Thus, T is the true performance measurement of what the organization succeeded to achieve. OE is what the organization has to pay in order to achieve the T.

Well, I should have also included ‘I’, standing for ‘Investment’, as the part of the capital being invested to make it possible to achieve the T. But, I think there is no conceptual difference between ‘I’ and ‘OE’.  The difference is about time frame.  ‘I’ refers to expenses that stretch beyond one year.  There are mechanisms to convert multi-year expenses into equivalent stream of annual expenses – and these are part of the OE.  So, a $10M machine, which is supposed to work for 10 years, represents an annual expense of $1.1M or whatever conversion rate you think is appropriate.

Comment on a minor complication: Originally Goldratt defined ‘I’ as ‘Inventory’. He moved to the more generic term later.  But, what is a little missing point in the above rough chart is that the materials being purchased are in a temporary state of Inventory (part of Investment) until it either become part of T or part of OE when scrapped.  I don’t think it really complicates the simple picture.

The key point is to understand that OE is the critical enabler to generate T.  And being made of many individual items creates a technical problem to predict how much OE would support future T, for instance taking actual initiatives to double the current level of T might require additional delta-OE that could be more, or much less, than the current level of OE.

The majority of management decisions are about growing or just maintaining the current level of T. After all Sales is about achieving T, and the efforts of Operations are aimed at delivery.  But, there is a constant pressure to reduce OE, mainly because OE represents an ongoing threat to the organization:  you have to pay the OE no matter whether you made enough T or not.  The tricky point of saving OE is that in most cases the negative impact on T is ignored.  The emphasis on T makes you aware that you need to be very careful not to reduce T.

So, we have to understand the dependencies between T and OE, and they look very complicated, because OE is about capacity of so many different, seemingly independent, items.

TOC, through Throughput Accounting plus understanding the full impact of the five focusing steps, the role of buffers in planning and buffer management in execution, gives a much simpler answer to the connection between OE and T.

Critical insight #1: It is enough that one resource would be overloaded, receiving more load than its available capacity to seriously harm the expected T unless significant additional OE is added.

Critical insight #2: There is a real need to maintain protective capacity, certain amount of excess capacity, in order to provide enough flexibility to overcome market fluctuations and other types of uncertainty.  There is no safe formula to calculate precisely the required protective capacity, so conservative assessment is required and then getting the appropriate feedback to ascertain that it is enough.

Critical insight #3: Every internal resource has a finite capacity being covered by a portion of OE, but many times there are temporary ways to increase capacity for a cost, usually much more expensive per unit of capacity than the regular available capacity.  Such means could be part of the protective capacity, but their real value is allowing taking opportunities that clearly require more capacity than the current OE covers. That means delta-OE has to be considered and compared to the expected delta-T.

Any decision that deals with ways to increase T has to analyze the possibility that one or more of the critical resource would be overloaded, and if so find a way to either reduce other sales or increase the capacity of the specific resource(s).

The cost of capacity changes in stepwise ways, which makes the behavior of OE to be clearly non-linear.  One might look at it as a complication, and it really makes the whole notion of “per-unit” measurements non-usable in reality.  But, when the full impact of uncertainty is recognized, then simulating ‘what-if’ scenarios could reveal when the connection between T and OE are clear enough in supporting a decision, or when there is a doubt.

Another realization is that ideas for increasing T are usually significant and their expected impact, both on Sales/Throughput and on the required capacity, is far from being deterministic.  So, some means to check both the conservative realistic possibilities and the more optimistic ones have to be carefully checked.

Another insight: When judging the impact of an idea on sales it seems that if the conservative assessment of the impact is already good, then there is no need to check the option that the impact would be far greater. This is a mistake! When the market reacts very favorably then more problems in capacity, causing delays in delivery, have to be taken into careful consideration.  So, there are clear possible negative impacts of succeeding too well.  It can be called “The curse of blessing”.  I heard that interesting insight from Shimon Pass.  This is a devastating insight if you are not aware of it.

Is the above “simple”?

I think it is as simple as we can get when we strive to be right most of the time.

People who like to know more on what I have briefly outlined above could ask me for a presentation and demo of Throughput Economics, a detailed methodology for evaluating decisions for achieving better much more T than OE.