Dynamic capacity planning incorporates the randomness and variability of the real world.  It shows you how to target your capital-equipment (capex) spending to attain throughput and cycle-time goals for millions of dollars less than with spreadsheets.

Dynamic capacity models account for the variability of the real world

In operations with variability, executives know that they cannot run them close to 100% loading because queue times become unacceptably high. They know to invest in surplus capacity—not to increase throughput, but to keep queue times in check.

But what is the optimal amount of surplus capacity to purchase, and exactly where? What is the optimal capital-equipment (capex) purchase plan?

The high cost of static capacity modeling

Many companies use static spreadsheet models for this and swag a target loading number: if loading goes above X percent at any operation, they invest in more capacity at that operation. In wafer fabs and fab-like operations, I’ve seen X as low as 65% and as high as 95%.

But swagging a number like this leaves massive amounts of money on the table—$10 million+ a year for a typical wafer fab. How?

Because one is investing in some equipment that actually doesn’t do much to reduce cycle time per dollar invested, and not investing in other equipment that actually reduces cycle time quite a lot per dollar invested.

What a dynamic model does that a static model doesn’t

Conducting a dynamic capacity analysis enables one to see the correlation between loading and queue-time contribution for each machine type. Then one can invest in machines in order of cycle-time-reduction per dollar. This way one can achieve the same throughput and cycle time of the spreadsheet method, but often for millions of dollars less. And what’s great is that the savings are all quantifiable—one simply compares the machine set recommended by the spreadsheet model with the machine set that the dynamic model says will produce the same throughput and cycle time. Then look at the price difference. The capex savings in the first year is often 10-20 times the cost of building the model. The return on investment for a dynamic capacity modeling project is thus easily quantifiable and justifiable.

How is a dynamic model built and operated?

How, exactly, is dynamic capacity modeling done? A dynamic model incorporates the same information that goes into a spreadsheet model-—and a bit more. The data is put into dedicated modeling software. Upon the “Run” command, an internal clock starts in the software, products start in the model, machines start breaking down and getting repaired, parts start occasionally requiring rework or are scrapped, operators start going on break or calling in sick—-all the pleasures of the real world. All of the above occur randomly, but in accordance with their real-world probabilities. When the run is finished, we now have a critical new statistic: time. We can see what every product’s queue time was in front of each machine. Rerunning the model with different quantities of machines, we can see the correlation between various machine-purchase options and cycle time.

Types of operations that are best candidates for dynamic modeling

Any operation that is variable and/or complex. Great examples are wafer fabs, job shops, hospitals, and many supply chains. Few operations are so simple and determinate that they cannot benefit from dynamic capacity modeling.