One of the biggest problems with most simulation projects is the availability of good data.  The phrase Garbage-In-Garbage-Out gets tossed around liberally.  However, this is a double-edged sword.  As an analyst, villifying the existing data is meant to prompt your customer to go get better data.  Many times poor project timing or the lack of a convienent comparable system leads to the project team to take the easy way out.  That easy way is to look for the worst case scenario on the data and use that as an input.  The logic behind the decision is that if the system can handle the worst case, it will be sufficient for any production goals that have been set.

The phallacy in that approach is twofold.  First, using worst case data can easily lead to a system that is over-designed.  Too much capacity is required to support the wild variations introduced by worst case data.  Second, if your goal is something other than production (say inventory reduction), your worst case data may have the opposite effect.

I ran into this problem on a recent engine-chassis marriage project.  The engine-chassis marriage operation at an auto plant involves ”stuffing” the powertrain for a car into the shell of the car and fastening it in place.  Usually the power train is moved up into place while the car body moves overhead.  A separate build-up line is used to assemble the powertrain component and the sequence of the engines and transmissions matches the sequence of cars being built on the main production line.  A picture of a vehicle that “stuffs” the powertrain in place is shown below.

For this project, there were two main car styles that were produced by the plant.  Each “style” required different tooling for the cart shown.  the time to change the tooling was several job cycles so extra carts with a mixtures of tooling options were used to allow time to switch plates without interrupting production.  The key to sizing the system was how often these tooling plates would need to change.  This is driven by the sequence of cars going down the line.  Getting this sequence proved to be a challenge because the vehicle sequencing program at an assembly plant is quite complex.  Initially, we tried a completely random mix of jobs.  The rationale was that this would be the worst case.  While it truly was a very bad case for the system, it would have driven us to expensive changes to the system had we not tried to find a more realistic solution.  By spending the time to get an accurate reflection of the sequence, we were able to proceed comfortably with a system that had a lower capital investment.

In short, the competitive landscape of the manufacturing does not allow us to take the easy road and protect for the worst case scenarios.  We need to be diligent about gathering the appropriate data.  An error to the side of caution can be as damaging to a company as an error that causes a system to be undersized.