Add People and Machines; Mix Well

Using WWK’s Factory Explorer software, I built a Monte Carlo simulation model—that is, a model that emulates the random variability of the real world.  The model contained all pertinent production machines as well as the maintenance technicians responsible for keeping them running.  I modeled the machines’ demand for technician time in the form of scheduled preventive maintenance as well as random breakdowns.  Real-world historical breakdown data were used to ensure that the model’s machines broke down randomly but in accordance with their real-world probabilities.  Once a technician was available to respond to the breakdown, the time needed for repair was also random but in accordance with real-world probabilities.

Sim City

I ran the model using the existing machine quantity but with different quantities of technicians.  I assumed that incoming inventory was always available (not a bad assumption since this was the first operation of the company’s production process).  After running each scenario multiple times, I added up the fixed component of the machine cost and the total cost of employment of the technicians.  I divided this number by the resulting average wafers out for that scenario.  This gave me the cost per wafer for this machine/technician ratio.  I ignored variable costs because by definition they are always the same per wafer.  So this analysis just looked at the machine-quantity and technician-quantity components of wafer cost.

Surprising Results

I graphed the results, with cost per wafer on the vertical axis, and machine/technician ratio on the horizontal axis.  The analysis showed that the ratio that produced the lowest wafer cost was 3.5 machines per technician.  This was a higher number of machines per technician than the company was using.  The model showed that shifting to this ratio would actually increase wait-for-technician time by about five percentage points.  That is, the machines would spend about 5% more of their time idle than they did today, due to waiting for an available technician.  Doing something that increases machine idle time is anathema in manufacturing.  But the analysis showed that this would actually reduce the company’s cost per wafer.  At forecast volumes, savings would be $210,000 over three years.

The Tip of the Iceberg

This savings was from analyzing just one small area of a massive worldwide production operation.  What would be the savings if this sort of analytical method were used across the company?

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The simplest—and highest-profit-margin—modeling project that Simitar founder Bob Kotcher has ever done demonstrates the power of simulation modeling for operations improvement.

Celebrate your inner Seinfeld

At a restaurant one day, my inner Seinfeld came out (we all have one—come on).

Since the Americans With Disabilities Act was passed in 1990, many restaurants had to remodel their restrooms to make them wheelchair accessible. This often required them to remove internal partitions that comprised the stalls. With privacy gone, they put locks on the restroom doors and made each restroom single-user.

BUT…many businesses failed to remove the male or female signs from the doors. Furthermore, even some new restaurants, building new single-user restrooms from scratch, put male and female signs on the respective doors. Why not put unisex signs on each door? We now often have the problem of going to such restrooms, seeing a line in front of “ours,” and having to wait (and wait, and wait, and wait…) while the other restroom sits enticingly empty (as our meal buddy out on the table grows increasingly bored).

This calls for an engineering analysis…

Suppose we did a time study on the restrooms (yes, I am an engineer) and used the results to make a static capacity model in a spreadsheet? We’d almost certainly find that each restroom is loaded far below capacity—maybe 75% at peak hours. If we made them unisex, average loading would remain the same. So what’s the problem with them being dedicated by gender?

Well, a computer simulation model would show how, by replacing the two restrooms’ signs with unisex signs, average queue time would decrease.  This is because, due to random variation, there are times when there are women in line when the men’s room is wide open, and vice versa.  Making the restrooms unisex would reduce waiting time in such situations.  Reduced waiting would perhaps result in more satisfied customers and more repeat customers—all for about $20 for new signs.

From restaurant to wafer fab (they both offer chips)

Now, I doubt that restaurants will be conducting simulation analyses to see if spending $20 for new signs would be a profitable investment or not. But one internal client at a wafer fab did something similar.  You see, in wafer fabs and other factories, there are often several identical machines processing in parallel at a particular operation.  If they’re running different recipes, process engineers often like to dedicate each machine to a particular recipe.  This makes process control easier.  But it increases average waiting time due to the above phenomenon.  What is the best tradeoff?

At this client, a process engineer oversaw a wet bench with two parallel identical baths, the only difference being that they were set at different temperatures (dedicated by gender, if you will). Recipe A required temperature A, and all other recipes required temperature B.  The engineer found that he could run the Recipe A wafers at the B temperature if he made Recipe A’s processing time longer.  This would reduce average queue time for all wafers…but increase the processing time for Recipe A wafers.  His question to me was: if I set both baths to the same temperature, will the reduced queue time for all wafers outweigh the increased processing time for Recipe A wafers?

I pretty quickly did a simulation analysis and found that, yes, setting both baths to temperature B and increasing Recipe A’s processing time would actually reduce  average cycle time.

Act on the analysis results, bank the savings

Armed with the analysis results, the engineer made the change. And the result was the biggest return on investment of any simulation project I’d ever done! Not because the savings were so mammoth, but because the cost of the rather simple analysis plus the cost of making the ensuing change were so low. But the change never would have been made had the simulation model not been available to test it out, because nobody in his right mind would approve a change that would increase loading on a machine in order to reduce  cycle time. Until a simulation analysis showed that it did.

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Overview of modeling

Unlike most other operations-improvement consultancies, Simitar is expert in computer-simulation modeling. 

When operations are highly complex or variable, computer-simulation modeling can reveal huge opportunities for savings that spreadsheet models—and even experienced observers—overlook.  That’s because simulation models take into account the variability present in real life.  Simulation models also include a critical factor that spreadsheet models ignore: cycle time, and how it varies with load. 

Once optimal parameters are found in the simulation model, they can be applied to the real operation.  The result can be significant improvement in all aspects of operations performance, as well as reduced capital-equipment expenditures. 

Any type of operation can be modeled

Essentially any manufacturing, service, or business process can be modeled. Simitar personnel have modeled:

• Entire wafer fabs containing up to 1500 production steps and 170 types of production equipment spanning multiple sites around the world.
• Mid-sized production areas to assess complex interactions between machines and to estimate profit-maximizing staffing levels.
• Individual pieces of production equipment for internal throughput optimization.

Service industries and business processes are heavy beneficiaries of simulation modeling. Examples are hospitals, distribution networks, rail systems, airports, call centers, and claims processors.

A model can help you make thousands of decisions

People unfamiliar with simulation are usually not even aware of the vast variety of questions that a simulation model can answer—they don’t know which questions to ask. Here are a few examples:

• What capital equipment should I order to meet next year’s throughput and cycle-time goals at minimal cost?
• What combination of dispatching rules, setup rules, batching rules, and operator allocations will most improve my throughput, cycle time, on-time delivery, and cost?
• Where are WIP bubbles likely to form during the next shift, and what can I do to preempt them?

One-time or permanent model?

Models can be built for a one-time decision or can be maintained and used on an ongoing basis for continuous improvement and ongoing capacity planning.

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