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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How it Started

Reengineering—sometimes called business process reengineering, or BPR—originated in a 1990 article in the Harvard Business Review by former MIT professor Michael Hammer, who followed up in 1993 with the seminal book Reengineering the Corporation.

The Core of Reengineering

The core of reengineering is a change in organizational structure, in which people who were formerly organized by function (“functional silos”) are instead organized by process or product. That is, all the people who need to work together in a process or product report to the same manager, instead of separate functional managers.  There’s more to it than this, but this is the heart of reengineering.

The Problem with Traditional Organizational Structures

“Functional silos” are at odds with producing a great product quickly and efficiently.  For example, a new product may start out as an idea from Marketing, then Marketing “throws it over the wall” to R&D, which develops technologies to meet Marketing’s idea.  Then R&D throws it over the wall to Engineering, which designs the product.  Then Engineering throws it over the wall to Operations, which figures out how to make it.  Once in production, all the people who need to work together to make the product—line workers, technicians, manufacturing and equipment and industrial engineers—report to separate managers.  There’s not good flow and communication and coordination between all these different functions.  The product suffers.  Delays occur.  A lot of time and money must be expended just coordinating and communicating between departments, who are working on a variety of products and processes at once.

Definition of a Reengineered Organization

All the people who need to work together to produce a great product are in the same organization, receiving the same marching orders from the same boss, and are—ideally—in the same room.  How could this not lead to a smoother, faster product delivery that better satisfies the customer’s needs?

Oftentimes, organizing by product isn’t practical.  For example, a job shop that produces a great variety of low-volume custom products cannot have a separate production area or production team for each product (or even product category).  When organizing by product at a company level cannot be done, organizing by process can bring many of the benefits of reengineering, even on a scale as small as a particular production area in a factory.  That is, everyone working in a particular process—such as a certain production area—reports to a common manager.

An Example of a Traditional Organization

I was in a company that made a product that required about twenty production operations.  One of those production operations had, working within it:

1. Equipment operators that reported to the production supervisor
2. Equipment technicians that reported to the maintenance manager
3. An equipment engineer who reported to the equipment engineering manager
4. A manufacturing engineer who reported to the manufacturing engineering manager

These are classic functional silos.  All the people who needed to work together to perform the operation effectively and efficiently reported to a variety of different managers with a variety of different priorities, giving their people a variety of different marching orders.  For this reason, problems were very difficult to resolve.  It would be hard to get people to attend improvement meetings because they were more beholden to their bosses and their functions than to a particular operation.  If people were not doing their jobs, one had to go up to their functional managers to try to get it resolved.  The functional manager was often lackadaisical about resolving the problem because this was just one area out of a whole factory that his functional group covered.

An Example of a Reengineered Organization

In contrast, early in my career, I worked at the very successful company Applied Materials, which makes computer-chip-making equipment.  Applied had its manufacturing organized into work cells by process, such as Chamber Assembly, Gas-Panel Assembly, and Final Assembly.  In each of these work cells were assemblers, a manufacturing engineer, and a buyer/planner, all reporting to that cell’s work-cell manager—organized by process, rather than segregated by function.  In my work cell, we would have a weekly process-improvement meeting with all members present.  We would discuss problems and ideas for improvement, decide how to implement them, agree on action items, and boom-boom-boom it would all get done.  If we had been segregated by function, each attempted improvement would have dragged on for weeks or months if it got done at all.  During my work at Applied, the company moved from #7 in its industry to #1, due in part I think to its efficient work-cell-based manufacturing. 

Summary

The heart of reengineering is replacing functional silos with an organization organized by product or process: all people who work on a particular product or in a particular process report to a common manager.

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•  Fresh outside perspectives

•  Expertise

•  Unbridled firepower, since they’re not beholden to the daily production grind

But bringing in a consultant, if done wrongly, can be disruptive—and even destructive—to a firm.

The wrong way

I experienced the wrong way first-hand when I was employed at a company as its sole industrial engineer, many years ago.  I had a list about ten pages long of all the opportunities for improvement that I had identified in its manufacturing, ranked by ROI (return on investment).  I made rapid progress completing projects and knocking them off the list, but it was disheartening seeing all the money that we were still leaving on the table, being that I was only one guy.  Try as I may, my boss would not or could not hire more engineers.

One day, I heard that the vice president of operations had had a consulting firm come in for $30,000 for a couple weeks to assess opportunities for improvement in our manufacturing.  I saw the document that the firm produced and almost all of their ideas were ones that I already had on my list.  I showed the V. P. my list and he was surprised.  I was dismayed that my company had seen fit to have an outside firm come in to do what I had been chartered to do, without even consulting with me.  If done properly, I would have welcomed the consultants, appreciating the long-overdue extra manpower and enjoying working with them as a team, showing them what I had, and putting our heads together to identify even more opportunities for savings and working together to execute our plans.

That would have been the right  way.

But, as it was, I firsthand experienced the wrong  way to bring in consultants.

The right way—3 rules

1. Bring in a consultant who knows that  the front-line people who’ve worked in a company for years have more ideas and insights than even the smartest outsider.
2. Bring in a consultant who knows that his job is not to displace local experts, but to employ them as a foundation to create-—together-—even more ideas, then work together with them to implement them.
3. Bring in a consultant who knows that working as a team with local experts—as equal partners—not only brings the tangible benefit of making use of all the employees’ experience and ideas, but it also wins their psychological support.  When they see that the consultant will be an ally and source of empowerment for them—not an opponent—they support him fully.

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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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Dynamic 

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.

The post Save Up to Millions of Dollars a Year in Capex: Use Dynamic Capacity Planning appeared first on Simitar Operations-Improvement Consulting.

What is the best method of capacity planning

For a deeper dive on this subject, see this Simitar article, which was published in the FabTime newsletter (see page 5):

FabTimeNewsletter13 02

[This issue of the FabTime newsletter is made available with express permission of FabTime.  To subscribe to receive future issues of the FabTime newsletter, please visit FabTime’s website at http://www.FabTime.com/newsletter.shtml.]

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Knowing this, you can preempt problems such as WIP bubbles.  The result is:

• Higher throughput
• Better on-time delivery
• Reduced cycle time
• Reduction in problems/headaches/firefighting

It also enables you to set more realistic production goals for your managers each shift.

How I do it

Using simulation software, I build a detailed model of the factory, including all the major machines and products in the factory.  I create an interface with the MES (manufacturing-execution system software) to pull in up-to-the-minute WIP and machine status to the model.  Regularly (say, weekly, daily, or even every shift) I download the current factory status into the model, run it, and predict factory performance in the coming hours, days, or weeks.

Use the model’s findings to preempt problems

Where problems are foreseen, actions can be taken to preempt them, such as changing a prioritization scheme at a machine, changing a machine setup, or shifting the timing of a PM.  I can further simulate the effects of these corrective actions to gain confidence that the actions will actually solve the problem.

Use the model’s findings to set realistic production goals

The model’s output can also be used to set fair, realistic production goals for production managers in each area, each shift.  Managers perform better knowing that they are being judged fairly.  For example, a manager who will be receiving fewer upstream assemblies will not be dinged for having below-average moves.  Similarly, a manager who meets moves goals knows that it was due to his/her performance–not the luck of having higher-than-average levels of WIP coming in, since that is already accounted for in the goal.

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The state of the art in industrial engineering

I can’t think of any aspect of the above philosophies that conflicts with any other.  Each iteration is just what came before it, combined with new findings.  So you don’t need to determine which to apply.  A competent industrial engineer will know all of them, and make use of all of their tools as appropriate.

The post How Do I Choose Between Lean, Six Sigma, Theory of Constraints (TOC), and other Operations-Improvement Approaches? appeared first on Simitar Operations-Improvement Consulting.

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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