The semiconductor industry is undergoing a sea change. It’s being split into haves and have-nots, and it has become much more difficult for everyone to make a profit. Never have so many smart people worked so hard for so little money.
Walk into a multibillion-dollar chip-fabrication plant—a fab—and you may very well get the impression that the industry is headed for a spectacular meltdown. One of the first things you’ll see is a bay the size of two basketball courts packed with equipment for projecting a lithographic design onto wafers. Nearby, you’ll find a towering bin, called a stocker, filled with wafers waiting to be processed by this equipment. The wafers are worth from US $10 million to $100 million—all of it idle inventory.
Why? To amortize the $5 billion investment in a fab over a five-year schedule costs more than $3 million a day. Conventional wisdom holds that to generate that much money you must keep all the equipment running all the time, even if that means creating large unused queues of wafers. What’s more, to justify that scale, you have to produce a semiconductor product in volumes of at least 5000 to 10 000 wafers per month.
More than anything else, Moore’s Law has been responsible for the gigantic costs. It takes huge amounts of capital to support the incessant cycles of investment and obsolescence that keep Moore’s Law on the march. That rapid cycling explains why a company’s shining jewels can turn into white elephants in just five years.
Although industry giants like Intel and Samsung work on a vast scale and can therefore make these huge investments work for them, smaller companies (and even some sovereign states) can no longer afford to play the game. A massive restructuring in the industry is forcing them to consolidate or outsource production in order to gain sufficient scale to compete.
Every month new alliances and divestitures bring fresh evidence of this restructuring. In 2006, Texas Instruments announced that it would partner with foundries to codevelop future process technologies based on line widths (the smallest feature on a chip) of less than 45 nanometers. In 2003 and 2006, respectively, Motorola and Philips—iconic companies in the industry—spun off their semiconductor operations entirely. In 2006, LSI Logic (now LSI Corp.) acquired Agere Systems and continues to struggle. Intel sold its communications and application-processor business to Marvell Technology Group. Advanced Micro Devices, its cash flow and its competitiveness in question, acquired ATI Technologies in 2006.
All these strategic moves were meant to recover the growth and profitability of the past. But none of them have done so.
There is, however, a glimmer of hope, and it comes from an unlikely source: the Toyota Motor Corp. For more than 30 years, Toyota has followed a production system that has enabled it to increase quality, double capacity, produce a wider variety of models in a given factory, and change the mix on a dime. Last year Toyota made more cars than any other company, surpassing General Motors.
Even more important, Toyota’s approach to mass production has produced bountiful profits. In 2005, it earned more than all the other auto manufacturers in the world combined. Yet although many scholars and executives have scrutinized Toyota’s plants and production methods—GM went so far as to open a joint venture with Toyota in California—no one has yet been able to fully replicate its success.
In early 2007, we had the opportunity not merely to emulate Toyota’s system but to apply its principles to a logic fab belonging to an integrated device manufacturer (IDM). As consultants, we are not at liberty to divulge the company’s name; however, it’s safe to say that the company is highly competitive—that is, it has survived and prospered by pursuing Moore’s Law, always remaining at the forefront in technology and operational excellence. But Moore’s Law was turning this jewel of a fab into a white elephant while the equipment was still relatively new.
In just seven months, the organization was able to reduce the manufacturing cost per wafer by 12 percent and the cycle time—the time it takes to turn a blank silicon wafer into a finished wafer, full of logic chips—by 67 percent. It did all this without investing in new equipment or changing the product design or technical specifications. And this short experiment has exposed only the tip of the iceberg. We believe that these early results point to what we call the new economics of semiconductor manufacturing and that this will have a profound and lasting effect on the industry and create new opportunities for growth.
The principles and philosophy of the Toyota Production System (TPS) that we applied were first described in 1999 by Steve Spear and Kent Bowen, then at the Harvard Business School , in their article “Decoding the DNA of the Toyota Production System” in the Harvard Business Review. They noted that Toyota trains its workers to treat any problem that arises as an opportunity to learn. Toyota designs and redesigns work according to a rigorous process to examine the current state of production and generate hypotheses on how to improve it, together with a highly specified expected outcome.
It’s an empirical approach based on iterative experimentation, one that long escaped the many Toyota watchers who typically fell into the trap of confusing the company’s tools—such as kanban cards, used to order parts—with its principles.
Spear and Bowen distilled TPS into four rules, which in summary are (1) highly specify activities, (2) clearly define the transfer of material and information, (3) keep the pathway for every product and service simple and direct, and (4) detect and solve problems where and when they happen, using the scientific method. When we present these rules, even in their fully detailed form, clients generally protest that they “do it that way already.” But on closer examination—while auditing their fabs—we often find something quite different [see sidebar, “The Toyota Production System Sanity Check”].
Here are examples from our work.
The first rule, on activities, states that “all work shall be highly specified as to the content, sequence, timing, and outcome.” At the fab we studied, maintenance technicians were supposed to clean the etch chamber from top to bottom, but we observed that sometimes they did it from bottom to top. That order wouldn’t have been so bad if it had been followed consistently, because the behavior would have become a new set point around which further improvements could be based. But in fact, the method of cleaning changed unpredictably. There was so much random variability in the work that nothing could be learned from the results.