Image: Stuart Bradford

The next rule states that “every customer-supplier connection must be direct, and there must be an unambiguous yes-or-no way to send requests and receive responses.” This rule was violated when, for example, a worker—we'll call her Jane—operated a deposition-process machine that received wafers supplied by another worker, whom we'll call Bill. That arrangement made Jane Bill's “customer.” In a properly ordered system, he'd send her wafers when—and only when—they were needed. In practice, he sometimes had no wafers when she needed them, and at other times he encumbered her with wafers she could not use. Jane then had to throw those excess wafers onto the costly pile of inventory.

The third rule states that “the pathway for every product and service must be simple and direct.” This rule was violated when a cassette of wafers showed up at a bay equipped with 10 identical process tools, any one of which could be used to process the cassette. But which tool were the workers supposed to use? No one could say offhand because the path was not simple and direct, and the critical decision was left to the operator on the floor.

Why does this indeterminacy matter? First off, not knowing the direct path from the outset makes it hard to reconstruct that path later on, if a batch of wafers should show defects. Even worse, ignorance of the path prevents the system as a whole from discovering a defective machine in time to prevent a recurrence. Memory fades quickly, and when workers are able to avoid a malfunctioning machine without fixing it immediately, they lose opportunities to learn and improve. The Toyota system requires that the problem be addressed locally, immediately, and intensively by clearly defined actors, not just any “expert” who happens to be available.

The fourth rule states that “any improvement must be made in accordance with the scientific method under the guidance of a teacher, at the lowest possible level in the organization.” One worker found a better way to get a customer the material he needed. He helped the customer pull the material from his supplier instead of having the supplier push it to the customer, as had been done before. The worker had to analyze the current state of things, document it, and formulate a hypothesis that included an experiment with an expected outcome that could be measured and compared with the actual outcome. Such problem solving engages everyone and creates an army of scientists engaged in continual improvement and organizational learning.

Implementation of these ideas is harder than it may seem; it requires a certain adjustment of thinking. We have studied many companies that were trying to increase their operational efficiency. In the beginning, they typically lump TPS under the rubric of “lean” manufacturing. But while many lean techniques have great merit, the Toyota system is strikingly different.

Most semiconductor companies undertake elaborate theoretical work in offices and conference rooms, using computer simulations and spreadsheets, in the hope of determining fundamental mechanisms. They typically focus on creating big projects that promise to yield “silver bullet” solutions. While this approach can certainly have its rewards, it's not the Toyota way.

TPS constitutes a highly empirical method of managing a multistep manufacturing process. Such empiricism beats simulation, because no simulation model is sophisticated enough to capture the complexities inherent in semiconductor processing. The fastest way to develop process understanding is to execute lots of small, fast-paced scientific experiments on the factory floor. The factory is the laboratory. This is the essence of TPS—rapid, iterative, experimental problem solving.

Such experimentation, done in the course of regular production work on the factory floor, doesn't necessarily yield a final, perfect answer to a particular problem—no such perfection may exist. Nor does TPS use an off-the-shelf “cookbook” approach. Instead it adjusts to the strategic imperatives concerning cost, quality, flexibility, or any other metric the company wishes to emphasize at a given time. For this project, our client instructed us to concentrate on reducing cycle time and cost, both of which were critical to opening new markets for the fab.

In January 2007, we set to work on this collaborative effort. At its inception, the organization viewed the initiative to implement TPS as just another management flavor of the month. The prevailing attitude was to wait it out until the consultants left and things returned to normal. Thanks to the commitment, tenacity, and vision of the plant manager, this kind of resistance melted away.

To get the people at the fab to buy into our program, we formed a project team composed of eight people representing key functions, such as manufacturing management, equipment maintenance, finance, strategic planning, engineering, and fab floor personnel. As the first item of business, the team set a goal to cut costs by 12 percent and cycle time by 32 percent within the first six months. Moreover, we strove to build a learning organization that would be able to sustain the work long after we had left.

The next order of business was to train people in the Toyota system and in manufacturing science. In accordance with Toyota's principles, much of the training took place on the fab floor. The plant management played the role of new hires attempting to gain certification to process wafers in the fab under the supervision of a senior technician. In other words, they acted as apprentices. The basic premise behind this approach is that in order to mentor, coach, or teach someone to solve problems, you must have direct experience in solving them yourself.

Although at first this learning technique met with considerable resistance (and skepticism), it proved to be highly effective. Many members of the plant's management were very surprised, and exhausted, after experiencing what a typical day was really like for the people who actually make the product.

By August 2007, the organization had lowered cycle time in the fab by 67 percent and reduced costs by 12 percent. In addition, the number of products produced increased by 50 percent, and the production capacity increased by 10 percent, all without additional investment. If the fab continues on this journey of organizational learning and improves aspects such as equipment maintenance variability, we expect even bigger gains.

The potential impact of the Toyota Production System is profound, because its improvements affect the general relationship between a factory's cost of additional production capacity and the average cost per unit. This relationship forms what economists call an economy-of-scale curve, and it applies to a number of capital-intensive businesses, including semiconductor and automobile manufacturing.

Let's examine the concept in detail. Imagine that it costs an average of $20 per chip to produce 2000 identical chips. If you then increase the volume to 4000 chips, the average unit cost drops to $12 per chip. Increase it further, say, to 6000 chips, and the cost per chip will drop to $10. This is a consequence of many factors, but mostly the rise in operational efficiency and manufacturing yields.

The major reason for increasing the size of a plant is to make full use of the lower unit cost that can be achieved at higher production volume, that is, economies of scale. Economies of scale exist when the factory's total capital and operating costs are increasing at a slower rate than its production volume.

Remember the old adage “You can't get something for nothing”? Well, there comes a point when you can't increase the output without making costs rise at an even faster rate. To take our earlier example, if you increase your output to 7000, the result may be that the cost per chip rises to $11. Increase the volume to 8000 and the cost per unit rises to $16 per chip. This rise comes because layers of management tend to grow as the workforce grows and because the burden of management increases as additional product types are assigned. Such “diseconomies of scale” explain the right-hand side of the U-shaped scale-to-cost curve [see graph, “TPS Lowers the Curve”].

Implementing TPS not only reduces the cost per unit at a given production volume, it also reduces the minimum number of units a fab needs to turn out to be cost-effective. That is, TPS moves the cost curve down and also broadens it.

Throughout the past 40 years, the only way to move the scale curve has been through the pursuit of Moore's Law, along with the enormous capital investment that this entails. Unfortunately, such spending pushes the curve not only down but also to the right [see graph, “New Opportunities for Profitable Growth”]. The result has been an increase in the minimum volume at which production is cost-effective. What all this means is that TPS will lower both the minimum cost and the volume of efficient production. When that happens, a lot of the great engineering ideas that have been shot down by the bean counters over the years will suddenly become attractive from a business perspective.