“Knowledge is power.” — Francis Bacon
As we move from the industrial age into the information age, knowledge is becoming an ever more central force behind the competitive success of firms and even nations. Nonaka has commented, “In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge.”1 Philosophers have analyzed the nature of knowledge for millennia; in the past half-century, cognitive and computer scientists have pursued it with increased vigor. But it has turned out that information is much easier to store, describe, and manipulate than is knowledge. One consequence is that, although an organization’s knowledge base may be its single most important asset, its very intangibility makes it difficult to manage systematically.2
The goal of this paper is to present a framework for measuring and understanding one particular type of knowledge: technological knowledge, i.e., knowledge about how to produce goods and services. We can use this framework to more precisely map, evaluate, and compare levels of knowledge. The level of knowledge that a process has reached determines how a process should be controlled, whether and how it can be automated, the key tasks of the workforce, and other major aspects of its management. Better knowledge of key variables leads to better performance without incremental physical investment.
Two examples illustrate the importance of technological knowledge in the form of detailed process understanding. Chaparral Steel, a minimill, was able to double output from its original electric furnace and caster. Semiconductor companies routinely increase yields on their chip fabrication lines from below 40 percent to above 80 percent during a period of several years. In these cases, the incremental capital investments are minimal. The improvements are instead due to multiple changes in the manufacturing process, including different procedures, adjustments of controls, changes in raw material recipes, etc. Why weren’t these changes implemented at startup? The reason is that the knowledge about the process and how to run it is incomplete and develops gradually through various kinds of learning.
Many authors have noted that there is a difference between data and information. A few have also noted that there is a difference between information and knowledge.3 Although not always clear-cut, the distinction among the three in production processes is very important. Data are what come directly from sensors, reporting on the measured level of some variable.