Making goods evolved over several centuries from craft production to complex and highly automated manufacturing processes. A companion paper by R. Jaikumar documents the transformation of firearms manufacture through six distinct epochs, each accompanied by radical changes in the nature of work. These shifts were enabled by corresponding changes in technological knowledge. This paper models knowledge about manufacturing methods as a directed graph of cause–effect relationships. Increasing knowledge corresponds to more numerous variables (nodes) and relationships (arcs). The more dense the graph, the more variables can be monitored and controlled, with greater precision. This enables higher production speeds, tighter tolerances, and higher quality.
Changes in knowledge from epoch to epoch tend to follow consistent patterns. More is learned about key classes of phenomena, including measurement methods, feedback control methods, and disturbances. As knowledge increases, control becomes more formal, and operator discretion is reduced or shifted to other types of activity. Increasing knowledge and control are two dimensions of a shift from art towards science.
Evolution from art to science is not monotonic. The knowledge graphs of new processes are riddled with holes; dozens of new variables must be identified, understood, and controlled. Frederick Taylor pioneered three key methods of developing causal knowledge in such situations: reductionism, using systems of quantitative equations to express knowledge, and learning by systematic experimentation.
Using causal networks to formally model knowledge appears to also fit other kinds of technology. But even as vital aspects of manufacturing verge on “full science,” other technological activities will remain nearer to art, as for them complete knowledge is unapproachable.