My prior series of posts (part 1, part 2, and part 3) proposed that a common characteristic of all engineering activity was the use of abstract models to search a solution space before implementing a new method, device, or system. Engineers fill the gap between science, which seeks to generate accurate models, and manufacturing, which aims to accurately replicate a given embodiment. Some engineers are more “hands-on” than others, so we have a spectrum of engineering activities that range from mostly abstract to mostly physical. It is this spectrum of engineering skills that I want to discuss. (Since the traditional engineering fields focus on the physical realm, I’m addressing that particular domain. However, the same spectrum issues will exist for engineers operating in alternate realms. See my prior posts for a discussion of engineering realms.)
There are many different kinds of engineers, but I’m going to constrain the conversation by dealing with just three types of engineering professionals: research, design, and production engineers.
Research engineers operate at a high level of abstraction. This necessitates that they be part scientist, and part applied mathematician, as well as an engineer with knowledge of the physical world. When striving to create models that more accurately represent physical world behavior, they take on the role of scientist. In creating such models, they tend to look for general behaviors that apply to all systems, or sets of systems, rather than the idiosyncratic behavior of individual implementations.
Researcher engineers do not normally need to extend the bounds of mathematics (although some do), but must have very strong mathematical skills to create and manipulate abstract models of physical behavior. A researcher in chemical engineering might, for instance, develop a new means for causing to plastics to decay in a ecological manner. The primary focus would likely be on a model that explains the decay mechanism, rather than how the method might someday be introduced into production.
Design engineers bridge the gap between high and low levels of abstraction. While they are free to consider a broad range of implementation methods, they must eventually deliver a design that will produce a desired result. They use existing models to examine possible solutions, rather than launching new experimental studies.
Rather than seeking a theoretically optimal result, as the researcher might, the design engineer must settle for a practical compromise between competing interests. Experience plays heavily into knowing how much weight to give to each design constraint. A thorough knowledge of manufacturing methods is also crucial to the design engineer, as the end design must allow for robust performance, whether for a single prototype, or for a product that will be reproduced millions of times.
A production engineer worries about keeping the manufacturing line operating in a smooth fashion. There is no need to worry about the theory of how the end product operates, or what it is going to look like–those decisions have been made upstream by the research and design engineers. Instead, the production engineer is mostly a troubleshooter, waiting for a production snag to arise, then resolving the issue in a manner that keep the problem from reoccurring.
While the research and design engineers can worry about more global issues, the production engineer has to be concerned about individual elements. Specific batches, machines, and output units each give indications as to the state of the manufacturing process. Coordination of corrective efforts, mixed in with the natural stress of high-volume manufacturing, requires that the production engineer be a strong communicator.
Just as there are differences between the various realms of engineering, each engineering job requires its own level of abstraction. In my next post, I’ll write more about the skill sets needed along the engineering spectrum, and their impact on engineering education.