Enhance Decision-Making With Control Systems

This text is a part of our unique career advice collection in partnership with the IEEE Technology and Engineering Management Society.

A lot of engineering is decision-making. Engineers make choices about product design, program administration, know-how street maps, analysis instructions, management of technical groups, and extra.

As a previous president of the IEEE Control Systems Society and now the 2026 president-elect of the IEEE Technology and Engineering Management Society, in addition to holding management positions in trade and academia, I’ve thought so much in regards to the connections between control systems and technology management.

The secure, dependable efficiency of airplanes and spacecraft, automobiles and vans, properties and buildings, chemical crops and manufacturing amenities, communication and monetary networks, and lots of different advanced programs depends on automation and control systems. However, as I talk about right here, the ideas of management engineering are additionally related to human decision-making in know-how administration.

Whether or not in engineering or administration, uncertainties are pervasive. Within the case of the latter area, we are able to by no means make certain about innovation processes, market projections, and folks’s personalities and capabilities. Certainly, the uncertainties could seem so overwhelming that some is perhaps tempted to decide by flipping a coin.

However most choices should not made randomly, and management engineering affords insights for managerial decision-making below uncertainty.

Psychological fashions and uncertainty

We depend on psychological fashions—our data, beliefs, assumptions, experiences, observations, and reasoning. However fashions of any selection should not actuality. They’re correct approximations at greatest, and so they’re utterly incorrect at worst. It’s important that every one decision-makers acknowledge the discrepancies between their psychological fashions and actuality, after which take motion to scale back the mismatch.

Let me draw an analogy from management engineering. To develop a management system for an plane, for instance, mathematical fashions—not the psychological selection—are developed of the airplane’s airframe. For numerical accuracy, the fashions require “enough excitation,” which suggests offering quite a lot of inputs, similar to deflections of flight control surfaces, and measuring how the airplane reacts to them.

Primarily based on that information, fashions of the required accuracy could be created and integrated into the flight controller design. The info should be wealthy sufficient in order that related alerts can rise above irrelevant noise.

Choices are not often one-and-done affairs. Main a workforce, managing a undertaking, allocating sources, and endeavor a design all require common interactions with others, with preliminary choices adjusted frequently over time.

The identical applies to psychological fashions for human decision-making. Monitoring regular day-to-day operations of a company or a undertaking seemingly wouldn’t present info of a excessive sufficient signal-to-noise ratio for psychological fashions to be reliably up to date.

As an alternative, particular duties and conditions could be instrumental in attaining the purpose. For instance, a supervisor might give a difficult activity to a workforce member primarily to enhance the supervisor’s psychological mannequin of the worker, fairly than to deal with a urgent organizational want. The improved psychological mannequin will help the chief decide one of the best position for the worker when an precise difficult state of affairs arises.

No matter effort, psychological fashions won’t ever be good. There’ll at all times be uncertainty. So, one essential lesson for decision-makers to remember is that no matter you realize, you solely assume you realize. Resist the temptation to consider you actually know the reality.

As a decision-maker, the objects of your psychological fashions embrace your group, different stakeholders, and the exterior surroundings. However additionally they embrace your self-model. It is advisable to have a transparent understanding of your personal capabilities, preferences, and circumstances. Examples embrace your workload, the tempo at which you’re employed greatest, your flexibility in mild of different priorities, and what motivates you. And, in fact, you should admire that your self-models are unsure, too.

Individuals usually don’t know themselves in addition to they assume they do. Be sincere with your self, and ask for feedback from trusted colleagues and friends. Don’t react defensively; hearken to the suggestions, then mirror. Doing so can strengthen your understanding of your self.

Dynamics and decision-making

Typically the results of a call aren’t instantly obvious. It may possibly take days and even years for that to occur. Within the meantime, observations can present a sign of the results, however they may be incorrect. In management idea, for instance, we train the idea of inverse response, the place the preliminary response to a call is the alternative of the ultimate impact.

A easy instance is what occurs to an organization’s income if it considerably will increase its analysis and improvement funding. For the following few quarters, income seemingly will probably be decrease due to the R&D bills. As soon as new merchandise roll out, profitability in all probability will improve.

A supervisor who doesn’t acknowledge the non permanent inverse response pattern and cuts R&D sources can worsen fairly than enhance issues by sacrificing the long-term vitality of the corporate. Such short-sighted choices occur all too usually.

Choices are not often one-and-done affairs. Main a workforce, managing a undertaking, allocating sources, and endeavor a design all require common interactions with others, with preliminary choices adjusted frequently over time.

These dynamics should be thought of in advanced decision-making conditions. The changes are based mostly on monitoring the exercise, thereby closing the suggestions loop.

Time delays could be particularly tough to handle. As famous, choices made about initiatives and processes take time to have an effect. Delays may result from varied sources together with communication points, new insurance policies, staffing issues, procurement occasions, and reporting processes.

To be an efficient decision-maker, your psychological mannequin ought to embrace estimates of delays. The problems arising from unanticipated setbacks in suggestions processes are well-known, each in management engineering and systems engineering. The power to anticipate delays—and, the place potential, to scale back them—is a precious ability for decision-makers.

Connecting the dots

The interconnections among the many ideas of psychological fashions, uncertainty, dynamics, and suggestions are deep and engaging. The insights they provide for decision-making are quite a few.

One instance is the robustness-performance tradeoff in management engineering. The tradeoff refers to the truth that the very best ranges of efficiency can’t be attained whereas concurrently being strong throughout occasions of excessive uncertainty. This perception is the premise of the “no free lunch” theorem in optimization, that means that no decision-making method could be optimum in all conditions.

When uncertainty ranges improve from a mismatch between a psychological mannequin and actuality, the presence of noisy information, or exterior disturbances, decision-making needs to be much less aggressive. As an alternative, it is best to reply by making gradual modifications and ready for suggestions alerts. To paraphrase, the extra unsure the state of affairs, the extra one ought to hedge one’s bets.