Prioritizing Process Over Results

In the fast-paced world of engineering, it's easy to fixate on outcomes—delivering a product on time, hitting performance metrics, or solving a crisis before it escalates. While results are undeniably important, they often overshadow the foundational element that leads to sustainable success: the process behind decision-making. This process isn't just a checklist; it's a disciplined approach to problem-solving that transforms ambiguous inputs into clear, actionable insights.

Engineering problems rarely arrive neatly packaged. They come with vague requirements, conflicting stakeholder inputs, and unseen variables that can derail even the most innovative ideas. A results-oriented mindset might push one to jump straight to solutions, leading to shortcuts, overlooked risks, or poor outcomes. In contrast, a process-focused approach ensures decisions and outcomes are robust, repeatable, and adaptable.

New engineers often enter the workforce with strong technical skills from academia but they may lack the contextual awareness that comes with experience. They might view problems through a narrow lens, focusing on immediate fixes to demonstrate quick wins. This can work for straightforward tasks but falters in complex scenarios where ambiguity reigns.

Senior engineers, on the other hand, treat every decision as an opportunity to build a comprehensive understanding. They've learned that skipping steps, like fully scoping a problem, leads to downstream headaches. For them, the process is second nature: a mental framework that filters noise, identifies key variables, and tests assumptions rigorously. This isn't about being slower; it's about being smarter.

Step-by-Step Process for Effective Engineering Decision Making

1) Gather and Clarify Inputs

Start with the raw inputs: stakeholder requirements or feedback, an idea, a failure, an alternative. These are often ambiguous. "The device is failing prematurely" or "We want an alternative assembly process that is easier to follow." Don't assume you must implement an idea or solve a problem without clarity; actively seek details.

Ask questions like:

• What are the symptoms and how do they relate to each other?

• Who is affected and how large is the impact?

• What constraints exist such as budget, timeline, materials, or regulations?

  
  

This stage sets the foundation. Without it, you risk solving the wrong problem.

2) Evaluate Relevance and Impact

Not all inputs are equal. Assess each for relevance and potential impact. Consider factors like:

• Regulatory and Compliance

• Functional Requirements: How does this affect performance, usability, or integration with other systems?

• Reliability and Safety: What are the failure modes? Use tools like FMEA (Failure Mode and Effects Analysis) or Failure Mode Analysis (FMA) to quantify risks, if applicable.

• Economic and Environmental Factors: What's the cost-benefit? How does it align with company goals?

Also, consider your own level of knowledge. Don't hesitate to brush up. Review datasheets, research papers and textbooks, or consult colleagues. This evaluation refines the problem, highlighting what's critical versus peripheral.

3) Define the Scope Fully

With inputs filtered, articulate the problem's boundaries. Create a clear scope document outlining objectives, assumptions, constraints, and success criteria. This step prevents scope creep and ensures everyone is aligned. It's where ambiguity dissolves into a well-defined challenge.

4) Develop Hypotheses

Based on your analysis thus far, generate one or more hypotheses about the problem's cause or the idea's viability. A hypothesis should be measurable (either via testing, analysis, or both) and specific. "The failure is due to intermittent connectivity caused by thermal cycling weakening solder joints." Hypotheses bridge understanding and action. They encourage creative thinking while grounding it in evidence. For complex issues, propose alternative hypotheses to avoid tunnel vision.

5) Conduct Hypothesis-Driven Testing

Finally, validate through targeted experiments (preferable) or analyses. Design tests that isolate variables with clear assumptions. "It is assumed the solder joints are subjected to temperature fluctuations between -10°C and 60°C during normal operation." Be ready to justify these assumptions. Use simulations, prototypes, or field trials as appropriate. Document everything.

This isn't about proving yourself right, it's about learning. With a sound process-oriented approach, if a hypothesis is rejected, it is not a failure of the engineer. Iterate back to earlier steps. The goal is validation, not vindication.

Example

To illustrate, imagine debugging a drone propulsion system. One path a process-oriented engineer could take is:

• Gather inputs: Flight logs show erratic behavior in humid conditions.

• Evaluate impact: Reliability is key for commercial use; check IP ratings and material specs, gather assembly data and evaluate process, etc.

• Define scope: Isolate factors to those influenced by or related to humidity. Consider coincidental alternatives such as improper voltage or assembly error. Clearly document.

• Hypothesize: Moisture ingress is corroding contacts. Document others as well. Prioritize most likely or significant.

• Test: Evaluate faulty units for corrosion damage. Simulate humidity cycles and measure resistance across contacts.

In Conclusion

Shifting from results to process requires an upfront commitment, but the payoff is immense: fewer mistakes, deeper insights, and better results. For new professionals, practicing this framework on small tasks builds confidence and over time, it becomes intuitive. By investing in process, you're not just solving today's problems but equipping yourself and your team for tomorrow's challenges.