ENGGEN 115 Chap.2 Concept Generation and Selection
Concept Generation and Selection
Concept generation expands the design space before evaluation narrows it. Functional decomposition and morphological thinking help create alternatives that differ in principle rather than surface detail. Selection then makes the decision traceable through requirement-derived criteria, ratings, weights and sensitivity checks.
In a strong response, the matrix supports judgement rather than replacing it: the team explains uncertain ratings, tests whether plausible weights change the ranking, and records why the selected concept deserves development. Constraints should screen out infeasible options before compensating scores are calculated.
Close totals deserve investigation rather than ceremonial ranking, especially when one uncertain rating drives the result. The chosen concept should leave selection with a clear risk question for analysis or prototyping.
What this chapter covers
- 01
Functional decomposition
- 02
Morphological combinations
- 03
Divergent ideation
- 04
Weighted decision matrices
- 05
Rating evidence
- 06
Sensitivity and robustness
Worked example · free
Select a portable display base
- +1Check that the criterion weights sum to 1.00: 0.30 + 0.45 + 0.25 = 1.00.
- +1Folded feet score 5(0.30) + 2(0.45) + 4(0.25) = 3.40.
- +1The water-fill base scores 3(0.30) + 5(0.45) + 3(0.25) = 3.90.
- +1The flat-pack truss scores 4(0.30) + 4(0.45) + 2(0.25) = 3.50.
- +1Recommend the water-fill base provisionally, then test whether transport and filling assumptions justify its ratings.
Key terms
- Functional decomposition
- Breaking an overall purpose into smaller functions that can be solved and recombined.
- Morphological chart
- A table of possible means for each function, used to assemble distinct system concepts.
- Selection matrix
- A structured comparison of concepts against stated criteria and a consistent rating scale.
- Criterion weight
- A numerical expression of a criterion's relative importance in a particular decision.
- Sensitivity check
- A test of whether reasonable changes to uncertain ratings or weights alter the preferred option.
Concept Generation and Selection FAQ
How different should generated concepts be?
Aim for differences in operating principle, structure, energy path, interface or assembly, not merely colour or styling. Functional decomposition helps because each sub-function can be met in several ways. Combining those means creates alternatives with genuinely different trade-offs and gives selection a meaningful job.
When is a weighted matrix misleading?
It misleads when criteria overlap, weights lack stakeholder justification, rating directions are inconsistent, or unsupported numbers create false precision. It can also hide a fatal constraint because high scores elsewhere compensate for failure. Screen non-negotiable constraints first, then use the matrix for viable concepts.
What should I write after calculating scores?
Interpret the margin and uncertainty. Identify which criteria drive the result, whether a close second deserves further testing, and what change in weight or rating could reverse the ranking. Finish with a development recommendation linked to the brief rather than simply declaring the largest total the winner.
How do morphological charts improve creativity?
They separate the overall system into functions and list several means for each one, which prevents the first complete object from controlling the search. Combining means can reveal unusual architectures. Compatibility still matters: flag combinations that physically conflict, but retain unfamiliar feasible paths long enough for comparison.
Should every criterion have a numerical weight?
Use a weight only when relative importance is meaningful and can be explained from the brief or stakeholder evidence. Some conditions are absolute constraints and should be screened rather than traded. If importance is uncertain, compare plausible weight sets and report whether the recommendation survives instead of pretending one set is exact.
Exam move
Choose a simple device and write its verb-noun functions before sketching. Force at least three means for each function, then combine them into concepts that differ structurally. Build a small matrix with documented rating reasons and perform one break-even or alternative-weight check. Review whether any constraint was incorrectly treated as a compensating criterion.
This routine trains both divergence and accountable convergence. Repeat the exercise with another person's criteria and compare why the ranking changes. Create anchored descriptions for scores one, three and five before rating anything, then hide the totals until every reason is recorded.
For a close result, calculate the smallest rating or weight change that reverses the winner and turn that fragile input into a prototype or analysis question. Finish by drawing the chosen concept and annotating which feature answers each leading criterion. Keep one discarded path and explain why it failed a constraint or compatibility rule.
This makes rejection criteria explicit and prevents the next ideation round from repeating the same structural conflict under a new appearance. Store rating evidence beside the matrix so totals never become detached from their reasons. Recheck every criterion against the problem specification before finalising weights.