Screen 5–7 factors in a handful of runs. Build a 2-level fractional design, see its resolution and alias structure, then enter your results to rank the effects — all client-side.
Set your factors on the left and press Generate Design to build a fractional run sheet with its alias structure.
Watch: 2^k Factorial DOE: Test Multiple Changes Together
You have many factors but limited runs. Enter the lab to see how a fractional design screens out the vital few with a fraction of the experiments.
Pick how many factors to screen (5–7) and how few runs you can afford. The calculator builds a 2-level fractional design using standard generators and names each factor's low and high levels.
It works out which effects get mixed together in this smaller design (called confounding) and gives the design a resolution rating — so before you run a single trial, you know exactly which effects you can trust and which ones are tangled up with each other.
Run your experiment in random order, record the result for each test, and the calculator shows which factors have the biggest effect (by comparing the average result when each factor is 'on' versus 'off'). This tells you what to focus on.
A fractional factorial design lets you screen many factors in a fraction of the runs a full factorial would need. It is the workhorse of the early Analyse and Improve phases, where the goal is to separate the vital few factors that matter from the trivial many before committing to a detailed study.
A fractional factorial runs a carefully selected fraction — a half, a quarter, an eighth — of all the combinations a full factorial would test. By giving up the ability to estimate high-order interactions (which are usually negligible), you screen many factors in very few runs. A 2⁵⁻¹ design tests five factors in just 16 runs instead of 32, and a 2⁷⁻⁴ design screens seven factors in only 8.
A fractional design runs only a carefully chosen half (or quarter) of all the possible factor combinations, so you can test many factors in far fewer runs. The trade-off is 'confounding': because you skipped runs, some effects get tangled together and cannot be told apart. The design's 'resolution' is a grade for how serious that tangling is — higher resolution means the main factor effects stay clear of one another. Use a fractional design to screen lots of factors quickly, find the vital few, then run a fuller experiment on just those to confirm the details.
An engineer wants to test temperature, pressure, speed, and humidity — each at two settings. A full test of every combination would need 16 runs. A fractional factorial design cuts this to 8 carefully chosen runs.
The results still identify temperature and speed as the two factors that matter most, with pressure having almost no effect.
What to do with this: Focus improvement on temperature and speed only. You got 80% of the insight from 50% of the effort — that's the whole point of fractional factorial.
Experiments cost time, material and machine capacity. Fractional designs let a team learn which factors actually drive a result in a handful of runs rather than dozens, freeing budget for the deeper studies that follow. They turn "we think it might be temperature" into hard, ranked evidence.
Reach for a fractional factorial in the Analyse and early Improve phases of DMAIC, whenever you have many candidate factors and limited runs. It is the standard screening tool: identify the vital few, then switch to a higher-resolution or full factorial, or a response surface design, to optimise them.
DOE is a core Black Belt skill, and fractional factorials are where most real projects start because of their efficiency. Reporting resolution and alias structure alongside the ranked effects shows tollgate reviewers that your conclusions account for confounding rather than ignoring it.
Once the calculator ranks your effects, separate the vital few large effects from the trivial many. Check the alias of any big effect to be sure you are crediting the right cause. Then run a follow-up experiment on the survivors — a higher-resolution or full factorial to confirm interactions, or a response surface design to find optimal settings. Fix the unimportant factors at their most convenient or cheapest levels and document the decision.
Combine fractional factorial screening with full factorial and response surface studies inside a structured DMAIC project.
A full factorial tests every combination of your factors — 2^k runs for a 2-level design, so a 7-factor study needs 128 runs. A fractional factorial runs a carefully chosen fraction (2^(k−p)) of those combinations, for example 16 runs instead of 128. You give up the ability to estimate every interaction cleanly, but in return you screen many factors with far fewer runs — ideal when experiments are expensive and most high-order interactions are negligible.
Resolution describes how badly effects are tangled together (aliased). In a Resolution III design main effects are aliased with two-factor interactions, so use it only for rough screening. In Resolution IV main effects are clear of two-factor interactions but those interactions are aliased with each other. In Resolution V main effects and two-factor interactions are all estimated cleanly — the best fractional designs. Higher resolution means less confounding.
Aliasing means two or more effects are estimated by the same column of the design, so the calculator cannot tell them apart — their estimates are added together. It arises because you ran only a fraction of the full design. The alias (confounding) structure lists which effects share a column. When an important effect appears large, you check its aliases to decide whether the main effect or an interaction is the true cause, often with a small follow-up experiment.
Use a screening (fractional) design early in an investigation when you have many candidate factors — typically five or more — and want to find the vital few that actually matter before spending runs on a detailed study. Screening relies on the sparsity-of-effects principle: usually only a handful of factors dominate. Once screening identifies them, follow up with a higher-resolution or full factorial, or a response surface design, to optimise the survivors.
A fractional factorial points you to the vital few factors. Structured training shows you how to confirm and optimise them in a full DOE study.
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Want to screen many factors in a handful of runs inside a real improvement project? The Black Belt covers fractional factorial designs, screening experiments, and the advanced Design of Experiments toolkit.
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