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Resolution V aliases only three-factor interactions — which in a finishing process are almost never real.
Total Runs Required
—runs
Fractional design, replicated, plus centre points
Design Economics
Runs in Base Design
—no.
Design Resolution
—no.
Main Effects & Interactions
—no.
Total Experiment Cost
—/experiment
Total Experiment Time
—h
Saving vs Full Factorial
—/experiment
The resolution formula is a heuristic that holds for the standard minimum-aberration designs and can overstate what an arbitrary fraction achieves — the alias structure of the specific generator is what matters and must be checked before running anything. Effects counted as estimable assume the resolution supports them, which fails below Resolution V where two-factor interactions confound with each other. Centre points detect curvature but do not let you model it; a significant curvature result means the design must be augmented to a response surface. Randomisation of run order is not optional and is not represented in any of these numbers.
Using this calculator
About the Design of Experiments Factor & Run Planner
The formula
This is the expression the tool evaluates. Every term is named underneath, with the unit it must be supplied in.
Each input feeds the expression evaluated in the browser; the symbol table below names every term and its unit.
Symbols used above
Symbol
Stands for
Unit
factors
Factors
k
fractionExponent
Fraction Exponent
p
replicates
Replicates
no.
centrePoints
Centre Points
no.
costPerRun
Cost per Run
/run
timePerRun
Time per Run
h
totalRuns
Total Runs Required
runs
fractionalRuns
Runs in Base Design
no.
resolution
Design Resolution
no.
estimableEffects
Main Effects & Interactions
no.
totalCost
Total Experiment Cost
/experiment
totalTime
Total Experiment Time
h
savingVsFullFactorial
Saving vs Full Factorial
/experiment
How the result is derived
Step by step, from the values you type to the figure on screen.
The 6 inputs are read from the form on every keystroke: Factors, Fraction Exponent, Replicates, Centre Points, Cost per Run and Time per Run.
Each value is checked against the accepted range in the input table below. A value outside its range stops the calculation rather than producing a misleading figure — the results blank out and a message appears.
The validated values are substituted into the expression above, which resolves Total Runs Required together with every supporting figure in one pass — no value is carried over from a previous entry.
The supporting outputs — Runs in Base Design, Design Resolution, Main Effects & Interactions, Total Experiment Cost, Total Experiment Time and Saving vs Full Factorial — come from the same pass, so they always describe the same case as the headline figure.
Results are rounded for display only. The full-precision value is used throughout the chain, so reading a rounded intermediate figure back into the tool by hand can shift the last digit.
What each input means
Where to read each value on the floor, the unit it must be in, and the range the tool accepts.
Input
Unit
Accepted range
Default
What it means
Factors
k
2 to 10 k
5
Fraction Exponent
p
0 to 5 p
1
0 is a full factorial; 1 halves the runs, 2 quarters them.
Replicates
no.
1 to 10 no.
2
Centre Points
no.
0 to 20 no.
3
Cost per Run
/run
1 to 20000 /run
180
Time per Run
h
0.1 to 100 h
2.5
What the tool returns
The headline figure and every supporting value it is built from.
Output
Unit
What it tells you
Total Runs Required (headline result)
runs
Fractional design, replicated, plus centre points
Runs in Base Design
no.
Design Resolution
no.
Main Effects & Interactions
no.
Total Experiment Cost
/experiment
Total Experiment Time
h
Saving vs Full Factorial
/experiment
Worked example
Given
Factors
5 k
Fraction Exponent
1 p
Replicates
2 no.
Centre Points
3 no.
Cost per Run
180 /run
Time per Run
2.5 h
The tool loads with this case already solved — the Total Runs Required shown above is its answer. Change one value and the difference from this baseline is the sensitivity of the result to that variable.
How to use it
Work through the input groups in order — Design and Run Economics. The defaults are a realistic case, so you can change one value at a time and watch what moves.
There is no calculate button. Every figure recalculates as you type or drag, which is what makes this usable for a what-if sweep rather than a single answer.
Read Total Runs Required in the dark results panel — that is the headline figure, expressed in runs.
Check the supporting rows underneath (Runs in Base Design, Design Resolution, Main Effects & Interactions, Total Experiment Cost, Total Experiment Time and Saving vs Full Factorial) before acting on the headline — they are where an implausible input usually shows itself first.
Reset to defaults returns every field to the reference case, which is the quickest way to check whether a surprising result came from the tool or from an input you had changed earlier.
Where this is used
Process planning — establishing Total Runs Required before a trial is booked, so machine time and material in Product Engineering, Specifications & Feasibility are committed against a calculated figure rather than an estimate.
Costing and quotation — Total Runs Required is an input to the cost sheet, and quoting from a worked number rather than a remembered one is what keeps a margin intact.
Troubleshooting — when the floor result drifts from plan, entering the measured values (starting with Factors) shows how much of the gap in Total Runs Required each variable explains.
Teaching and study — the accepted ranges bracket normal Product Engineering, Specifications & Feasibility practice, so moving one variable at a time shows the shape of the relationship rather than a single answer.
Assumptions and limits
The resolution formula is a heuristic that holds for the standard minimum-aberration designs and can overstate what an arbitrary fraction achieves — the alias structure of the specific generator is what matters and must be checked before running anything. Effects counted as estimable assume the resolution supports them, which fails below Resolution V where two-factor interactions confound with each other. Centre points detect curvature but do not let you model it; a significant curvature result means the design must be augmented to a response surface. Randomisation of run order is not optional and is not represented in any of these numbers.
Every input is bounded to the range normal practice occupies (Factors 2 to 10 k, Fraction Exponent 0 to 5 p and Replicates 1 to 10 no., and so on for the rest). Those bounds are guard rails against typing errors, not a claim that the formula fails one unit outside them.
The calculation is deterministic: the same inputs always give the same result. It carries no allowance for machine condition, operator skill, ambient conditions or lot-to-lot material variation unless an input above explicitly represents one.
Nothing is sent anywhere. The maths runs in your browser, so the numbers you type never leave the page.
Questions people ask
What do I need to know before using the Design of Experiments Factor & Run Planner?
Have these to hand: Factors, Fraction Exponent, Replicates, Centre Points, Cost per Run and Time per Run. With those entered, the tool returns Total Runs Required immediately.
What exactly is Total Runs Required?
Fractional design, replicated, plus centre points. It is reported in runs. It is derived from Factors, Fraction Exponent, Replicates, Centre Points, Cost per Run and Time per Run, and is the figure the rest of the Product Engineering, Specifications & Feasibility calculation is built around.
Which units does this calculator expect?
Enter Factors in k, Fraction Exponent in p, Replicates in no., Centre Points in no., Cost per Run in /run and Time per Run in h. Mixing unit systems is the most common cause of a result that looks an order of magnitude wrong — convert before typing, not after reading.
What are the other figures under the main result?
They are the intermediate quantities the calculation passes through: Runs in Base Design, Design Resolution, Main Effects & Interactions, Total Experiment Cost, Total Experiment Time and Saving vs Full Factorial. They are shown because a headline number nobody can trace is a number nobody trusts — checking them against your own expectation is the fastest way to confirm the inputs were read as you intended.
Can I rely on this for a production decision?
The resolution formula is a heuristic that holds for the standard minimum-aberration designs and can overstate what an arbitrary fraction achieves — the alias structure of the specific generator is what matters and must be checked before running anything. Effects counted as estimable assume the resolution supports them, which fails below Resolution V where two-factor interactions confound with each other. Centre points detect curvature but do not let you model it; a significant curvature result means the design must be augmented to a response surface. Randomisation of run order is not optional and is not represented in any of these numbers. Treat the output as an engineering estimate that narrows the trial window, not as a substitute for the trial.