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OFAT vs DOE? OFAT or One Factor at a Time is a method in which the impact of change in one factor is studied on the output when all the other factors are kept constant. DOE or Design of Experiments is a method in which the impact of change in factors is studies on the output when all factors can be changed at the same time. Similarity in both techniques 1. Both require experiments to be conducted 2. Both are statistical techniques. Solutions identified from these need to be checked for practical or business sense as well Differences in both techniques 1. In OFAT, only 1 factor can be changed while in DOE, all factors can be changed in a single experiment 2. DOE can be used to screen the critical factors from among a list of multiple factors and can also be used to optimize the factors for a desirable output. On the other hand, OFAT can only be used for screening of critical factors 3. OFAT will only tell the main effect of the factor on the output. DOE will tell us both about the main effect and interaction effects (i.e. the combined effect of 2 or more factors) on the output 4. In OFAT, the project lead can decide the number of experiments that they want to do. DOE will give us the number of experiments that are required (basis the fractional or full factorial design) It is a well established fact that DOE is superior to OFAT as it can help you change multiple factors at the same time and hence allows to study the impact using less number of experiments. However, the question is that whether there is a need to change multiple factors? E.g. Let us assume the mileage of the car as the output. There are multiple inputs for this (limiting to 5 for explanation) Mileage = f(Car Condition) Mileage = f(Road Condition) Mileage = f(Fuel Type) Mileage = f(Way you drive) Mileage = f(Resistance between tyres and road) Now if a car manufacturer wants to understand which of the factors is important for mileage, they will definitely prefer DOE over OFAT. They will be able to identify the critical factors and also optimize the value of critical factors to get maximum mileage. Now, consider my situation. I have only one car (10 years old), I take the same route to office everyday, i have a fixed driving style and the tyres are also in good condition. The above things mean that except for Fuel Type every other factor is almost constant. Now if I need to maximize the mileage of my car, I dont need a DOE. I can simply do a OFAT. This is precisely what I did. I have a BP station where I refuel my car. I experimented with the Speed (97 octane) fuel as compared to the normal fuel. Now common sense would suggest that there will be a statistically significant change in the mileage. However, when i did OFAT testing, the mileages were not different (may be the car engine is old and higher octane makes no difference) and I could continue to use the normal petrol and save by not spending extra for Speed. The point that I want to highlight is that if experimentation does not cause much and you can reasonably assume the other factors to be constant, then OFAT is also useful. Otherwise, it is well established that DOE is advantageous over OFAT. P.S. The data for my fuel test is available on request (though I will have to dig it out from the hard-disk).

One Factor at A Time Design of Experiments In OFAT, we hold 1 factor as constant and alter 2nd variable level Multiple (more than 2 factors) can be manipulated It is sequential, one factor at a time Simultaneous with multiple factors Experimenter can decide upon the number of experiments to be conducted In DOE, number of experiments is selected by the design itself We CANNOT estimate interactions among the Factors Systematically interactions are estimated Design is Experimenters decision Factorial designs (Full and Fractional) Low precision in OFAT With regards to Precision, in designed experiments the estimates of each factor is High High chances of False optimum (when 2+ factors considered) which can mislead High chances of optimization Used to estimate curvature in factors If there is curvature, estimation is done by augmenting into central composite design Domino effect, If one experiment goes wrong resulting in Inconclusiveness Orthogonal design, easy to predict and make conclusions It is sensible to say DOE is superior over OFAT, as we can save time and don’t have to perform multiple tests / experiments. Let’s see how Designed Experiments take an upper hand against OFAT with an example. Let’s run an example for 3 factors in 15 runs Few interpretations, with reference to above diagram In DOE, we can estimate the interactions between the factors but not in OFAT In DOE, prediction is better as the experimental runs have better data spread compared to that in OFAT with same number of experimental runs Curvature determination is better as it covers entire spectrum in DOE compared to OFAT and for that matter Response Optimisation is also better in designed experiments.