A sequential designing-modeling technique when the input factors are not equally important

The first thing springs to mind for understanding, forecasting, and improving the behavior of a complex system is a data-based model. This paper presents a sequential designing-modeling technique when the input factors do not have the same influence. The power of the combination of the design of experiments approach and modeling approach is investigated. The proposed technique adds the input factors to the process and designs and models them one after the other. At each step, one input factor is added based on its significance (impact), while each remaining input factor is set at its highest-influencing point (value). Ranking the factors in terms of significance and determining the point that has the highest effect for each factor are investigated. A comparison study between the new proposed sequential-stages technique (SeqST) and the classical single-stage technique (SinST) is given. The main results show that: (i) the performance of the SeqST is better than the performance of the SinST under different experimental conditions and scenarios, (ii) when there is a small number of training points in an experiment, there is a larger difference between the performance of the SeqST and the SinST than there is when there is a large number, (iii) when there are huge gaps between the importance of the factors in an experiment, there is a larger difference between the performance of the SeqST and the SinST than there is when there are small gaps, (iv) the SeqST has a much better performance using the correct order of the importance of the factors, and (v) the SeqST has a much better performance using a descending order of the numbers of the training points in the follow-up stages. In conclusion, for experiments with few trials and/or big gaps between the factors’ importance, it is highly recommended to use the SeqST with the ascending order of the factors’ importance and a decreasing order of the numbers of training points in the follow-up stages. This study gives a benchmark that guide experimenters to effectively designing and modeling their experiments.