A process is defined as a series of actions or steps taken in order to achieve a particular end. Currently, there are a wide range of studies involving different types of processes ranging from engineering, business, biology, to information theory. We are interested in a new type of process study, labelled as Process Based Strategy Model. The process specifically looks into the success probability of an n-step process with n independent step probabilities. In our model, there are exactly n steps that lead to the desired goal Xn a success in step i leads to step i + 1 but a failure in it only leads to goal Xi – 1 and thereby, a failure in achieving the end goal Xn.
We want to maximize the success probabilities of each step in order to assure the fulfillment of the end goal Xn. We accomplish this by developing theorems that adjust the success probabilities of each process steps. Another method of achieving our objective is by replacing a certain step of the process with one or more steps which results to a higher overall success probability. We also used some functions which possibly model real-life variables to correspond to success probabilities. Lastly, we apply the process based strategy model on a scenario which shows how elements of a population are moved through the process with the concepts of saturation and cycles.