The dynamic corporate environment has resulted in enterprise practitioners exploring optimal measures towards enhancing business sustainability and competitiveness. The statistical factorial technique is effective for experimenting with business process variables resulting in the optimal execution of business processes. An ideal factorial model design integrates business process variable variations, main effects together with interactions. A factorial model for predicting the impacts of business process variables on business processes is developed. Factorial methodologies serve as an effective tool for simulating scenario responses. The scenarios result in the desired response based on selected business variable effects and interactions. A framework for setting and adjusting a series of selected independent business process variables to obtain a dependent response (business process turnaround time) is developed. The factorial model allows for configurations of defined key performance identifiers based on selected defined metrics to quantitatively determine effects of each business process variables on enterprise operations.

This research develops a factorial model design for predicting and evaluating what effect changing or combining business process variables would have on corporate operations. A factorial model case effectively quantifying impacts of selected business process variables on business change is developed. Results present a ranked layout of business process variable impacts on business processes based on distinct effects and interactions. Developed factorial model is efficient for large multinationals exploring the current and future status of business processes based on the impacts of selected business process variables.