Data-Driven Approach using Monte Carlo Method and Analytical Hierarchy Process for Decision-Making

Abstract

Information is a fundamental factor affecting the efficiency of decision-makers, explicitly the capacity of decision-making. Outcomes require to be analyzed and controlled from the datasets for deriving the precise value of the expertise. Hence, decision-makers are required to gain useful perspectives on the use of outcomes, which constantly evolving of high volume, velocity, variety, and value using data-driven approach analytics.

Decision support systems (DSS) supply information as analytic results to decision-makers, which vary during their proceedings period. Thereby, decision support systems are limited in terms of the capacity to provide useful information based on the data sources available.

The thesis aims to perform and improve different statistical approaches (Monte Carlo, and decision tree) and analytical hierarchy process (AHP) in the final decision-making by avoiding several biases during the contextual analysis of datasets realized from the decision support system. The technique (DSS) is an analytical tool used to modelling different uncertainties in data analysis and provides the ability for executive managers to access the correct and strategic decision-making from an analytical approach done in real-time. Therefore, the use of Monte Carlo method and analytical hierarchy process in the decision-making process appear as the principal vector capable to control the process and coordinate the entire system constituted to solve the issue.

Finally, a data-driven approach provides a real benefit to project management for correct decisions in terms of time and profit. Likewise, the method provides the correct approach to the implementation of different analytical tools in decision-making. Furthermore, considerable effort is required for planning, creating, deploying, maintaining, and continuously improving domain-specific big data processes for each company. Therefore, data-driven approaches used in the project management appear objective and rational for several technology possibilities to generate different concept analytics from the artificial intelligence.