The objective of this paper is to detail preliminary work revolving around modeling. It provides understanding and underpinning implementation procedures of dynamics of large-scale events with Hajj examples, where a large population of people is contained for a significantly long but limited period within certain areas. It is essential to note further that the motivation behind this subject’s discussion could also be fueled by sales, inquiries, or security concerns. However, knowledge emergence on service point procedures implementation suggests that service points implementing data are extinct, and this is obliged to implement the next feature. As such, there is a critical need to reform a process and how to analyze the work. Developing this literature report requires extensive use of factual data for accuracy; as such, data mining and simulation techniques will be essential in explaining what services are needed. The simulation techniques used herein incorporate several databases targeting to exploit the advantage of proficiency in predicting distribution demand for population points based on available current estimates. Henceforth, data mining, in this case, is used to inform intelligent decision making on investing in services points as pushed for by customers’ demand.

This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.