# Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
## 1 Introduction
The purpose is to study and improve business processes using process mining tools and techniques at Oracle Financials based on the ERP procurement data of Pakistani organizations. Procurement is procuring goods and services, preparing and processing requirements, and final acceptance and approval for payment, as shown in Figure 1.  
The primary contribution of this study is to show how process mining techniques can be used to compute the alignments between event logs and process models and highlight both low-level and high-level deviations. This study applies process mining techniques in the field of the project procurement process. Given an event log and a Petri net, these metrics yield intuitive insights into the conformance between the log and the net, even if the log is non-fitting. The main objective of this research is to thoroughly analyze the provided ERP procurement log data to find an effective and efficient process model that shows the actual process flow of the organization along with frequently correlated sub-processes, optimum time frames calculated for the sub-processes for process flow, and detection of anomalies. Basically, procurement is the act of buying goods and services. This process includes the creation and processing of claims and the final receipt and approval of payments. It typically includes supply planning, standard determination, supplier research and selection, value analysis, financing, price negotiation, procurement, and other related functions. Process mining techniques and algorithms can be used to generate useful knowledge for organizations and help organizations improve business processes from ERP systems. Various process mining tools and techniques are applied to log data to find the technique that provides the best solution. In process mining, log data needs to be filtered and loaded into process mining software (such as PrOM, DISCO, etc.) for actual mining and process model reconstruction. After this process, the model can be used for its intended purpose. PrOM is a general-purpose open-source framework, which means it supports various plugins for various process mining techniques, such as the Alpha algorithm and its extensions. Another process mining tool is Disco [10]. It can easily transform and filter data, and it can handle large event logs and complex process models. Disco is used to automatically map logs with CSV and XLS extensions to XES or MXML format (powered by PrOM) to optimize performance and control deviations without informing the algorithm. In this work, the Disco performance view is used to find time delays in the procurement process. The PrOM plugin Conformance Checker is utilized for the initial validation stage, making it easier to compare the event log with the model. This comparison shows whether the algorithm's output, including aggregation and decomposition processes, is the desired outcome. Before this comparison, the models are manually transformed into Petri nets. As this is a process conformance-based case study for the procurement process, we have not compared it with any other study. Different process mining algorithms and techniques, i.e., fuzzy miner, \(\alpha\)-algorithm, heuristic miner, genetic algorithm [11], and colored Petri nets (CPN) have been applied to log data aimed at the discovery of processes from event logs used to find the optimal solution. Three qualitative and competence metrics, discovery, consistency, and enhancement, are introduced to evaluate the utility of the models. These metrics provide analysts with fast and reliable feedback on how representative the current model is relative to observed actual behavior. In discovery, process models are created from event logs without using a priori models, which means no additional information is used. In discovery, the alpha algorithm is used for model building. The generated model is called the initial process model, which can be crafted by hand. The conformance checking has an a priori model.