# A Petri Net Approach for Business Process Modeling and Simulation
###### Abstract
Business processes must somehow be raised in a context that can be understood by people; if the definition of process is \"a list of related activities that seek to obtain the objectives of the organization\", then a list of activities could describe the process. If this list is shown as a workflow in which the order of their execution is indicated, then the process can be made more understandable, leaving abstraction aside. Consequently, we understand that a model is a representation and that modeling is the creation of a model. This concept is significantly important, as a clear understanding of the process is the starting point for the creation of IT systems, simulation studies, and ways to improve or implement processes for an organization, for example sales control, collection, storage, etc. These issues have led to interest in the study of BPM. Process modeling differs from process design in that design must consider the organizational scheme in assigning tasks, the technology being used, and even the budget that must be allocated for its operation. For this reason, it is important to clarify that design is not modeling [7]. The concept of process mining is related to data mining, and is used to discover, validate, and improve workflow behaviors. By combining data mining and process analysis, organizations can extract information from the registry of their information systems and understand their performance, thereby discovering areas for improvement. Mining uses a data-based approach for process optimization, which allows leaders to make decisions without ambiguity in the understanding that their information is current, reliable, and trustworthy. One of the functions of process mining is model discovery. This implies the implementation of certain algorithms that convert an event record into a model that is capable of representing the recorded behavior. However, in the present work we do not try to create or discover a new process, as the focus is that a graphic model is obtained starting from a process described in natural language and with certain writing rules that allow the behavior of each one of its activities to be simulated. Process mining has several discovery algorithms, including the Alpha algorithm, which produces a Petri net from a record of events that, in theory, should be able to represent the record in question. This was one of the first algorithms able to contemplate the concurrency of activities in its model. This Alpha algorithm processes the record following a series of rules, from which it searches for a series of relationship patterns between activities. The algorithm proposed in this work, in general terms, reads the records of a database, reads whether it is a square or a transition, and uses existing connections to later draw a Petri net on a plane. Its operation is described and explained later [8]. There are several opportunities in the field of process mining [9]:  
1. Finding the behavior pattern of a process based on organizational structures. 2. Designing processes to facilitate the agile use of data. 3. Using process mining to create a balance between automation and human participation in business processes. 4. Investigating reference models for value creation in the context of best practices. This project tries to bring system designers closer to obtaining business improved process models that are aimed at ensuring better practices. Petri nets are a tool to model workflows; when modeling them, users tend to draw nodes that represent tasks or activities, with arcs between the nodes representing the sequence of activities. Petri nets (PN) belong to a technique that is gaining popularity in different industrial sectors, such as automotive, computing, information technology, and more. They first gained popularity in the 1990s for simulating more common systems, for example, production lines, banking service processes, and supply chains, among others. PNs have an elegant and simple mathematical foundation.