# POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach
## 1 Introduction  
The main characteristics of corporate and industrial processes are dynamics, complexity, and uncertainty. In particular, the manufacturing industry increases in complexity due to the dynamic situation change of many infrastructures and various products and services produced. At the same time, the globalization of industry makes the development cycle of new and innovative technologies shorter and leads to shorter product life cycles [1]. Therefore, companies need to quickly adapt to changes in the environment and technology to gain a competitive advantage [2]. Accurately grasping the planning deviations of a business process in real dynamic situations is essential to enabling companies to respond flexibly to all situations [3]. Process management through general documentation can lead to discrepancies between the actual and documented [4]. In the business flow, many and various event logs are generated. More business management systems can appear based on these logs. Process mining helps keep complex processes up to date and monitored. It extracts data from the event log and performs an important function to improve flow. In addition, it can increase the competitiveness of tasks such as the the optimization of management time, cost reduction and error control. As a result, relevant research being conducted in many fields [5]. However, the efficiency is limited because of the lack of high-level predictive analysis of the manufacturing and overall industry in the monitoring system. Most business process-related solutions have mainly solved enterprise operation problems such as infrastructure statistics [6]. Unfortunately, simply making it efficient only in terms of operations cannot keep manufacturing companies competitive in the future. Accordingly, to provide betterproducts and services, many companies are putting a lot of effort into making decisions using vast amounts of data generated in manufacturing [7,8]. In the future, in addition to documenting, diagnosing, and inspecting historical data, it is also necessary to shift the direction of pre-decision making that enables predictive analysis of the next action of flow. There has been much prior research related to process prediction, but there is still an insufficient number of cases in which various decision-making models are applied. In this paper, we propose a predictive model of the process and compare it to existing studies. Business process prediction extracts features from log data and matches variables of interest. Predicting successive target values, such as the remaining process execution time, is a regression problem. It is a classification problem to predict the next event of a running case, the result of executing a process instance, and individual target values such as service level contract violations. Based on the recorded log data, the past events of the current process instance are considered. It focuses on devising a classification problem that predicts the next event and an approach to solving it. This enables analysts to intervene and prevent problems that may occur in the workflow with previously analyzed information. This is an important issue in a monitoring system. We solve this problem with a fully attention-based transformer approach. For process mining, we propose method of transforming OpenAI GPT-2 (Generative Pre-Training 2), a transformer-based one-way language model and compared it to deep learning methods from other studies. In addition, the prediction accuracy is further improved through the specific data pre-processing method and hyperparameter optimization of the proposed model. POP-ON (Prediction of Process Using One-way Language Model based on NLP (Natural Language Processing) Approach) proposed in this paper is designed according to the application and necessity in actual process prediction, and shows superior quantitative indicators compared to previous research results. The rest of this paper is organized as follows.