Evaluating Performance of Restaurant POS Processes in Fast-Food Restaurants

Abstract

There are billions of operations happening in a wide range of sectors on a daily basis. When it comes to the hospitality sector, it appears essential to handle POS operations in a more efficient way in restaurants. To fill the gap in the studies about event log data in the fast food restaurant POS context, an approach needs to be developed. Regarding these, in this study, restaurant event log data for taking orders are comprehensively analyzed using process mining principles and machine learning applications to increase productivity. After the discovery of processes, the bottlenecks of the existing system were extracted in fast food restaurant point of sale (POS). The main focus was determined as order-taking process times, which can be the most troubled part of the fast food delivery process. Regression analysis was conducted to identify possible reasons for increasing time for order taking in a restaurant pos. This analysis can extract the main drawbacks of the system and provide insights to solve problematic points in order to increase productivity. Process discovery techniques, such as heuristics miner, directly follows graph (DFG) are used under process mining methodologies to discover event logs in a visual manner in the background. To be able to understand the logic of event logs deeply, exploratory data analysis techniques were performed to identify the effect of log activity types by also focusing on their respective attributes. Afterwards, it needed to adopt performance analysis, comparative, and action-oriented process mining techniques to evaluate, identify, and operationally support the business. In addition to process mining approaches, feature engineering, descriptive statistics techniques and outlier elimination are used along with various regression methods such as XgBoost, Random Forest to identify the relationship between variables of the system. The detailed descriptions of the feature relations are also explained to understand how variables affect the order taking time directly or indirectly. After that, the study found possible reasons, such as how many products are sold or how many different operators are working on that POS, affecting ordering time and how much they are specific to its context. By identifying these reasons, it is shown that order-taking processing times in a restaurant POS can be dramatically decreased with specific recommended actions in particular contexts. By applying research findings, order-taking process times are expected to improve by around 21% in a territorial business, which implies productivity growth in POS environments. Consequently, the study first showed how different techniques can be used to identify outliers in relationship metrics in restaurant POS event log data. Secondly, it is a direct, crucial example of what factors affect a restaurant's POS processes and how much. Meanwhile, it significantly suggests machine learning integrated process mining approaches by combining the mentioned techniques. Lastly, the paper can reveal how efficient this process structure is for operator usage, which is a question of further study.