Mining Shift Work Operation from Event Logs

Abstract

Event logs are records of events that are generally used in process mining to determine the manner in which various processes are practically implemented. Previous studies on process mining attempted to combine the results based on different perspectives such as control flow, data, performance, and resources (organizational) to create a simulation model. This study focuses on the resource perspective. A prior study from the resource perspective focused on clustering the resources into organizational units. Implementing the results of the above study in a simulation model will yield inaccurate results because the resources are assumed to always be available if no task is performed. In a practical scenario, resources (particularly humans) tend to work based on shifts. Thus, we propose mining the shift work operation of resources from event logs to tackle this issue. We utilized a self-organizing map and k-means clustering to incorporate the shift work information from the event logs into the simulation model. Moreover, we introduce a distance function and weight-centroid updating rule in the clustering technique to realize our objective. We conducted extensive experiments with artificial data sets to assess the effectiveness of the proposed method. The simulation shows that introducing the shift work operation time of resources can yield more accurate results. Furthermore, the proposed distance function can capture the shift work operation of the resources more precisely compared with the general distance function.

Big Data and AI for Process Innovation in the Industry 4.0 Era

“…This approach is effective for understanding the relationship between resource paths and resource quality. In the paper "Mining Shift Work Operation from Event Logs" by Utama, Sutrisnowati, Kamal, and Bae [7], a practical approach that combines process mining with resource availability in shift work operations is presented. The study utilized a self-organizing map and k-means clustering to incorporate the shift-work information that can be inputted to the simulation model.…”

Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics

“…Different techniques have been used to analyze the diverse perspectives of the processes, such as control flow, performance and organizational [30]. For the control flow, which is the focus of this work, petri nets [30,31], causal nets [31] and process trees [32], among other methods, have been used to represent process models and algorithms have been proposed that can generate them (e.g., alpha for petri nets [28] and Inductive Miner for process trees [28]).…”

“…Different techniques have been used to analyze the diverse perspectives of the processes, such as control flow, performance and organizational [30]. For the control flow, which is the focus of this work, petri nets [30,31], causal nets [31] and process trees [32], among other methods, have been used to represent process models and algorithms have been proposed that can generate them (e.g., alpha for petri nets [28] and Inductive Miner for process trees [28]). Directly Follows Graphs (DFGs) that can be obtained by DFG-based algorithms such as heuristic miner [28] are also widely used because they are one of the easiest notations to interpret by non-expert users of process mining [31].…”

“…In recent years, research has been conducted to visualize event logs through domain models or even theoretical frameworks that can be used to represent and analyze the data [38]. The inclusion of shift work operation to model the organizational perspective of processes [30], the construction of a model representing the added value in service processes [39], the modeling of user behavior in MOOCs to identify self-regulated learning strategies [35] and the analysis of dropout behavior through the investment model [40], are examples of that. In this work, we propose to use the backpack metaphor to conceptualize students' curricular trajectories as a novel approach to understand how they manage their failed courses.…”

Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

“…ERP systems encompass configurable modules supporting business processes that have received wide industrial adoption, and they contain extensive amounts of data about the behaviors of such processes. Recorded data can be used to analyze whether a predefined process specification conforms with real activities [1]. Process mining offers automated techniques for this type of analysis.…”