Performance analysis and continuous process improvement efforts are often supported by the construction of process models representing the interactions of the partners in the supply chain. This study was conducted to determine the state of the art in the process mining field, specifically in the context of cross-organizational process. The Systematic Literature Review (SLR) method is used to review a collection of twenty-one papers that are classified according to the Artifact framework of Hevner, et al. and within the Process Mining framework of Van der Aalst. In the reviewed papers, the authors conducted a variety of techniques to establish the event log, which is then used to perform the process mining analysis. Eight of the reviewed papers focus on the definition of concepts or measures. Five of the papers describe models and other abstractions that are used as a theoretical basis for process mining in the context of supply chains. The majority twenty of papers describe some kind of informal method or formal algorithm to perform process mining analysis. Nine of the papers that propose a formal algorithm also present an accompanying software implementation. Eight papers discuss the data preparation challenges and twelve papers discuss process discovery techniques.
<span>The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The <em>L</em>* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.</span>
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Utilization of information technology is currently growing rapidly in helping activities especially in storing an event log. The activity which is behavior of the user can be analyzed using process mining. The process mining purpose to extract information from event logs on business processes that working. Discovery technique is used in this research. The purpose of this study is to compare two algorithms applied by creating an e-commerce application that is aware of the processes. E-commerce applications require event logs to read the behavior of visitor activities against the application. This research method starts from understanding the business processes that working, then designing a website by creating the application used. Furthermore, data collection through applications that are promoted through social media. The application will be recorded user activity and formed an event log. The event log that formed then discovered using alpha and alpha++ algorithms by utilizing the ProM Lite 1.2 tools. The evaluation results show that the alpha algorithm has shortcomings, namely length one loop, length two loop and non-free choice. And the alpha++ algorithm fixed this deficiency.
The problem of measuring the dwelling time of the container logistics process at ports in developing countries is often a major problem. Therefore, process mining as a field of data science that focuses on analyzing event log data is used to perform business process analysis. In process mining, the process is a sequence of events or activities that are carried out to achieve certain goals. Event logs help an organization to find gaps between the designed business processes and the reality of the processes that occur. In this study PM4PY as a python library is used to perform process mining techniques. The results of the fitness calculation in this study indicate that the container logistics business process model is close to 0.99.
Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining
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