Process Mining Methodology Research Articles

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the ‘Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the ‘Return from Waiting’ activity to ‘Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.

Assessment of distance to primary percutaneous coronary intervention centres in ST-segment elevation myocardial infarction: Overcoming inequalities with process mining tools.

In ST-segment elevation myocardial infarction (STEMI), time delay between symptom onset and treatment is critical to improve outcome. The expected transport delay between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy. The "Centro" region of Portugal has heterogeneity in PCI assess due to geographical reasons. We aimed to explore time delays between regions using process mining tools. Retrospective observational analysis of patients with STEMI from the Portuguese Registry of Acute Coronary Syndromes. We collected information on geographical area of symptom onset, reperfusion option, and in-hospital mortality. We built a national and a regional patient's flow models by using a process mining methodology based on parallel activity-based log inference algorithm. Totally, 8956 patients (75% male, 48% from 51 to 70 years) were included in the national model. Most patients (73%) had primary PCI, with the median time between admission and treatment <120 minutes in every region; "Centro" had the longest delay. In the regional model corresponding to the "Centro" region of Portugal divided by districts, only 61% had primary PCI, with "Guarda" (05:04) and "Castelo Branco" (06:50) showing longer delays between diagnosis and reperfusion than "Coimbra" (01:19). For both models, in-hospital mortality was higher for those without reperfusion therapy compared to PCI and fibrinolysis. Process mining tools help to understand referencing networks visually, easily highlighting its inefficiencies and potential needs for improvement. A new PCI centre in the "Centro" region is critical to offer timely first-line treatment to their population.

Akıllı Kampüs Konsepti Altında Dijital Süreçler İçin Süreç Madenciliği Metodolojisi

Digital transformation affects universities as well as many industries. Universities are increasingly using various digital resources and systems to manage their knowledge. The smart campus, on the other hand, supports informed decision-making by integrating these resources and systems. Process mining provides real insights for digital transformation, allowing processes to be examined more transparently. This study aims to examine the proposed project implementation processes related to the smart university with the process mining methodology. For this purpose, 32 completed projects submitted to İzmir Bakırçay University Scientific Research Projects Coordinatorship (BAPK) with the proposed methodology adapted from Deming's continuous improvement cycle were examined. The data are taken from two different pages in the project automation system. According to the research findings, Projects are grouped into three categories: Guided Projects (GDM, 5 projects), Graduate Thesis Projects (TEZ, 5 projects), and Career Start Support Projects (KBP, 22 projects). 40.6% (13 projects) of the applications went directly to the project review stage, while 19 (59.4%) needed procedural correction. Considering the time from the creation of the application of 32 projects to the signing of the contract, it is seen that the arithmetic average of the cycle time is 15.1 weeks, and the median average is 52.5 days. The notable difference between arithmetic and median mean is that very few projects are of long duration. Procedural adjustments affect project evaluation cycle time by an additional 14 days. The carelessness or lack of knowledge of the applicants extends the cycle time of the process from 15 days to 53 days. The total duration of unnecessary waiting time in the process is 17 days. This study primarily proposes that non-digital processes should be digitized as soon as possible.

Valuable Business Knowledge Asset Discovery by Processing Unstructured Data

Modern organizations are challenged to enact a digital transformation and improve their competitiveness while contributing to the ninth Sustainable Development Goal (SGD), “Build resilient infrastructure, promote sustainable industrialization and foster innovation”. The discovery of hidden process data’s knowledge assets may help to digitalize processes. Working on a valuable knowledge asset discovery process, we found a major challenge in that organizational data and knowledge are likely to be unstructured and undigitized, constraining the power of today’s process mining methodologies (PMM). Whereas it has been proved in digitally mature companies, the scope of PMM becomes wider with the complement proposed in this paper, embracing organizations in the process of improving their digital maturity based on available data. We propose the C4PM method, which integrates agile principles, systems thinking and natural language processing techniques to analyze the behavioral patterns of organizational semi-structured or unstructured data from a holistic perspective to discover valuable hidden information and uncover the related knowledge assets aligned with the organization strategic or business goals. Those assets are the key to pointing out potential processes susceptible to be handled using PMM, empowering a sustainable organizational digital transformation. A case study analysis from a dataset containing information on employees’ emails in a multinational company was conducted.

Abstracting Process Mining Event Logs From Process-State Data To Monitor Control-Flow Of Industrial Manufacturing Processes

In industrial manufacturing, a production process usually consists of multiple manufacturing steps during the transformation of raw material into a complete product. Depending on varying product specifications, the production process may differ within the performed manufacturing steps and within the definition of process ”normality”, which refers to the multi-dimensional sensor data captured during the manufacturing process. Process Mining offers excellent process discovery and monitoring capabilities; however, it relies on well-defined event logs. In Industry 4.0 manufacturing, this requirement poses a significant challenge as data commonly generates by a range of low-level sensor measurements, i.e. process-state data such as temperature or humidity and product-state data such as quality measurements or material compositions. In this paper, we investigate the applicability of Process Mining methodologies in combination with concepts of data-driven Digital Twins to discover and monitor process control-flow. We introduce a concept called self-discovering and event-log-aware Digital control-flow Twins to bridge the gap of Process Mining, Digital Twins and continuous process data. We overcome the abstraction gap by generating discrete event logs backed by descriptive statistics of reoccurring process-state characteristics using unsupervised clustering. Our approach does not rely on preexisting information of manufacturing processes in the form of discrete event logs, making it suitable for various information systems that incorporate cyclic process behaviour.

Multi-level process mining methodology for exploring disease-specific care processes

BackgroundPublic healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods. AimThe objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases. MethodsThe proposed methodology called Process Mining Methodology for Exploring Disease-specific Care Processes (MEDCP) is based on a systematic, step-wise refinement of the raw event logs by using such a multi-level expert taxonomy of events that encapsulates the professional concepts of the analysis. A treatment process is defined according to domain-specific rules to identify the starting (index) and closing events. Concepts from various levels of the taxonomy support the final process definition for an analysis that can deliver meaningful conclusions for domain experts. ResultsThe applicability of the methodology was demonstrated on two case studies in the cardiological and oncological care domains, in the public health care system in Hungary over a period of ten years. Thanks to the multi-level taxonomy, these studies successfully identified the most important high-level event sequence patterns and some key anomalies in the national care system, such as the significantly different behavior of low-volume vs. high volume care providers in the oncology study or the geographically connected, homogeneous clusters of providers with similar care spectra in the cardiology study. DiscussionAs the case studies showed, the proposed methodology can improve the efficiency of standard process mining methods, and deliver high level conclusions that are easy to interpret by domain experts. System-level insight into health care processes can serve as a basis for the optimisation and long-term planning of the whole care system.

Process mining to optimize palliative patient flow in a high-volume radiotherapy department.

In radiotherapy, palliative patients are often suboptimal managed and patients experience long waiting times. Event-logs (recorded local files) of palliative patients, could provide a continuative decision-making system by means of shared guidelines to improve patient flow. Based on an event-log analysis, we aimed to accurately understand how to successively optimize patient flow in palliative care. A process mining methodology was applied on palliative patient flow in a high-volume radiotherapy department. Five hundred palliative radiation treatment plans of patients with bone and brain metastases were included in the study, corresponding to 290 patients treated in our department in 2018. Event-logs and the relative attributes were extracted and organized. A process discovery algorithm was applied to describe the real process model, which produced the event-log. Finally, conformance checking was performed to analyze how the acquired event-log database works in a predefined theoretical process model. Based on the process discovery algorithm, 53 (10%) plans had a dose prescription of 8 Gy, 249 (49.8%) plans had a dose prescription of 20 Gy and 159 (31.8%) plans had a dose prescription of 30 Gy. The remaining 39 (7.8%) plans had different dose prescriptions. Considering a median value, conformance checking demonstrated that event-logs work in the theoretical model. The obtained results partially validate and support the palliative patient care guideline implemented in our department. Process mining can be used to provide new insights, which facilitate the improvement of existing palliative patient care flows.

Application of Process Mining technology to analyze the correspondence of life cycles of information resources to a generalized model

The article solves the problem of assessing the applicability of the Process Mining methodology and ProM software to identify failures and deviations in the life cycle of information resources. The object of the research was the database of the Seattle Public Library, hosted on the website Kaggle.com, records were analyzed for the period from 2005 to 2017. In the course of the research, the information resource life cycle model was restored using the example of a library storage unit. Then, events atypical for the restored model were identified, that is, the system made it possible to identify deviations in the life cycle and statistically analyze the factors that caused them by means of a higher detailing of the process in the problem area. The obtained result confirms the possibility of detecting errors and inconsistencies in the processes of managing information resources and analyzing the root causes of errors based on the event log of working with an information resource.

A process mining methodology for modeling unstructured processes

The effective representation of business processes is widely recognized as a critical task in Business Process Management (BPM). Unfortunately, the complexity of unstructured processes makes process modeling extremely challenging and limits the suitability of traditional model‐driven approaches, which appear considerably less effective and efficient. Nevertheless, most of the recent and promising data‐driven approaches dealing with unstructured processes are not yet completely developed and typically fail to provide an adequate procedural process model. This study proposes a novel process mining‐based methodology to achieve a significant process model when unstructured processes occur. Specifically, the method assesses and combines the outcomes of different process mining algorithms by evaluating the process model through appropriate quality parameters (i.e., accuracy and comprehensibility). The final output of the method corresponds to a unitary procedural process model that is mathematically computable, evaluable through objective quality metrics, comparable with other process models, convertible to other model languages, and usable for supporting BPM activities. Finally, a real case study of an Italian hospital is presented to verify the applicability of the proposed methodology.

Analysis of the life cycle of a library information resource using Process Mining technology

The purpose of this work is to evaluate the applicability of the Process Mining methodology for the analysis of life cycle models of information resources. ProM software was used with the Inductive visual miner plugin. The source data was a database of the Seattle Public library hosted on the site Kaggle.com. Even from a fragment of the transaction history of library information resources, it was possible to recover the lifecycle model. The result confirms that it is possible to build and analyze models of the life cycle of information resources based on the event history logs.

Construction of Empirical Care Pathways Process Models From Multiple Real-World Datasets.

Care pathways (CPWs) are "multidisciplinary care plans that detail essential care steps for patients with specific clinical problems." While CPWs impact on health or cost outcomes is vastly studied, an in-depth analysis of the real-world implementation of the CPWs is an area that still remains underexplored. The present work describes how to apply an existing process mining methodology to construct the empirical CPW process models. These process models are a unique piece of information for health services research: for example to evaluate their conformance against the theoretical CPW described on clinical guidelines or to evaluate the impact of the process in health outcomes. To this purpose, this work relies on the design and implementation of a solution that a) synthesizes the expert knowledge on how health care is delivered within and across providers as an activity log, and b) constructs the CPW process model from that activity log using process mining techniques. Unlike previous research based on ad hoc data captures, current approach is built on the linkage of various heterogeneous real-world data (RWD) sets that share a minimum semantic linkage. RWD, defined as secondary use of routinely collected data as opposite to ad hoc data extractions, is a unique source of information for the CPW analysis due to its coverage of the caregiving activities and its wide availability. The viability of the solution is demonstrated by constructing the CPW process model of Code Stroke (Acute Stroke CPW) in the Aragon region (Spain).

Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

Analysis of information resource life cycle

The main purpose of this work is to propose an approach to the analysis of the life cycle of an information resource based on the analysis of real historical data about the resource. To achieve it, several problems are solved – collection of baseline historical data, in the format of event log identify important parameters and patterns in the life cycle status, and making recommendations to improve work with information resource. As an object of research, the article considers the life cycle of service as part of the information process. Service is a reaction of the company’s service Department to the incidents that occur, the consequences of which must be eliminated in a minimum period of time. Process Mining methodology and ProM software were used as the main analysis tools. For the life cycle analysis the additional module Inductive visual miner was applied, which allowed to identify typical chains of life cycle variants, and also to analyze the load of life cycle participants. The analysis showed bottlenecks in the life cycle, “weak links” among the performers, allowed to formulate proposals for improving the life cycle.

Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets.

This work proposes the use of Process Mining methodologies on healthcare datasets containing diagnosis information as a means to identify the course of a disease across organizations. Datasets containing diagnosis information for administrative purposes are a good candidate due to its standardized format, widespread availability and coverage. We present a methodology to preprocess, cluster and mine diagnosis information and the results of a preliminary use case with diabetes type II. Some meaningful disease courses have been found but less useful patterns do also emerge. Future work involves lowering the level of granularity chosen (ICD three digit codes) and extending the time span of the data available (three years).

Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland.

While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis, (iii) do not highlight potential impacts of poor quality data on different types of process analyses. As our key contribution, we develop a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology. Our approach, adapted from elements of the well known CRISP-DM data mining methodology, includes conceptual data modeling, quality assessment at both attribute and event level, and trial discovery and conformance to develop understanding of system processes and data properties to inform data extraction. We illustrate our approach in a case study involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We describe the detailed preparation for a process mining analysis of retrieval and transport processes (ground and aero-medical) for road-trauma patients in Queensland. Sample datasets obtained from QAS and RSQ are utilised to show how quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the data extraction and pre-processing stages of a process mining case study to properly align the data with the case study research questions.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems.

The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.

Redesigning business processes: a methodology based on simulation and process mining techniques

Nowadays, organizations have to adjust their business processes along with the changing environment in order to maintain a competitive advantage. Changing a part of the system to support the business process implies changing the entire system, which leads to complex redesign activities. In this paper, a bottom-up process mining and simulation-based methodology is proposed to be employed in redesign activities. The methodology starts with identifying relevant performance issues, which are used as basis for redesign. A process model is “mined” and simulated as a representation of the existing situation, followed by the simulation of the redesigned process model as prediction of the future scenario. Finally, the performance criteria of the current business process model and the redesigned business process model are compared such that the potential performance gains of the redesign can be predicted. We illustrate the methodology with three case studies from three different domains: gas industry, government institution and agriculture.