A Case Study Lens on Process Mining in Practice

Emamjome, Fahame; Andrews, Robert; Hofstede, Arthur H. M. ter

doi:10.1007/978-3-030-33246-4_8

Cited by 31 publications

(38 citation statements)

References 19 publications

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“…There is a fertile research discourse on process mining. While some works in this discourse set out to illuminate the practical aspects of process mining (Turner et al , 2012; Thiede et al , 2018; Emamjome et al , 2019; van der Aalst, 2019; van der Aalst et al , 2007; De Weerdt et al , 2013), the majority of research is concerned with technical aspects, e.g. developing and improving process mining techniques (Augusto et al , 2018; Tax et al , 2019; Di Ciccio et al , 2017; Yeshchenko et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Adoption, use and management of process mining in practice

Grisold

Mendling

Otto

2020

BPMJ

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PurposeThis study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.Design/methodology/approachWe report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.FindingsWe find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.Research limitations/implicationsWe point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.Practical implicationsThe reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.Originality/valueThis study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

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Section: Introductionmentioning

confidence: 99%

Adoption, use and management of process mining in practice

Grisold

Mendling

Otto

2020

BPMJ

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“…Pre-hospital transport and care: Pre-hospital retrieval time influences morbidity outcomes in certain trauma patients and understanding the processes which occur in the transportation of trauma patients can inform interventions to improve the timeframes within which patients are transferred to appropriate hospital-based care [12]. While process mining has been applied to the healthcare domain in many case studies [13], there has been only limited attention paid to pre-hospital transport and care. Lamine et al [14] report on a simulation study of emergency call centre operations in France, and Bruns et al [15] apply complex event processing (CEP) to improve the availability and accuracy of information for emergency call centre dispatch operators.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Andrews

Wynn

Vallmuur

et al. 2020

IJERPH

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In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a case is defined as being an individual patient’s journey from roadside to definitive care. We describe challenges in constructing an event log from source data provided by emergency services and hospitals, including record linkage (no standard patient identifier), and constructing a unified view of response, retrieval, transport and pre-hospital care from interleaving processes of the individual service providers. We analyse three separate cohorts of patients according to their degree of interaction with Queensland Health’s hospital system (C1: no transport required, C2: transported but no Queensland Health hospital, C3: transported and hospitalisation). Variant analysis and subsequent process modelling show high levels of variance in each cohort resulting from a combination of data collection, data linkage and actual differences in process execution. For Cohort 3, automated process modelling generated ’spaghetti’ models. Expert-guided editing resulted in readable models with acceptable fitness, which were used for process analysis. We also conduct a comparative performance analysis of transport segment based on hospital ‘remoteness’. With regard to the field of process mining, we reach various conclusions including (i) in a complex domain, the current crop of automated process algorithms do not generate readable models, however, (ii) such models provide a starting point for expert-guided editing of models (where the tool allows) which can yield models that have acceptable quality and are readable by domain experts, (iii) process improvement opportunities were largely suggested by domain experts (after reviewing analysis results) rather than being directly derived by process mining tools, meaning that the field needs to become more prescriptive (automated derivation of improvement opportunities).

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“…Process Mining, a sub-discipline of data science and business process management (BPM), is a powerful technique that allows deriving insights into business process performance and behaviour using historical records from event logs [1]. Previous research shows that Process Mining is widely applied in practice [7,10]. Thereby, using an event log as the fundamental data source for Process Mining, organizations gain insights into process performance, conformance of processes to existing process models, and process improvement opportunities [26].…”

Section: Introductionmentioning

confidence: 99%

“…They consider logs that at least fulfil the conditions of 3-stars ( ) as suitable for Process Mining techniques. However, most real-life logs do not comply with these conditions as they tend to be incomplete, noisy, and imprecise [7,10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Fischer

Goel

Andrews

et al. 2020

Lecture Notes in Computer Science

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Timestamp information recorded in event logs plays a crucial role in uncovering meaningful insights into business process performance and behaviour via Process Mining techniques. Inaccurate or incomplete timestamps may cause activities in a business process to be ordered incorrectly, leading to unrepresentative process models and incorrect process performance analysis results. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the log is used as input for any Process Mining activity. To the best of our knowledge, research on the (automated) quality assessment of event logs remains scarce. Our work presents an automated approach for detecting and quantifying timestamp-related issues (timestamp imperfections) in an event log. We define 15 metrics related to timestamp quality across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). We adopted the design science research paradigm and drew from knowledge related to data quality as well as event log quality. The approach has been implemented as a prototype within the open-source Process Mining framework ProM and evaluated using three real-life event logs and involving experts from practice. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data preprocessing phase of Process Mining projects.

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A Case Study Lens on Process Mining in Practice

Cited by 31 publications

References 19 publications

Adoption, use and management of process mining in practice

Adoption, use and management of process mining in practice

A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

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