Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Senderovich, Arik; Weidlich, Matthias; Yedidsion, Liron; Gal, Avigdor; Mandelbaum, Avishai; Kadish, Sarah; Bunnell, Craig A.

doi:10.1016/j.is.2016.01.002

Cited by 40 publications

(19 citation statements)

References 52 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subsequent studies concentrated more on algorithms/techniques for conformance checking (Rozinat and van der Aalst, 2008), process discovery (Rozinat et al , 2009), clustering and visualization (Song et al , 2009). Currently, as new tools and techniques continue to emerge (Senderovich et al , 2015, 2016; De Smedt et al , 2015), the number of application-oriented contributions is increasing (Conforti et al , 2015; Schönig et al , 2016).…”

Section: Resultsmentioning

confidence: 99%

How is process mining technology used by organizations? A systematic literature review of empirical studies

Thiede

Fürstenau

Barquet

2018

BPMJ

View full text Add to dashboard Cite

Purpose The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities. Design/methodology/approach The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers. Findings The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services. Practical implications Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information. Originality/value Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

show abstract

Section: Resultsmentioning

confidence: 99%

How is process mining technology used by organizations? A systematic literature review of empirical studies

Thiede

Fürstenau

Barquet

2018

BPMJ

View full text Add to dashboard Cite

show abstract

“…Nguyen et al (2016) proposed the evolution of process performance through the concept of staged phase flow. Senderovich et al (2016) proposed the operational performance improvement method according to queuing network-based conformance inspections and deviation origin analysis. Sun et al (2017) adopted a mirror matrix method based on footprint variant and parallel programming that optimizes the original process according to the log.…”

Section: Integration and Optimization Of Cross-lifecycle Business Promentioning

confidence: 99%

Engineering management for high-end equipment intelligent manufacturing

Yang¹,

Wang²,

Shi³

et al. 2018

Front. Eng

View full text Add to dashboard Cite

The high-end equipment intelligent manufacturing (HEIM) industry is of strategic importance to national and economic security. Engineering management (EM) for HEIM is a complex, innovative process that integrates natural science, technology, management science, social science, and the human spirit. Newgeneration information technology (IT), including the internet, cloud computing, big data, and artificial intelligence, have made a remarkable influence on HEIM and its engineering management activities, such as product system construction, product life cycle management, manufacturing resources organization, manufacturing model innovation, and reconstruction of the enterprise ecosystem. Engineering management for HEIM is a key topic at the frontier of international academic research. This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment. These issues include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories, quality and reliability management, information management, and intelligent decision making. The challenges presented by these issues and potential research opportunities are also summarized and discussed.

show abstract

“…A business seeking to conduct data-driven performance analysis, should first select the type of technique. Descriptive analysis will show the current state and [34], [23], [5], [6], [32], [3] Process Duration -- [41] Fragment Duration Activity Start and End Time [7], [24], [23], [17], [5] Activity Duration -- [19], [31], [29], [34], [7], [24] Waiting Duration [30], [38], [36], [ [21], [22] Framework to extract process characteristics from event logs discriminating between positive and negative cases [5], [6] Comparing waiting duration of similar process in different installations [11], [15] Collaborative Processes [26] Evolution of performance over time [40] Framework for performance-related analysis with information-poor event logs Type Domain…”

Section: Frameworkmentioning

confidence: 99%

A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Milani

Maggi

2018

Business Information Systems

View full text Add to dashboard Cite

Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis. accessibility to open source tools has never been easier, in particular for data driven analysis of business processes. Therefore, in a digital era, businesses cannot hope to survive with manually driven methods. The process analysis must also be digitally transformed by tapping into data-driven analysis methods.One group of techniques for data driven performance analysis uses event logs of processes to assess performance. Indeed, nowadays, business processes are often supported by IT systems that log their execution. For instance, an order-to-invoice process might include activities such as register, validate, approve, fill order and send invoice. Each order has a unique id and every activity is recorded in the event log with information about the time when it was executed (timestamp) and other additional data such as the resource (person or device) that executed the activity. As such, the process is inherently captured in the log. With process mining techniques [2], the performance of such processes can be assessed and analyzed in great detail based on event logs.The body of research and tools within the field of process mining has grown significantly during this decade. However, the availability of tools and approaches developed for specific aspects of process performance does not make it easier for businesses to employ them. In fact, it poses a challenge. There is no way for businesses to easily get an overview of what performance indicators can be measured, what input data is required for such analysis, or what industry specific implementations are available. In light of this context, we propose a framework for the selection of log-based performance analysis techniques. We do so by conducting a systematic literature review to identify the body of existing work. We analyze the results and focus on identifying existing process performance indicators, required input data, and approaches available. Based on the results, we build a framework for the selection of suitable performance analysis approaches.The structure of this paper is as follows. Section 2 summarizes the research protocol for the systematic survey. In Section 3, the research que...

show abstract

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Cited by 40 publications

References 52 publications

How is process mining technology used by organizations? A systematic literature review of empirical studies

How is process mining technology used by organizations? A systematic literature review of empirical studies

Engineering management for high-end equipment intelligent manufacturing

A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Contact Info

Product

Resources

About