Queue Mining – Predicting Delays in Service Processes

Senderovich, Arik; Weidlich, Matthias; Gal, Avigdor; Mandelbaum, Avishai

doi:10.1007/978-3-319-07881-6_4

Cited by 72 publications

(46 citation statements)

References 20 publications

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“…The number of resources as function of time, R t , is extracted from data using statistical methods as reported in [20]. The distribution of interarrival times A t and processing times per class B c can be fitted with the techniques presented in [16,21].…”

Section: Discovery Of Fork/join Networkmentioning

confidence: 99%

“…In earlier work, therefore, we argued for an explicit representation of the queueing perspective and demonstrated its value for several real-world processes [20,22]. However, the existing techniques all considered the simplistic setting of a singlestation system, whereas, this paper addressed the more complex scenario of service processes that are scheduled and have a multi-stage structure that involves resource synchronization.…”

Section: Related Workmentioning

confidence: 99%

“…First, the key actors of service processes namely, customers and service providers (or resources), and their complex interaction in terms of customer-resource matching policies (e.g. First-Come FirstServed, Most-Idling Resource-First) [5] need to be specified. Second, a network model is required to define the dependencies of different stages of the service process, including parallel processing of activities [6].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, A2 runs in O n 3 . It is worth noting that A2 may be easily adopted to dominate only a subset of the measures (T max , F max ) by disregarding the respective condition in lines [17][18][19][20][21][22]. This relaxation of the constraints would probably allow better performance on the ξ ∈ ΞC ξ measure.…”

mentioning

confidence: 99%

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

Senderovich

Weidlich

Yedidsion

et al. 2016

Information Systems

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Section: Discovery Of Fork/join Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Senderovich

Weidlich

Yedidsion

et al. 2016

Information Systems

Self Cite

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“…This paper is an extended and revised version of our earlier work [7] that focused on single-class settings only. In this work, we also consider the more complex setup of multi-class settings.…”

Section: Introductionmentioning

confidence: 99%

Queue mining for delay prediction in multi-class service processes

Senderovich

Weidlich

Gal

et al. 2015

Information Systems

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101

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Business analytics in service operations—Lessons from healthcare operations

Baron

2021

Naval Research Logistics

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We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics. KEYWORDSbusiness analytics, service operations, healthcare analytics, queue mining FRAMEWORK FOR BUSINESS ANALYTICSRecent advancements in computing and analytics suggest that both the practice and theory of service operations have been dramatically changed and will continue to shift in the near and more distant future. The availability of data and efficient tools for visualization and analysis opens several possibilities for research and practice in service operations. Cohen (2018) presents a modern view of the impact of big data on service operations. Business analytics in service operation is very broad as it includes, for example, revenue management for services, shared economy, and post-purchase services.To improve our understanding of the different paths toward implementing analytics, we surveyed the literature on analytics in services. As this is a vast literature, we only discuss the literature on business analytics in healthcare operations, with a focus on work related to queueing in this sector. Importantly, the trends and approaches discussed are applicable to business analytics in many services, as well as in other sectors.A common theme of analytics is that it uses quantitative analytical tools, such as statistics, econometrics, computer science, and operations research, to analyze specific problems. Moreover, these analytic tools are used to analyze and model data. Finally, business analytics uses analytics to support decision making in businesses. Similar to the decision-centric definition of analytics in Rose (2016), we offer the following concise definition:• Business analytics: Usage of data-driven analytical methodologies to support decisions. Defining descriptive, predictive, comparative, and prescriptive analyticsQuantifiable performance measures of different business objectives for different business units are often required to support business decisions. Therefore, every bus...

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Queue Mining – Predicting Delays in Service Processes

Cited by 72 publications

References 20 publications

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

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

Queue mining for delay prediction in multi-class service processes

Business analytics in service operations—Lessons from healthcare operations

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