Estimating Activity Start Timestamps in the Presence of Waiting Times via Process Simulation

Fracca, Claudia; Leoni, Massimiliano de; Asnicar, Fabio; Turco, Alessandro

doi:10.1007/978-3-031-07472-1_17

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The choice of estimating different start moments leads to estimating different activity-instance durations. In [14], Fracca et al proposes a technique to estimate the starting event where different activity-duration configurations are simulated, and the resulting simulated event log is compared with the real event logs to assess the similarity with respect to time-related aspects (activity-instance waiting times and process-instance durations): the more similar are the real and simulated event log is, the more realistic are the estimation of activity instance durations. The simulation of different activity-duration configurations requires a simulation model, which consists of a process model that is extended with additional information related to the simulation aspects, such as the inter-arrival time, the routing probabilities at the XOR gateways, the roles and the resource-activity allocation, potential work calendar, and more.…”

Section: Dealing With Non-compliant Traces and Missing Timestampsmentioning

confidence: 99%

“…The simulation of different activity-duration configurations requires a simulation model, which consists of a process model that is extended with additional information related to the simulation aspects, such as the inter-arrival time, the routing probabilities at the XOR gateways, the roles and the resource-activity allocation, potential work calendar, and more. The simulation model can be constructed by combining different process mining techniques, as also discussed in [14].…”

Section: Dealing With Non-compliant Traces and Missing Timestampsmentioning

confidence: 99%

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Foundations of Process Enhancement

Leoni

2022

Lecture Notes in Business Information Processing

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Process models are among the milestones for Business Process Management and Mining, and used to describe a business process or to prescribe how its instances should be carried out. It follows that they need to fulfill certain properties to be useful. If they aim to represent how the process is currently being executed, they need to be precise and recall the behavior observed in reality. If the goal is to ensure that the process is executed according to laws and regulations, its model should only allow the behavior that is valid from a domain viewpoint and provides some guarantee to ensure good performance level. Process enhancement is the type of Process Mining that aims at models that fulfill these properties, and the literature further splits it into two subfields: process extension and process improvement. Process extension aims to incorporate the process perspectives on data, decision, resources and time into the model: their inclusion in process models enable designers to fine-tune the model specifications, thus obtaining models with higher levels of precision. Process improvement passes through an “improved” process model. If the model contains portions of behavior that lead to unsatisfactory outcomes (high costs, low customer satisfactions, etc.) or that violate norms and regulations, one would like those portions to be disallowed by the model. In case some executions are observed in reality and are not allowed by the model, they should be incorporated into the model if they are observed to generally yield good performances. This chapter discusses these two types of process enhancement, and illustrates some basic and some advanced techniques to tackle it, highlighting the pros and cons, and the underlaying assumptions.

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Section: Dealing With Non-compliant Traces and Missing Timestampsmentioning

confidence: 99%

Section: Dealing With Non-compliant Traces and Missing Timestampsmentioning

confidence: 99%

Foundations of Process Enhancement

Leoni

2022

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several approaches have been proposed to address this problem. However, these approaches are either manual and qualitative [22] or they produce a single number that does not allow one to identify the source(s) of deviations between the BPS model and the observed reality [6,10].…”

Section: Introductionmentioning

confidence: 99%

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Chapela-Campa,

Benchekroun,

Baron

et al. 2023

Lecture Notes in Computer Science

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Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be the cycle time of a process if one or more resources became unavailable. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and empirical observations, or automatically discovered from execution data. Regardless of its origin, a key question when using a BPS model is how to assess its quality. In this paper, we propose a collection of measures to evaluate the quality of a BPS model w.r.t. its ability to replicate the observed behavior of the process. We advocate an approach whereby different measures tackle different process perspectives. We evaluate the ability of the proposed measures to discern the impact of modifications to a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify sources of discrepancies.

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Data-Driven Identification and Analysis of Waiting Times in Business Processes

Ali,

Milani,

Dumas

2024

Bus Inf Syst Eng

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Reducing waiting times in end-to-end business processes is a recurrent concern in the field of business process management. The uptake of data-driven approaches in this field in the past two decades, most notably process mining, has created new opportunities for fine-grained analysis of waiting times based on execution data. As a result, a wide range of approaches for waiting time identification and analysis on the basis of business process execution data have been reported in the literature. In many instances, different approaches have considered different notions of waiting time and different causes for waiting time. At present, there is a lack of a consolidated overview of these manifold approaches, and how they relate to or complement each other. The article presents a literature review that starts with the question of what approaches for identification and analysis of waiting time are available in the literature, and then refines this question by adding questions which shed light onto different causes and notions of waiting time. The survey leads to a multidimensional taxonomy of data-driven waiting time analysis techniques, in terms of purpose, causes, and measures. The survey identifies gaps in the field, chiefly a scarcity of integrated multi-causal approaches to analyze waiting times in business processes, and a lack of empirically validated approaches in the field.

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Estimating Activity Start Timestamps in the Presence of Waiting Times via Process Simulation

Cited by 4 publications

References 15 publications

Foundations of Process Enhancement

Foundations of Process Enhancement

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Data-Driven Identification and Analysis of Waiting Times in Business Processes

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