scholarly journals Predictive Process Monitoring in Operational Logistics: A Case Study in Aviation

2019 ◽  
pp. 250-262
Author(s):  
Björn Rafn Gunnarsson ◽  
Seppe K. L. M. vanden Broucke ◽  
Jochen De Weerdt
Keyword(s):  
Process Monitoring ◽  
Predictive Process

Encoding resource experience for predictive process monitoring

2021 ◽  
pp. 113669
Author(s):  
Jongchan Kim ◽  
Marco Comuzzi ◽  
Marlon Dumas ◽  
Fabrizio Maria Maggi ◽  
Irene Teinemaa
Keyword(s):  
Process Monitoring ◽  
Predictive Process

scholarly journals Predictive Process Monitoring Methods: Which One Suits Me Best?

2018 ◽  
pp. 462-479 ◽  
Author(s):  
Chiara Di Francescomarino ◽  
Chiara Ghidini ◽  
Fabrizio Maria Maggi ◽  
Fredrik Milani
Keyword(s):  
Process Monitoring ◽  
Monitoring Methods ◽  
Predictive Process

scholarly journals Business Process Models in the Context of Predictive Process Monitoring

2021 ◽  
Vol 28 (1) ◽  
pp. 39-46
Author(s):  
Florian Spree
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Process Model ◽  
Academic Research ◽  
Research Area ◽  
Process Models ◽  
Business Process Model ◽  
Business Process Models ◽  
Wide Range ◽  
Predictive Process

Predictive process monitoring is a subject of growing interest in academic research. As a result, an increased number of papers on this topic have been published. Due to the high complexity in this research area a wide range of different experimental setups and methods have been applied which makes it very difficult to reliably compare research results. This paper's objective is to investigate how business process models and their characteristics are used during experimental setups and how they can contribute to academic research. First, a literature review is conducted to analyze and discuss the awareness of business process models in experimental setups. Secondly, the paper discusses identified research problems and proposes the concept of a web-based business process model metric suite and the idea of ranked metrics. Through a metric suite researchers and practitioners can automatically evaluate business process model characteristics in their future work. Further, a contextualization of metrics by introducing a ranking of characteristics can potentially indicate how the outcome of experimental setups will be. Hence, the paper's work demonstrates the importance of business process models and their characteristics in the context of predictive process monitoring and proposes the concept of a tool approach and ranking to reliably evaluate business process models characteristics.

scholarly journals Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1510
Author(s):  
Chih-Hung Jen ◽  
Chien-Chih Wang
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Sound Frequency ◽  
Detection Accuracy ◽  
Control Line ◽  
Time Process ◽  
Intrinsic Mode Functions ◽  
Low Frequencies ◽  
Linear Discriminant

Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.

Engineering Information Systems to Allow the Integration of Process Monitoring With Asset Management

1997 ◽  
Author(s):  
R. A. Platfoot ◽  
A. Koh ◽  
E. Van Voorthuysen
Keyword(s):  
Decision Making ◽  
Process Monitoring ◽  
Asset Management ◽  
Process Data ◽  
Engineering Information ◽  
Aerosol Cans ◽  
Information Chain ◽  
Wide Readership ◽  
Machine Cell

Abstract A system has been developed whereby process data downloaded from PLC’s is entered into an information chain which incorporates an analysis module which provides tailored reports to a wide readership. The various positions associated with running the factory were analyzed for the types of decisions associated with the job responsibility plus the information necessary to support the decision making. A case study is presented for analyzing aspects of the manufacture of three piece steel aerosol cans. A hardware and software solution was designed, linking a machine cell through the PLC to a operator/process interface.

scholarly journals Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 267 ◽  
Author(s):  
Niyi Ogunbiyi ◽  
Artie Basukoski ◽  
Thierry Chaussalet
Keyword(s):  
Survival Analysis ◽  
Process Monitoring ◽  
Contextual Factors ◽  
Survival Function ◽  
Analysis Approach ◽  
Contextual Features ◽  
Monitoring Framework ◽  
Predictive Process ◽  
The Impact ◽  
Remaining Time

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches.

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