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

Niyi Ogunbiyi,Artie Basukoski,Thierry Chaussalet

doi:10.3390/a13110267

Abstract

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.

Highlights

Predicting process outcomes in operational business management is essential for customer relationship management (e.g., ‘will this customer’s order be completed on time?’), enterprise resource planning (e.g., ‘what level of resourcing will be required to manage running cases/process instances?’) and operational process improvement (e.g., ‘what are the common attributes of cases that consistently complete late?’), among others
We evaluated the survival analysis approach against two clustering-based remaining-time approaches identified in the literature and a couple of methods which used a zero prefix-bucketing combined with a gradient boosting machine and multilayer perceptron neural network regressors respectively to predict the remaining time for each trace [14]
This study has proposed an approach to censor an event log to facilitate its use for building a survival function

Summary

Introduction

Predicting process outcomes in operational business management is essential for customer relationship management (e.g., ‘will this customer’s order be completed on time?’), enterprise resource planning (e.g., ‘what level of resourcing will be required to manage running cases/process instances?’) and operational process improvement (e.g., ‘what are the common attributes of cases that consistently complete late?’), among others. Predicting the remaining time of a process instance is very useful. It is essential for effective scheduling of sequentially dependent processes and is a crucial determinant of consumer choice (e.g., where two or more services are identical in price and quality). Process context: similar cases that may be competing for the same resources. Social context: the way human resources collaborate in an organisation to work on the process of interest

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