Periodic performance prediction for real‐time business process monitoring

Abstract

Purpose

The purpose of this paper is to provide industrial managers with insight into the real‐time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real‐time business process monitoring.

Design/methodology/approach

In the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real‐time progress in order to describe the ongoing status.

Findings

The proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.

Research limitations/implications

The performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.

Originality/value

Whereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real‐time indicator describing the current capability of ongoing process.

Citation

Kang, B., Kim, D. and Kang, S. (2012), "Periodic performance prediction for real‐time business process monitoring", Industrial Management & Data Systems, Vol. 112 No. 1, pp. 4-23. https://doi.org/10.1108/02635571211193617