Model-Driven Transformation of Digital Business Processes
DOI:
https://doi.org/10.23055/ijietap.2025.32.4.10851Keywords:
Digital Transformation, Business Process; Model-Based SystemsAbstract
Digitalization through technologies such as sensors, information, and communication technologies creates enormous volumes of data, promotes increased internal and external interactions, and opens new opportunities, particularly in maintenance. One of the major impediments to enterprise transformation is the complexity of business processes. This complexity, in turn, manifests itself through stochastic dynamics to degrade performance. Therefore, we propose a framework for business process transformation. The key model for this framework is based on the business process complexity model with a stochastic dynamic (BPCSD) using Markov chains, which estimates the expectation and variance of the processing steps and flow time. We also propose mapping key elements of the widely used Business Process Modeling Notation (BPMN) to BPCSD and validate these in a maintenance operation. In the case study, we present a method to integrate these models and derive options for business process transformation. We identify tacit processes from a communication log and find that including the tacit processes in the BPSCD model improves its predicted expectation and variance of flow times by 2% and 28%, respectively. We also show the application of the BPCSD as a tool for business process transformation. Using the BPCSD, we identify the efficient options that can reduce the flow time and the standard deviation by 20~ 29%. The business processes presented in the case study are pervasive, and the associated digital infrastructure is widely used in a variety of enterprises, which makes the study relevant to maintenance management in many digital enterprises.
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