Recent Advances in Machine Learning for Business Process Optimization: A Systematic Review

Abstract

Machine learning (ML) technology has been swiftly turning out to be the appropriate procedure of harmonizing the business activity in the cross-industrial environment. As people are getting more exposure to the big data and introducing new advancements in the possibilities of the internet, the use of ML has been solely undertaken with the aim of enabling organizations to do so in order to become more efficient and effective in their businesses by performing and making decisions. This systematic review touches on the given topic by discussing the emerging trends in the use of ML in optimization of business processes with specific mention of the importance that what it has in the operations management, supply chain management, marketing, human resource management as well as customer service. The key conclusions of the recent studies are generalized in the article and it was examined what ML-algorithms are most widespread and whether they are difficult to apply and what is beneficial in their activity. The review also predictive assumes that deep learning, reinforce learning and predictive learning would be more important in simplification of business processes as well as organisational competitiveness of the organisation. The results illustrate that ML would possess possibility to transform the likelihood of the business optimization on its way to the automation of the decision making procedure, and initiate the allocation of the resources, as well as increase the total endeavours of productivity. But the issue of privacy of the data, the lack of experts and the interface of ML systems with legacy are significant obstacles on the way to large-scale deployment. The future research directions in the field were outlined as the results of the paper in which the arguments about the necessity in the development of the extractable and understandable ML models in the business were indicated.