Machine leaning solution based on gradient descent algorithm for improved business process outcomes

Ivana Dimitrovska, Toni Malinovski and Dane Krstevski

International Journal of Business Innovation and Research, 2021, vol. 24, issue 4, 555-570

Abstract: This study aims to provide guidelines that can help organisation identify preconditions before they can employ machine learning, as well as provide evidence that machine learning can be used to improve business process outcomes. It considers supervisory learning as a business learning strategy, and employs machine learning solution based on gradient descent algorithm in large enterprise company in North Macedonia. The solution was designed to streamline the business process, automate the activities, and provide resilience to employees' subjectivity, wrong decisions, and human errors. The machine learning solution was used in production for ten months, including period of changes in the business process, and its average accuracy was 95.018% compared to the employees' decisions. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. Although it is a specific case study, it provides valuable information that organisations can use while undertaking similar initiatives.

Keywords: machine learning; linear regression; gradient descent algorithm; business process improvement. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=114047 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbire:v:24:y:2021:i:4:p:555-570

Access Statistics for this article

More articles in International Journal of Business Innovation and Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().