Development of Intellectual Decision Making System for Logistic Business Process Management

This research paper delves into the design and development of an Intellectual Decision Making System (IDMS) incorporated into a Logistic Business Process Management System (LBPSMS), employing advanced Machine Learning (ML) models. Aimed at streamlining and optimizing logistics business operations, the focal point of this study is to significantly elevate efficiency, enhance decision-making precision, and substantially reduce operational costs. This research introduces a pioneering hybrid approach that amalgamates both supervised and unsupervised machine learning algorithms, creating a unique paradigm for predictive analytics, trend analysis, and anomaly detection in logistics business processes. The practical application of these combined methodologies extends to diverse areas such as accurate demand forecasting, optimal route planning, efficient inventory management, and predictive customer behavior analysis. Empirical evidence from experimental trials corroborates the efficacy of the proposed IDMS, showcasing its profound impact on the decision-making process, with clear and measurable enhancements in operational efficiency and overall business performance within the logistics sector. This study thus delivers invaluable insights into the realm of machine learning applications within logistics, extending a comprehensive blueprint for future research undertakings and practical system implementations. With its practical significance and academic relevance, this research underscores the transformative potential of machine learning in revolutionizing the logistics business process management systems.

@article{Kozhamkulova2024,
  title     = {Development of Intellectual Decision Making System for Logistic Business Process Management},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zhadra Kozhamkulova and Leilya Kuntunova and Shirin Amanzholova and Almagul Bizhanova and Marina Vorogushina and Aizhan Kuparova and Mukhit Maikotov and Elmira Nurlybayeva},
  doi       = {10.14569/IJACSA.2024.0150186},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150186}
}