Decoding Sales Order Anomalies: Advanced Predictive Modeling and Discrepancy Resolution Utilizing Machine Learning Algorithms

This study examines the accuracy of order prediction and determines the grounds for order block predictions. It sets order deviation by calculating forecasted variation using R2 scores and mean absolute deviation. The blocks that are checked mainly include—business partner block, credit block, common block, and delivery block. Demand forecasts compare six months’ worth of sales data against mean absolute deviation and coefficient of variation. This study puts forth a proposal for solving discrepancies between sales order forecasts and confirms credit management’s system credit limits on sales orders. Parameters for evaluating orders are set relying on historical data. Machine Learning (ML) has been utilized in this study—which involves Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms to improve accuracy where they achieve 96% and 93% respectively.

@article{Soni2025,
  title     = {Decoding Sales Order Anomalies: Advanced Predictive Modeling and Discrepancy Resolution Utilizing Machine Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Amit Kumar Soni and Pooja Jain},
  doi       = {10.14569/IJACSA.2025.0160767},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160767}
}