Predictive Analytics and Language Model Reasoning for Adaptive Supply Chain Planning
- 1Boho Design
Abstract
Supply chain planning requires organizations to interpret changing demand, uncertain supply, transport disruptions, and large volumes of enterprise information. This study presents an integrated decision framework that connects machine learning forecasts with the contextual reasoning capabilities of large language models. Five connected layers support data acquisition, preparation and knowledge integration, predictive modeling, language model recommendations, and operational optimization with performance feedback. The reported evaluation uses publicly available data comprising approximately 2.3 million supply chain records, 850,000 inventory records, 520,000 logistics records, and 410,000 customer order records. Reported outcomes include demand forecasting accuracy of 98.1%, inventory optimization efficiency of 96.8%, logistics cost savings of 37.4%, a 42.6% improvement in decision response time, and a 33.8% decrease in stockouts. System reliability reaches 98.7%, while overall supply chain productivity improves by 35.2%. These findings suggest that combining quantitative prediction with contextual decision support can improve coordination across planning activities. The framework offers a basis for examining adaptive supply chain operations; the reported performance should be interpreted within the experimental scope described in this study.
Keywords: Adaptive supply chain planning; predictive analytics; machine learning; large language models; inventory management; demand estimation; logistics optimization; enterprise decision support.
Related
- Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models: A Unified Framework for Pain Sensing, Contextual Reasoning, Safe Sensory Feedback, and Adaptive Interaction
- Employing AI-driven predictive analytics to enhance patient outcomes and streamline healthcare processes
- The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics
- A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.
- FactorFlow: A Visual Analytics Workspace with Large Language Model-Assisted Interpretation for Factor Analysis
BibTeX
@misc{patel2026predictive,
title = {{Predictive Analytics and Language Model Reasoning for Adaptive Supply Chain Planning}},
author = {Puneet Patel},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23539361},
url = {https://zenodo.org/records/23539361},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Predictive Analytics and Language Model Reasoning for Adaptive Supply Chain Planning
AU - Puneet Patel
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23539361
UR - https://zenodo.org/records/23539361
AB - Supply chain planning requires organizations to interpret changing demand, uncertain supply, transport disruptions, and large volumes of enterprise information. This study presents an integrated decision framework that connects machine learning forecasts with the contextual reasoning capabilities of large language models. Five connected layers support data acquisition, preparation and knowledge integration, predictive modeling, language model recommendations, and operational optimization with performance feedback. The reported evaluation uses publicly available data comprising approximately 2.3 million supply chain records, 850,000 inventory records, 520,000 logistics records, and 410,000 customer order records. Reported outcomes include demand forecasting accuracy of 98.1%, inventory optimization efficiency of 96.8%, logistics cost savings of 37.4%, a 42.6% improvement in decision response time, and a 33.8% decrease in stockouts. System reliability reaches 98.7%, while overall supply chain productivity improves by 35.2%. These findings suggest that combining quantitative prediction with contextual decision support can improve coordination across planning activities. The framework offers a basis for examining adaptive supply chain operations; the reported performance should be interpreted within the experimental scope described in this study. Keywords: Adaptive supply chain planning; predictive analytics; machine learning; large language models; inventory management; demand estimation; logistics optimization; enterprise decision support.
ER -