AI-Enabled Supplier On-Time Performance Framework

Rajesh Gangula

Abstract: Supplier On-Time Performance (OTP) is a critical indicator of supply chain reliability, cost efficiency, and operational continuity. Traditional OTP processes are often manual, reactive, and prone to inconsistency due to static rules, delayed visibility, and limited exception handling. This paper introduces an AI-Enabled Supplier On-Time Performance (SOTP) Framework that transforms OTP management into a predictive, automated, and self-optimizing ecosystem. The framework standardizes OTP measurement, automates eligible PO identification, and integrates performance-based fine computation directly into ERP systems such as Oracle Accounts Payable. An industry case study demonstrates how retail organizations use this model to monitor delivery accuracy, enforce vendor compliance, and streamline debit memo creation. The proposed AI enhancements spanning dynamic exclusion management, predictive PO risk scoring, automated rule execution, natural-language-based vendor communication, sentiment-driven escalation, and intelligent dispute resolutions significantly reduce manual effort while improving accuracy and transparency. AI-powered dashboards, scenario simulations, and adaptive analytics further elevate decision-making, enabling organizations to proactively mitigate delivery risks and enhance supplier collaboration. This end-to-end AI-augmented SOTP framework provides a scalable foundation for resilient, efficient, and future-ready supply chain performance management.

Keywords: AI, Supplier On-Time Performance, Supply chain Management, ERP, Oracle

How to Cite?: Rajesh Gangula, "AI-Enabled Supplier On-Time Performance Framework", Volume 13 Issue 6, June 2024, International Journal of Science and Research (IJSR), Pages: 1975-1980, https://www.ijsr.net/getabstract.php?paperid=SR24617094222, DOI: https://dx.doi.org/10.21275/SR24617094222