Presentation
AI Hackathon Presentation Script (Line-by-Line)
Slide 1: Title Slide
AI-Driven Predictive Monitoring & Early Warning Integration Visibility System for WMS to OM Fusion Integration.
| Line | Script |
| Line 1 | "Good morning, everyone. Welcome to the AI Hackathon 2025 presentation." |
| Line 2 | "We are presenting today, on December 16th, 2025." |
| Line 3 | "Our Team is named Creators." |
| Line 4 | "The concept we are presenting is the AI-Driven Predictive Monitoring & Early Warning Integration Visibility System for WMS $\to$ OM Fusion Integration." |
Slide 2: Summary/Overview
(Introduce the key pain points and the proposed solution)
| Line | Script |
| Line 5-8 (Problem) | "The core Problem Statement is rooted in three areas: Lack of Real-Time Visibility, No Early Warning for Potential Delays or Processing Risks, and the High Manual Effort required for Reactive Troubleshooting." |
| Line 9-13 (Value) | "By solving these problems, our system offers significant Business Value: Prevention of Integration Failures & Delays, Complete Visibility Through a Unified Dashboard, Reduced Manual Effort & Faster Troubleshooting, and ultimately, Improved Customer Satisfaction & Business Continuity." |
| Line 14-16 (Solution) | "Our Proposed AI Solution has two key components: Use Case 1: Integration Lag Predictor—an AI Model to Predict WMS $\to$ OM Sync Delays, and Use Case 2: Early Warning Integration Visibility Dashboard—for Real-Time Processing Insights & Alerts." |
| Line 17-24 (Team) | "And here is our team: Meeta Rathore, Saroj Sahu, Narendra Enamala, Jeju Babu Dadi, Narendra Arikatla, Naveen Kumar Vasa, and Paritala Lakshmi Anil Prasad." |
Slide 3: Problem Statement Details
(Detail the three specific problem areas)
| Line | Script |
| Line 25-27 | "Diving deeper into the Problem Statement..." |
| Line 28-29 (Visibility) | "Problem Statement 1 highlights the Lack of Real-Time Visibility. WMS to OM integration issues go unnoticed until users report discrepancies, which causes delayed shipments and incorrect order statuses." |
| Line 30-31 (Early Warning) | "Problem Statement 2 is the lack of an Early Warning system. We currently have no automated mechanism to flag messages trending toward delays, abnormal processing duration, or repeated retry patterns." |
| Line 32-34 (Manual Effort) | "Finally, Problem Statement 3 is the High Manual Effort & Reactive Troubleshooting. Support teams rely heavily on manual log review, making the process slow, error-prone, and resource-intensive." |
Slide 4: Business Value Proposition Details
(Connect the solution back to the business benefits)
| Line | Script |
| Line 35-37 | "This leads us to our Business Value Proposition." |
| Line 38-39 (Prevention) | "Business Value 1 is the Prevention of Integration Failures & Delays. The system proactively identifies slowdowns or abnormal trends before they become full failures, preventing disruptions." |
| Line 40-41 (Visibility) | "Business Value 2 is Complete Visibility Through a Unified Dashboard. A single dashboard will provide end-to-end real-time transaction visibility, allowing teams to quickly spot stuck or high-risk messages." |
| Line 42-43 (Reduced Effort) | "Business Value 3 promises Reduced Manual Effort & Faster Troubleshooting. Automated monitoring and alerts eliminate repetitive log checks, letting teams focus on resolution rather than detection." |
| Line 44-45 (Continuity) | "And Business Value 4 is Improved Customer Satisfaction & Business Continuity. Fewer issues mean accurate order updates, smoother operations, and improved business stability." |
Slide 5: Proposed AI Solution - Use Case 1
(Focus on the predictive AI agent)
| Line | Script |
| Line 46-48 | "Let’s look at the two components of the Proposed AI Solution, starting with Use Case 1: Integration Lag Predictor." |
| Line 49-50 | "This is an Agentic AI solution, deployed on the Google Agentic AI Cloud Platform, designed to predict potential WMS $\to$ Oracle Fusion OM synchronization delays before they occur. The AI agent continuously learns from historical data and monitors real-time events to identify delay patterns." |
| Line 51 (Functions) | "Its Key Functions include: Detecting unusual processing time, predicting integration lag, flagging transactions likely to experience delayed synchronization, and proactively notifying support teams via Teams or Email alerts." |
Slide 6: Proposed AI Solution - Use Case 2
(Focus on the operational dashboard)
| Line | Script |
| Line 52-55 | "The second part is Use Case 2: Early Warning Integration Visibility Dashboard—implemented using Visual Studio and Power BI Embedded for operational monitoring." |
| Line 56 | "This dashboard is the operational front end. It consumes the predictions, preventive actions, and execution outcomes generated by the Agentic AI Predictor (Use Case 1) and presents them in a unified view for operational teams." |
| Line 57-62 (Functions) | "Key Functions of the dashboard include: End-to-End Integration Visibility; Early Warning Risk Indicators highlighting AI-predicted high-risk transactions; Transaction Aging & Health Indicators using a color-coded Red/Amber/Green view; Preventive Action Tracking for retries and re-syncs; and Centralized Operational Monitoring for faster decision-making." |
Slide 7: Solution Details - Use Case 1 (Predictor)
(Provide technical details on the AI agent)
| Line | Script |
| Line 63-64 | "Let's look at the technical Solution Details for the Integration Lag Predictor." |
| Line 65-67 | "The Agentic AI agent on Google Cloud connects to Oracle Fusion and middleware to observe WMS $\to$ OM events in real time. It analyzes historical logs, timestamps, errors, and processing durations to predict potential lag or failure risks for in-flight transactions." |
| Line 68-71 (Preventive Actions) | "Based on predefined policies, the agent takes preventive actions. This includes: sending proactive notifications; automatically triggering integration re-sync or retry for eligible transactions; and only escalating when automatic retries fail." |
| Line 72 | "All predictions and actions are recorded to be consumed by the Visibility Dashboard (Use Case 2)." |
Slide 8: Solution Details - Use Case 2 (Dashboard)
(Provide technical details on the dashboard implementation)
| Line | Script |
| Line 73-74 | "And here are the Solution Details for the Early Warning Integration Visibility Dashboard approach." |
| Line 75-76 | "We are developing a Visual Studio–based application to extract integration data from Oracle Fusion and consume the prediction outputs from the AI agent. This application will process and normalize the data, storing it in a central SQL database." |
| Line 77-83 | "Power BI Embedded is then used to create a real-time dashboard directly on top of this database, which eliminates the need to build a custom web application. This dashboard will provide a unified view of all WMS $\to$ OM transactions, processing status, AI-flagged high-risk items, and color-coded aging indicators. Power BI alerts will be configured to send notifications when risk conditions are met." |
| Line 84 | "Support teams access the dashboard through Power BI Service, ensuring fast adoption and minimal maintenance." |
Slide 9: Thank You
| Line | Script |
| Line 85 | "Thank you for your time. We are now open for any questions regarding the AI-Driven Predictive Monitoring System." |
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