# Process-Aware AI: Project Overview

## 1. Goal & Vision
The primary goal of **Process-Aware AI** is to **optimize Greige Issuance** in textile manufacturing. 
The system aims to replace static, "one-size-fits-all" norms with **dynamic, data-driven recommendations** that minimize waste (surplus fabric) while ensuring order fulfillment (preventing shortfalls).

## 2. The Problem
*   **Static Norms**: Traditional planning uses fixed rules (e.g., "Always add 5% process loss").
*   **Inefficiency**: 
    *   **Over-issuing**: Wastes raw material (Cotton, Tencel, etc.) and increases deadstock.
    *   **Under-issuing**: Causes "shortfalls" (orders not fulfilling demand), requiring expensive reprocessing or urgent small-batch productions.
*   **Lack of Feedback**: Planners rarely see if their "buffer" was actually needed or if it caused waste.

## 3. The Solution
We have built an **AI-Driven Decision Intelligence System** that:
1.  **Analyzes History**: Looks at every past order for a specific article.
2.  **Evaluates Outcomes**: Did X% reservation succeed? Did Y% fail?
3.  **Recommends Precision**: Suggests the *exact* reservation needed to succeed based on historical performance, not just a guess.

## 4. Key Achievements (Current Status)
*   **✅ Data Pipeline**: Successfully ingesting Sale Orders, Norm Rules, and Production Data.
*   **✅ Interactive Dashboard**:
    *   **Global Views**: Trends, Norm deviations.
    *   **Article Drill-down**: Deep dive into specific fabric behaviors.
*   **✅ "Outcome-Based" AI Engine**:
    *   Moved away from simple averages (which are skewed by outliers).
    *   implemented **Success-Based Logic**: Recommends the median reservation of *successful* orders.
    *   implemented **Failure-Safe Logic**: If an article has 0% success history, analyzes *why* it failed and recommends a robust buffer to ensure future success.
*   **✅ Scenario Playground**: Allows planners to simulate "What if we changed the norm to X%?" to see financial and operational impact.

## 5. Value Proposition
*   **Reduce Waste**: Identify articles where standard norms (e.g., 5%) are too high compared to actual needs (e.g., 2%).
*   **Prevent Failures**: Identify "Same-Norm" articles that frequently fail and require higher buffers.
*   **Standardization**: Reduce dependency on individual planner intuition by providing a standardized, data-backed baseline.