Create README.md

supplier_id,supplier_name,country,lead_time_days,quality_rating,contract_status
SUP001,PT Global Steel,Indonesia,14,4.5,Active
SUP002,Asia Raw Material Ltd,Singapore,21,4.2,Active
SUP003,Logam Jaya Abadi,Indonesia,10,4.7,Active
SUP004,Pacific Industrial Co,China,30,4.0,Review
SUP005,Indo Component Supply,Indonesia,7,4.8,Active

README.md

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+---
+license: mit
+task_categories:
+- tabular-classification
+- time-series-forecasting
+- anomaly-detection
+language:
+- en
+tags:
+- enterprise-ai
+- industrial-analytics
+- global-logistics
+- supply-chain-intelligence
+- operational-risk-modeling
+- sustainability-analytics
+- demand-forecasting
+- smart-warehouse
+size_categories:
+- n<1K
+---
+
+# Global Enterprise Logistics & Supply Chain AI Dataset (Corporate Edition 2024)
+
+## Corporate Overview
+
+This dataset represents a high-level enterprise simulation of global logistics and supply chain operations.  
+It is designed to reflect the operational complexity of multinational corporations managing multi-regional distribution centers, cross-border trade routes, and diversified product portfolios.
+
+The dataset integrates operational efficiency metrics, forecasting performance indicators, supplier reliability scoring, transportation risk modeling, sustainability tracking, and AI-ready anomaly classification signals.
+
+---
+
+## Strategic Coverage
+
+The dataset simulates:
+
+- Multi-region warehouse operations (Asia-Pacific, North America, Europe)
+- Cross-functional business units
+- Inventory risk management & safety stock modeling
+- Forecast vs actual demand comparison
+- Fulfillment performance analytics
+- Transportation cost & delay risk modeling
+- Carbon emission tracking & sustainability monitoring
+- Labor & automation performance benchmarking
+- Operational anomaly labeling for supervised AI training
+
+---
+
+## Enterprise AI Applications
+
+Suitable for advanced AI system development including:
+
+- Multi-variable demand forecasting
+- Inventory optimization modeling
+- Supply chain risk prediction
+- Anomaly detection in logistics operations
+- ESG (Environmental, Social, Governance) analytics modeling
+- Cost-efficiency optimization
+- Industrial automation benchmarking
+- Enterprise digital twin simulation
+
+---
+
+## Data Architecture
+
+Each record represents a time-stamped operational snapshot of a logistics facility.
+
+Data fields include:
+
+- Operational metrics
+- Forecasting variables
+- Financial indicators
+- Sustainability indicators
+- Risk assessment scores
+- AI classification label
+
+---
+
+## Technical Format
+
+- CSV (Comma-Separated Values)
+- UTF-8 Encoding
+- Structured Tabular Format
+- AI Training Ready
+
+---
+
+## Intended Audience
+
+- Enterprise AI Engineers
+- Supply Chain Data Scientists
+- Industrial Systems Analysts
+- Logistics Optimization Researchers
+- Corporate Digital Transformation Teams
+
+---
+
+## License
+
+MIT License – Available for research, AI experimentation, and industrial simulation.