A newer version of the Streamlit SDK is available: 1.62.0
metadata
title: Workforce Optimization AI
emoji: 📞
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.56.0
app_file: app.py
pinned: false
📞 Workforce Optimization AI / İş Gücü Optimizasyonu
This project is an AI-driven decision support system designed to optimize staffing levels based on historical call/demand data. It uses Long Short-Term Memory (LSTM) networks to predict future demand and suggests actionable staffing strategies.
Bu proje, geçmiş çağrı/talep verilerine dayanarak personel seviyelerini optimize etmek için tasarlanmış yapay zeka destekli bir karar destek sistemidir. Gelecekteki talebi tahmin etmek için LSTM ağlarını kullanır ve uygulanabilir personel stratejileri önerir.
🚀 Features / Özellikler
- AI Demand Forecasting: Predicts future call volumes using deep learning.
- Strategic Recommendations: Automatically identifies "High Intensity" (Risk) and "Saving Area" (Opportunity) periods.
- Cost Optimization: Helps managers balance service quality and labor costs.
- Interactive Visualization: Dynamic charts showing demand vs. staff capacity.
🛠️ Tech Stack / Teknolojiler
- Python (Core Logic)
- TensorFlow/Keras (LSTM Model)
- Streamlit (Web Interface)
- Plotly (Interactive Graphics)
- Scikit-Learn (Data Scaling)
📖 How to Use / Nasıl Kullanılır?
- Upload Data: Upload a CSV file containing a column named
calls. - Set Parameters: Adjust hourly wage and staff capacity per hour from the sidebar.
- Analyze: - View the Decision Logic to understand AI suggestions.
- Check the Strategic Recommendations for peak and low-demand periods.
- Download the Detailed Schedule for operational planning.
🧠 Strategic Logic / Stratejik Mantık
- High Intensity (🔥): When predicted demand exceeds capacity. Action: Increase staff to prevent customer loss.
- Saving Area (💰): When demand is significantly lower than capacity. Action: Reduce staff or plan breaks to minimize labor waste.