🏗️ PrediChain: AI-Powered Materials Demand Forecasting for Civic Infrastructure

⚙️ Project Overview

PrediChain is an AI-driven forecasting and procurement optimization system designed for civic infrastructure projects — helping organizations predict future material demand with high accuracy.

By leveraging machine learning, PrediChain enables smart supply chain planning, timely procurement, and optimized inventory levels for key construction materials like cement, steel, sand, and bricks.

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🚀 Problem Statement

In civic infrastructure, procurement inefficiencies often lead to:

• Overstocking (wasted capital and storage costs)
• Stockouts (delayed projects and cost overruns)
• Poor visibility into upcoming demand

Traditional planning relies on manual estimates that ignore seasonality, lead times, and external factors.
PrediChain solves this by forecasting future material demand using machine learning — transforming how supply chains in public infrastructure are managed.

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🎯 Objective

To build a predictive system that:

• Accurately forecasts monthly demand for key construction materials.
• Incorporates project, regional, and seasonal variables.
• Recommends optimal order quantities, safety stock, and reorder points.
• Supports data-driven procurement and inventory decisions.

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🏗️ Domain Focus

Civic Infrastructure Construction

PrediChain focuses on public infrastructure such as roads, bridges, and municipal buildings — where timely material availability is crucial.
The system forecasts demand for:

• 🧱 Cement
• 🔩 Reinforcing Steel (Rebar)
• 🪨 Sand & Aggregates
• 🧱 Bricks
• 🧰 PVC Pipes, Electrical Wiring, Paint, Tiles

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📊 Data Sources

PrediChain utilizes (or simulates) data such as:

Data Type	Example Fields
Historical usage	month, material, quantity_used
Project data	project_id, region, project_type, start_date, end_date
Procurement	lead_time, order_date, delivery_date
External	rainfall_mm, commodity_price_index, permits_issued, holiday_flag

If real datasets are unavailable, a synthetic dataset can be generated to simulate realistic construction material consumption trends.

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🧠 Machine Learning Approach

1. Data Preprocessing

• Time-based aggregation (monthly per material)
• Handling missing values and outliers
• Feature generation (lags, rolling averages, seasonality, project pipeline)

2. Modeling Techniques

• Baseline Models: Moving Average, Exponential Smoothing
• Forecasting Models: Prophet, ARIMA/SARIMA
• Machine Learning Models: XGBoost, Random Forest for multivariate regression
• Hybrid Ensemble: Prophet + XGBoost for accuracy and interpretability
• Intermittent Demand: Croston’s method for rarely used materials

3. Evaluation Metrics

• MAE (Mean Absolute Error)
• RMSE (Root Mean Square Error)
• sMAPE (Symmetric Mean Absolute Percentage Error)
• Cost-based metrics (holding & stockout cost impact simulation)

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🧾 Procurement Optimization Layer

Using predicted demand, PrediChain computes key operational insights:

Formula	Purpose
Safety Stock = z * σ * sqrt(LeadTime)	Ensures buffer stock
Reorder Point = DemandDuringLeadTime + SafetyStock	Optimal reorder trigger
EOQ = sqrt((2 * D * OrderingCost) / HoldingCost)	Minimizes total cost

These formulas guide when and how much to order for each material, directly reducing waste and delays.

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💻 System Architecture

        ┌──────────────────────┐
        │ Historical Data (CSV)│
        └──────────┬───────────┘
                   │
                   ▼
       ┌──────────────────────┐
       │ Data Preprocessing   │
       └──────────┬───────────┘
                   │
                   ▼
      ┌────────────────────────┐
      │ ML Model (Prophet/XGB) │
      └──────────┬─────────────┘
                   │
                   ▼
  ┌────────────────────────────────┐
  │ Forecast Output & Procurement   │
  │ Recommendations (ROP, EOQ)      │
  └────────────────────────────────┘
                   │
                   ▼
     ┌─────────────────────────────┐
     │ Interactive Dashboard (UI)  │
     │ Streamlit / React + Charts  │
     └─────────────────────────────┘

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📈 Key Features

✅ Real-time demand forecasting per material
✅ Seasonality & weather-aware predictions
✅ Procurement decision engine (ROP, EOQ)
✅ Cost & risk simulation (stockout vs overstock)
✅ Explainable forecasts (feature importance via SHAP)
✅ Visual dashboard for planning & what-if analysis

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🧩 Tech Stack

Component	Technology
Backend / ML	Python (Pandas, Scikit-learn, Prophet, XGBoost)
Data Storage	CSV / Firebase / SQLite
Visualization	Matplotlib, Plotly, Seaborn
Dashboard / Frontend	Streamlit or React.js
Deployment	Replit / Render / Streamlit Cloud

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📂 Project Structure

📦 PrediChain

├── 📁 backend

│   ├── 📁 __pycache__
        └── 📄 app.cpython-313.pyc

│   ├── 🤖 ml
        │   ├── 📁 __pycache__/
        │   ├── 📄 __init__.py
        │   ├── 📄 forecast.py           # Forecasting logic (Prophet, etc.)
        │   ├── 📄 recommendation.py     # Material recommendation logic
        │   └── 📄 utils.py              # Helper functions (data cleaning, validation)

│  ├── 📁 data
        │   ├── 📁 uploads               # User-uploaded datasets
        │   │   ├── 📄 generate_sample_data.py
        │   │   └── 📄 realistic_project_data.csv
        │   ├── 📁 forecasts             # Generated forecast outputs
        │        └── 📄 cement_forecast.csv

│  ├── ⚙️ app.py                    # FastAPI app entry point
│  ├── ⚙️ .env
│  ├── 📄 requirements.txt          # Dependency list
│  ├── 📄 generate_sample_data.py   # Script to generate sample/synthetic data
│  ├── 🧠 venv                      # Virtual environment
│  │   ├── 📁 Include/
│  │   ├── 📁 Lib/
│  │   ├── 📁 Scripts/
│  │   ├── 📁 share/
│  │   ├── 📄 .gitignore
│  │   └── 📄 pyvenv.cfg
│  └── 📄 .gitignore

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📦 PrediChain Frontend folder structure

├── 📁 frontend
│   ├── 📁 predichain-frontend
│   │   ├── 📁 public
│   │   │   ├── 📄 index.html
│   │   │   ├── 📄 manifest.json
│   │   │   └── 📄 robots.txt
│   │   │
│   │   ├── 📁 src
│   │   │   ├── 📁 components
│   │   │   │   ├── 📄 axiosConfig.js
│   │   │   │   ├── 📄 BenefitCard.jsx
│   │   │   │   ├── 📄 Chatbot.jsx
│   │   │   │   ├── 📄 ComparisonChart.jsx
│   │   │   │   ├── 📄 FeatureCard.jsx
│   │   │   │   ├── 📄 Footer.jsx
│   │   │   │   ├── 📄 Hero.jsx
│   │   │   │   ├── 📄 HistoricalVsForecastChart.jsx
│   │   │   │   ├── 📄 Layout.jsx
│   │   │   │   ├── 📄 LiquidEther.js
│   │   │   │   ├── 📄 LiquidEther.css
│   │   │   │   ├── 📄 MapPreview.jsx
│   │   │   │   ├── 📄 Mapsection.jsx
│   │   │   │   ├── 📄 MaterialForecastChart.jsx
│   │   │   │   ├── 📄 MaterialTable.jsx
│   │   │   │   ├── 📄 MetricCard.jsx
│   │   │   │   ├── 📄 Navbar.jsx
│   │   │   │   ├── 📄 RecommendationPanel.jsx
│   │   │   │   ├── 📄 Sidebar.jsx
│   │   │   │   ├── 📄 ThemeToggle.jsx
│   │   │   │   └── 📄 Topbar.jsx
│   │   │   │
│   │   │   ├── 📁 pages
│   │   │   │   ├── 📄 Auth.jsx
│   │   │   │   ├── 📄 CSVUpload.jsx
│   │   │   │   ├── 📄 Dashbaord.jsx
│   │   │   │   ├── 📄 FullMap.jsx
│   │   │   │   ├── 📄 Home.jsx
│   │   │   │   └── 📄 ProjectManagement.jsx
│   │   │   │
│   │   │   ├── 📁 Services
│   │   │   │   ├── 📄 dashboardService.js
│   │   │   │
│   │   │   ├── 📁 utils
│   │   │   │   ├── 📄 api.js
│   │   │   │
│   │   │   ├── 📄 App.js
│   │   │   ├── 📄 index.js
│   │   │   ├── 📄 firebase.js
│   │   │   └── 📄 index.css
│   │   │
│   │   ├── ⚙️ .env
│   │   ├── 📄 .gitignore
│   │   ├── 📄 firebaseConfig.js
│   │   ├── 📄 package-lock.json
│   │   ├── 📄 package.json
│   │   ├── 📄 postcss.config.js
│   │   ├── 📝 README.md
│   │   ├── 📄 tailwind.config.js
│   │
│   ├── 📄 postcss.config.js
│   ├── 📄 tailwind.config.js
│   ├── 📄 package.json
│   └── 📄 package-lock.json

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## 📉 Sample Output

Month	Material	Forecast (Tons)	Safety Stock	Reorder Point	Suggested Order
Nov 2025	Cement	420	40	460	480
Dec 2025	Steel	280	30	310	300

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🌍 Impact

• ⏱️ Reduced procurement delays by up to 30%
• 💰 Lowered inventory holding cost through precise ordering
• 🌱 Sustainability: minimized wastage of raw materials
• 🧮 Data-driven governance: supports transparent planning in civic projects

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🧩 Future Enhancements

• Integration with live procurement ERP systems
• Incorporate satellite data or real-time weather feeds
• Apply Reinforcement Learning for adaptive ordering policies
• Multi-tier forecasting (material → project → regional → national level)
• Predict supply risk based on vendor reliability

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🧑‍💻 Team PrediChain

• Domain: Civic Infrastructure
• Focus: Data-driven demand forecasting & inventory optimization
• Goal: Build a sustainable and intelligent materials supply chain ecosystem.

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⚡ How to Run (Local)

# Clone the repository
git clone https://github.com/yourusername/PrediChain.git
cd PrediChain

#Run backend
# Install dependencies
pip install -r requirements.txt

# Run the notebook or dashboard
streamlit run app.py

#Run frontend
# Go to frontend folder
cd frontend/predichain-frontend

# Install dependencies
npm install

# Run React app
npm start