data_visualisation

SunCharge Supply Chain Monitoring System

Part 1. Metadata

• Version: design (25/3/2024)
• Students:
  • Jørgen Aleksander Arnesen, r-0978820, KU Leuven
  • Daria Małgorzata Plewa, r-0976669, KU Leuven
  • Beste Karataş, r-0973831, KU Leuven
• Group number: group_21
• Dataset: SunCharge

Part 2. Project Description

Overview

SunCharge, a battery manufacturer, provided a dataset covering their supply chain. Our task is to develop a monitoring system to detect issues and identify improvements.

Key Features

• Vendors: Raw material suppliers and contributions.
• Production Plants: Data on production time, cost, processing time, and lead time.
• Distribution Centers: Inventory, order quantities, and sales forecasts.

Analysis Focus

1. Order Timeliness: Correlations between late deliveries and variables like material type, order quantity, and production plants.
2. Sales Forecast Accuracy: Comparing actual sales with predicted sales to evaluate forecast accuracy.

Part 3. Visual Design

Final Visualization

1. Map and Graph Integration:

   • Map of Europe: Highlights distribution centers and production plants.
   • Interactive Elements: Clicking on a center displays detailed graphs.
   • Scatterplot and Pie Chart: Shows transportation time differences and stage contributions.

2. Sales Prediction Visualization:

   • Line Chart: Compares actual sales to predicted sales.
   • Interactive Map: Allows toggling between different visualizations.

Part 4. Implementation

Visualization 1: Supply Chain Monitoring

• Map of Europe: Color-coded distribution centers.
• Interactive Features: Dropdown menus for filtering data.
• Graphs: Combined line and bar charts for order quantities with hover details.
• Scatterplot: Shows transportation time differences.

Visualization 2: Sales Prediction

• Integrated with Visualization 1: Same map and interactive features.
• Toggle Feature: Switch between different graphs for a cohesive user experience.

Part 5. Findings

1. Shipment Anomalies: Identified outliers in Greece and Baltic States.
2. Order Quantity Correlation: Larger order quantities often correlate with delays.
3. Sales Forecast Accuracy: Generally accurate, with some early-month inaccuracies.
4. Top Products and Markets: EV Car Batteries are the most purchased product, with Germany being the largest buyer.

Part 6. Challenges

• Pie Chart Glyphs: Difficult to implement.
• Database Optimization: Current setup limits performance.

Part 7. Contributions

• Daria: Backend database, data integration, report writing.
• Beste: Initial map setup, frontend work (JS, HTML, CSS), report writing.
• Jørgen: Frontend work, report writing.

Links

• YouTube Video Showcase
• GitHub Repository

Explore our GitHub repository for the full project code and detailed visualizations.