This project analyzes Maersk's supply chain to identify inefficiencies and optimize operations. Using Python for data manipulation and Power BI for dashboards, the study focuses on inventory management, shipping operations, and customer demand. Key outputs include enhanced inventory segmentation, detailed analysis of shipment delays, and strategic recommendations for a more efficient and responsive supply chain.
The project aims to:
- Analyze key challenges in shipment and inventory management.
- Utilize analytical approaches to propose actionable insights.
- Enhance decision-making processes within the supply chain through robust data visualization tools.
- Business Demand Analysis: Create interactive dashboards to identify and address inefficiencies.
- Tools Used:
- Python: Data preprocessing, cleaning, and exploratory data analysis (EDA).
- Power BI: Development of interactive dashboards.
- Sales Management Dashboard: Tracks sales, profits, orders, and customer activities.
- Inventory Management Dashboard: Focuses on inventory status, order fulfillment, and distribution costs.
- Shipping Management Dashboard: Monitors shipping operations and timeliness.
- Data Cleaning: Simplified datasets by removing irrelevant columns, correcting data types, and resolving data inconsistencies.
- Feature Engineering: Introduced features to assess shipment timeliness and calculate key business metrics.
Performed in-depth analysis to explore:
- Business performance metrics.
- Customer demographics and buying behaviors.
- Product preferences and profitability.
- Inventory levels and management efficiency.
Utilized ABC XYZ segmentation to categorize inventory based on revenue contribution and demand stability, aiding in prioritized management and optimization.
- High-Demand Segments (XA, YA): Reintroduce high-demand segments with new suppliers and an improved supply chain design.
- Emerging Segments (XB, XC): Conduct market research to explore and capitalize on the growth potential of these segments.
- Reduce Low-Performance Segments: Discontinue or minimize inventory in low-demand segments to optimize storage costs.
- Demand Forecasting: Implement advanced forecasting and reorder points to manage inventory levels effectively.
- Transportation Optimization: Redesign transportation routes to improve delivery efficiency.
- Local Logistics Collaboration: Partner with local logistics to enhance delivery capabilities, especially in remote markets.
- Strategic Warehouse Placement: Establish warehouses in strategic locations like Singapore to improve international market serviceability.
- Identified trends influencing inventory and shipping.
- Suggested strategic adjustments to improve supply chain responsiveness and efficiency.
This project was completed as part of the coursework for a Supply Chain Analytics Class at McCombs School of Business, University of Texas at Austin, where the practical application of analytical techniques in real-world scenarios was emphasized along with the Supply Chain Fundamentals Concepts