Internal crossdock analysis and optimization – DB Schenker

 Overview

Company Description

DB Schenker Business Unit Land Transport Cluster the BeNeLux

Project Description

Project description:

In our crossdock locations in Tilburg, Ede, Oldenzaal, Zwevegem, Mechelen, Eupen and Contern we process groupage shipments throughout the Benelux and is part of a European network, the locations are in varying sizes and configurations. Due to fluctuating shipment volumes, these crossdocks face operational challenges, particularly in the design and utilization of temporary storage areas. The existing layout and internal processes at these crossdocks are not adequately optimized to handle these fluctuations, leading to inefficiencies and potential delays.

The objective of this project is to develop predictive models and redesign strategies that enable the company to anticipate the need for changes in the layout and internal processes of these crossdocks. The solution should focus on maximizing space utilization, improving operational efficiency, and ensuring scalability to handle varying volumes effectively. This project will require an analysis of current crossdock operations, identification of bottlenecks, and the application of data analytics to predict volume changes and propose actionable redesigns for the internal layouts of the crossdocks.

Goals of the project:

Enhance Operational Efficiency: Optimize the internal processes at each crossdock to handle fluctuating volumes efficiently, minimizing delays and maximizing throughput.

1. Improve Space Utilization: Redesign the temporary storage areas to maximize space utilization, adapting to the varying sizes and shapes of the crossdock facilities.
2. Develop Predictive Capabilities: Build a predictive model that forecasts volume fluctuations, enabling proactive adjustments to crossdock operations.
3. Ensure Scalability and Flexibility: Create a scalable and flexible redesign strategy that can be adapted to different conditions and crossdock configurations within the network.

Deliverables:

1. Operational Analysis Report:
   • Detailed assessment of current layouts and operational workflows at each crossdock.
   • Identification and documentation of existing bottlenecks and inefficiencies.
2. Data Analysis and Predictive Model:
   • Collection and analysis of historical data on shipment volumes and operational metrics.
   • Development of a predictive model capable of forecasting demand and operational impacts.
   • Validation report of the predictive model, including accuracy metrics and performance indicators.
3. Redesign Proposal Document:
   • Comprehensive redesign proposals for temporary storage areas within the crossdocks, including detailed plans and specifications.
   • Simulation results or case studies demonstrating the effectiveness of proposed redesigns.
   • Strategies for implementation of redesigns, including phased plans and resource requirements.
4. Implementation Toolkit:
   • Implementation guideline & checklist.
5. Final Presentation:
   • A comprehensive presentation summarizing the findings, model, proposed redesigns, and implementation strategies.
   • Recommendations for ongoing adjustments and continuous improvement measures.

Essential student knowledge:

Theoretical Knowledge:

1. Supply Chain Management:
• Understanding of logistics operations, especially in relation to groupage shipments and crossdock management.
• Knowledge of supply chain dynamics, including inventory management, warehouse operations, and transportation logistics.
2. Operations Research:
• Familiarity with optimization theories and techniques, including linear programming, queuing theory, and simulation modeling.
3. Statistics and Data Analysis:
• Proficiency in statistical analysis and data modeling to handle large datasets and derive insights.
• Ability to apply statistical methods to forecast and analyze operational data.
4. Industrial Engineering:
• Concepts of system design and ergonomics, particularly in optimizing layout and operational efficiency in physical spaces like warehouses or docks.
5. Project Management:
• Basic principles of project management including planning, execution, monitoring, and closing projects effectively.

Practical Skills:

1. Data Science and Analytics:
• Proficiency in using data analysis software and tools (e.g., Python, R, MATLAB) for data cleaning, analysis, and predictive modeling.
• Experience with machine learning algorithms that can be used for forecasting and optimization.
2. CAD and Simulation Software:
• Skills in using computer-aided design (CAD) tools for designing layouts and simulation software to test and validate redesign proposals.
3. Problem Solving and Critical Thinking:
• Strong analytical skills to diagnose problems, identify viable solutions, and evaluate their potential impacts.
4. Communication and Presentation:
• Ability to clearly articulate findings, analyses, and recommendations both in written reports and presentations.
5. Adaptability and Learning:
• Willingness and ability to quickly learn about specific operational nuances of the company’s logistics and crossdock operations.

Soft Skills:

1. Collaboration:
• Ability to work effectively in teams, including with professionals from different backgrounds and expertise levels.
2. Attention to Detail:
• Precision and thoroughness in analyzing data and developing models and redesigns.
3. Proactive Thinking:
• Initiative in identifying potential future challenges and proposing pre-emptive solutions.

More information: escf@tue.nl