Using a Digital Twin of the Planning Algorithm to Identify Missing Factors in Operations Planning

 Overview

Company Description

Ewals Cargo Care is a family-owned transportation company, founded in 1906 by Alfons Ewals. The headquarters is located in Tegelen near Venlo, with 32 additional offices across Europe, ensuring a local presence in 15 countries and employing over 2,500 people.

Ewals has grown into a strong international logistics player, offering a wide spectrum of services ranging from full- and part-loads to Control Tower solutions. Especially, but certainly not exclusively, we are known  for our European multimodal network that includes more than 4500 Mega Huckepack XL(S) trailers. Supported by a solid partner network, Ewals continuously improves processes and invests in employee development.

Project Description

In a previous thesis project, Ewals developed a first version of an automatic planning algorithm to support transport planning. While this algorithm is operational, significant differences remain between its proposed planning and the actual planning decisions made in practice. These discrepancies are largely due to missing or incomplete data that does not fully capture the complexity of real-world planning.

The next step is to use the existing algorithm as a digital twin of the planning process. This digital twin will serve as a controlled environment to compare algorithmic proposals with actual planning outcomes, enabling the identification of missing factors and constraints that influence planning decisions but are not yet represented in the data.

The project will focus on:

• Setting up a the digital twin correctly in our architecture
• Systematically analyzing differences between algorithmic proposals and real-world planning.
• Identifying missing parameters, constraints, and qualitative factors (e.g., customer preferences, driver availability, operational exceptions).
• Developing methods and a strategy to incorporate these missing factors into the algorithm.
• Enhancing transparency by visualizing discrepancies and their root causes for planners.
• Design feedback loops so that deviations and user input can be systematically integrated into future planning proposals.

The focus for the student will be on strategic advice regarding the architecture and positioning of the algorithm within Ewals’ business processes. This includes analyzing how the digital twin can be embedded into daily operations, how transparency and usability can be ensured, and how the system architecture should evolve to support sustainable integration.

In line with academic literature, the thesis will build upon the development and validation of conceptual models. These models will provide the framework for understanding and improving the interaction between technology and business processes.

Goals of the project

• Establish the existing algorithm as a digital twin of the planning process.
• Identify and document missing factors that cause discrepancies between algorithmic and real-world planning.
• Provide strategic recommendations on the architecture and positioning of the algorithm within Ewals’ business processes.
• Provide recommendations on how to enrich data and integrate these factors into the planning model.
• Improve the reliability and transparency of the planning algorithm for future deployment.

Deliverables

• A working digital twin tuned to the Ewals business processes which can be adjusted easily to test new configurations
• A structured analysis of discrepancies between algorithmic proposals and actual planning outcomes.
• A catalog of missing parameters and constraints influencing planning decisions.
• A methodology for integrating these factors into the digital twin model. And ideally also incorporate a set off them already
• A report detailing findings, proposed improvements, and guidelines for future system integration.

Knowledge

This project provides an exciting opportunity to investigate in actual practical logistic challenges on yet unexplored territory within Ewals. It will not only equip you with valuable experience in these advanced technologies but also contribute meaningfully to a more sustainable and efficient logistics industry. You are expected to be able to explain the methods and techniques used, and you have the ability to put this into practice at the intersection of IT and business process management.

• Programming knowledge (preferential Python, SQL)
• Supply chain knowledge and affinity (Transport and Logistics)
• Be able to design and validate conceptual models from literature.
• Communicate findings clearly to both technical and operational stakeholders, with a focus on strategic advice and process integration.

More information: escf@tue.nl