Job Title Graduation: Supply chain risk assessment through an analytical digital twin Job Description Assignment type: Graduation Start date: January / February 2027 Assignment duration: 6 months Location: Veghel Education level: Master Desired study: Data Science/Data(AI) Engineering, Industrial Engineering, Operations Research Language: Dutch / English Description of assignment Modern projects depend on complex supply chain and logistics networks in which suppliers, transport movements, production activities, internal handling and project milestones are closely connected. Delays in one activity can propagate through the network and ultimately threaten the committed project delivery date. At the same time, projects contain different amounts of available slack, meaning that some delays can be absorbed while others immediately create a delivery risk. Vanderlande has a substantial amount of historical and operational logistics data available through its Data Platform. The challenge is to translate this data into actionable insight into the performance and resilience of the logistics network. By combining information on logistics activities, dependencies, historical performance and project slack, the student will investigate how emerging delivery risks can be identified and assessed at an early stage. The research will involve mapping the relevant logistics network and its dependencies, determining available slack for projects and orders, identifying relevant risk indicators and developing a data-driven analytical approach. Depending on the findings, the solution may take the form of a predictive risk model, a network optimization approach, or a combination of the two. Department description The CDAO (Central Data & Analytics Organization) is responsible for managing and developing the company’s Data Platform and for turning company data into solutions that support business operations and decision-making. CDAO works across the organization to make data accessible, reliable and usable for analytical and digital applications. CDAO works closely with the Supply department and other business stakeholders to develop data-driven solutions for complex supply chain and logistics challenges. One of these solutions is the development and application of a digital twin, which brings together data from different processes and systems to create a more comprehensive view of the supply chain and enable advanced analysis and decision support. The student will work within CDAO, and specifically in the Business Enablement Team(BET), while collaborating closely with Supply and other relevant stakeholders. The work environment combines research and development with practical business interaction. The student will have access to data, technical expertise and domain knowledge and will be expected to translate a real supply chain challenge into a working analytical solution. Tasks/responsibilities Investigate the supply chain processes, systems, data sources and dependencies relevant to the digital twin. Analyse and structure historical and operational data available through the Data Platform. Identify the key supply chain processes, relationships and performance indicators to be represented in the digital twin. Develop and implement digital twin components using appropriate programming and data-analysis techniques. Explore and evaluate relevant applications of the digital twin, such as delivery risk assessment, predictive analysis, scenario analysis and supply chain optimization, using historical or operational data. Translate the findings into practical recommendations for CDAO and the Supply department, and document the methodology, results and limitations in a research report. Your profile Must-have: Strong analytical and problem-solving skills, with the ability to independently structure an open-ended research problem. Genuine interest in programming and software development, as these will be substantial parts of the research. Experience with Python, SQL and relevant data-analysis libraries. Good communication skills and willingness to collaborate with data specialists and supply chain experts. *Mandatory enrolment to a Dutch Education System & resident of The Netherlands Nice-to-have: Knowledge of databases and experience working with large datasets. Knowledge of operations research, optimization, supply chain management or logistics, and/or experience with digital twins or other data-driven models. What we offer A monthly internship allowance (based on a 40-hour work week):€550 per month for a master student Full commuting allowance, including public transport, depending on whether you have access to free public transport or not On-site health center with gym Active employee communities and initiatives Excellent company restaurant and barista coffee bar Contact Do you recognize yourself in this challenging profile? Are you looking for an internship in our organization? Please fill out the application form and upload your resume and cover letter. For more information, contact us by e-mail: [email protected]. Every day, millions of people rely on Vanderlande without even knowing it. From baggage checked in at the airport to parcels on their way to someone’s doorstep, our automation solutions help keep the world moving. Vanderlande designs, delivers, and supports automation technology, operational software, and lifecycle services for leading airports and parcel operations worldwide. Across more than 100 countries, our customers rely on us to keep their operations moving reliably, efficiently, and sustainably. Behind every solution are the people who make it happen. At Vanderlande, how we work matters just as much as what we deliver. Our values, Every Day Better, We Care, and Team Play, guide how we collaborate, support each other, and create value for our customers. Here, you can work with colleagues across disciplines and cultures, contribute to work that makes a difference, and build your future in an organisation that invites you to grow. Explore what is possible at Vanderlande and discover how you can make an impact. Aspire. Grow. Achieve. Together.

Graduation: Supply chain risk assessment through an analytical digital twin.

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