Optimization of the NXP global supply chain simulation model – NXP Semiconductors Netherlands B.V.



Company Name / Department

NXP Semiconductors Netherlands B.V.

Contact Person

Eric Weijers

 Location HTC60, Eindhoven
Optional remote work Yes, number of days to be agreed upon in consultation
Travel expenses (own account or reimbursed by the company) No
Housing arranged by company No
Housing expenses (how much per month, own account or subsidized by the company) Own account
Internship compensation  400 euro per month
Study program Industrial Engineering / Computer Science / Mechanical Engineering
ESCF community

Full member

Start date

February, 2024


Company Description

    NXP Semiconductors enables a smarter, safer and more sustainable world through innovation. As the world leader in secure connectivity solutions for embedded applications, NXP is pushing boundaries in the automotive, industrial & IoT, mobile, and communication infrastructure markets.


    Project Description

    Project description:

    NXP has a complex supply chain to manufacture and deliver semiconductors to a wide range of customers. Currently, NXP uses a centralized/top-down LP model to generate supply chain plans based on capacity and demand. Due to the large scale of the models, run times are long and planners often need to make decisions without reconfirmation from the planning models. Literature states that simulation models can be developed to provide insights, decision support, and enable the use of optimization techniques, combined with limited computational and maintenance costs compared to LP models.

    Goals of the project:

    The supply chain simulation model needs to be optimized in terms of computational and maintenance costs such that it can be used in combination with AI optimization techniques, while accuracy for meaningful decision-making is still acceptable.


      • Research simulation model optimization techniques and implement the most promising one(s) in the NXP simulation model.
      • Quantify computational and maintenance cost savings of the proposed model optimizations.

      Essential student knowledge:

      • Affinity with supply chain management.
      • Experience with coding in Python or C++.
      • Proactive work attitude.
      • Team player.
      • Knowledge of discrete event simulation is a pre but not required.




        More information: escf@tue.nl  


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