Translate data from ERP systems and supplier logistic escalation dashboard to predict parameter optimization, thereby creating an early warning system
Objective
The objective of this assignment is to develop a data-driven early warning system by translating and integrating data from ERP systems and supplier logistics escalation dashboards. This should be done by using Artificial Intelligence (AI) to identify key performance drivers, predict potential disruptions, and trigger early warnings. By applying predictive analytics and optimizing critical parameters, the assignment aims to proactively prevent logistics issues and improve overall supply chain performance and decision-making.
Background
Currently, Frencken is performing a root cause analysis to identify recurring issues in supplier logistics and supply chain performance using ERP data and escalation dashboards (in a dedicated assignment). While this analysis provides insight into historical problems, it remains primarily reactive. This assignment builds on these findings by translating identified patterns and drivers into a proactive, data-driven approach, aiming to predict disruptions and trigger early warnings, possibly using AI for this.
Main Tasks
• Analyze and structure data from ERP systems and supplier logistics escalation dashboards
• Identify key performance drivers and root causes indicative of early warnings
• Develop predictive models to forecast potential logistics issues
• Apply parameter optimization to improve supply chain performance
• Design and implement an (AI-driven) early warning system
• Validate the solution and translate it into a practical dashboard or prototype
Deliverables
• Analysis of key performance drivers and root causes indicative of early warnings
• Predictive early warning system for possible supply chain disturbances
• Prototype dashboard or visualization tool
• Final report including methodology, results, and implementation roadmap
Enclosures:
Learning Outcomes for the Intern
• Ability to translate complex ERP and logistics data into actionable insights
• Experience with data analysis, predictive modeling, and AI applications in a supply chain context
• Understanding of key logistics performance drivers and optimization techniques
• Skills in designing and implementing data-driven decision-support tools
• Capability to develop an AI-driven early warning system
• Experience in stakeholder communication and translating business needs into analytical solutions
Ideal Profile
• Student (HBO/WO) Bachelor's or Master's in Business Administration, Supply Chain Management, Data Analytics, Commercial or Engineering
• Analytical mindset with strong problem-solving and communication skills.
• Experience or interest in data visualization (Excel, Power BI, or similar).
• Motivation to contribute to more resilient and sustainable supply chains.
Salarisomschrijving
In overeenstemming