Master Thesis: Hidden Lifecycle Costs, Portfolio Evolution and Circular Value through Advanced Anal

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About this opportunity:

Are you ready to uncover hidden costs and stranded value across complex product lifecycles? In this master's thesis, you will investigate how product structures, supplier commitments, demand, consumption, inventory, component costs, and end-of-life decisions interact across Ericsson's supply chain.

Using data mining, advanced analytics, AI-enabled discovery, and stakeholder interviews, you will reconstruct the lifecycle story of selected products and identify opportunities to reduce liabilities, prevent value leakage, and increase component reuse.

You will work independently while collaborating with experts in Supply, Finance, Sourcing, Product Engineering, with particular focus on working closely with Product Portfolio and Cost Management within NPI and Production. Your findings will support transformation in supply, product portfolio, circularity, and cost optimization.

What you will do:

• Connect product and submodule structures with supplier, demand, consumption, price, inventory, cost, and lifecycle data from Snowflake, SWB, Power BI, documents, and expert knowledge.
• Compare supplier commitments, production and delivery volumes, actual consumption, future demand, and financial liabilities to identify lifecycle value leakage.
• Analyze component costs and design evolution across product generations, including ASICs, antennas, and filters.
• Assess lifecycle costs and risks, including NRE, inventory holding, scrap, obsolescence, supplier liabilities, excess stock, last-time buys, and phase-outs.
• Identify component commonality, modularity, reuse opportunities, and recoverable value across products, submodules, phased-out products, and buffer inventories.
• Validate findings through stakeholder interviews and deliver a prioritized report with evidence, conclusions, business impact, improvement opportunities, and recommendations for further investigation.

The skills you bring:

• Background in Industrial Engineering, Supply Chain Management, Data Analytics, Computer Science, Business Analytics, Operations Management, or a related field
• Strong analytical mindset and curiosity for solving complex business problems with data
• Ability to work with large datasets and create clear, decision-oriented visualizations
• Knowledge of SQL and data-querying concepts; experience with Python, Power BI, Snowflake
• Interest in applying AI and Large Language Models to business and unlocking value from fragmented data
• Interest in supply chain, procurement, manufacturing, product lifecycle management, circularity, and cost optimization
• Strong communication skills and confidence conducting structured interviews across functions
• Ability to work independently, collaborate across the organization, and turn evidence into actionable insights

Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: Sweden (SE) || Stockholm

Req ID: 791434