Integrating Capacity Analysis and Should-Cost Modeling to Enhance Procurement Processes - An in-depth understanding of the supply and cost structures of aluminum foil in North America
Bibliographic record
Abstract
In the rapidly evolving electric vehicle (EV) battery industry, strategic supplier selection is crucial for securing a reliable and cost-effective supply chain. This thesis examines the implementation of a capacity analysis and a should-cost model to optimize supplier selection and contracting for Northvolt, a leading battery manufacturer, as it expands into North America. With Northvolt's new gigafactory in Montreal poised to commence production in 2026, establishing a robust network of local suppliers is imperative. The thesis focuses on answering one research question: How could a capacity analysis and shouldcost model contribute to the supplier selection, negotiation, and contracting processes of Northvolt? To answer this question, two research objectives must be achieved. Firstly, a capacity analysis was made with the purpose of mapping out the existing and future production capacity of battery cathode foil, along with the projected demand, corresponding carbon offsets, and environmental ambitions for the future. Secondly, a should-cost model was made to provide a detailed breakdown of production costs, offering insights into cost drivers, and enabling more informed negotiation strategies with suppliers. To assist in answering the research question, qualitative data was gathered through structured interviews with Category Managers at Northvolt. The thesis adopts an explanatory mixed method approach with focus on archival research and interviews to gather quantitative and qualitative data. Relevant actions have been taken to ensure reliable and valid results and analysis. The findings suggest that integrating these analytical tools significantly aids in making informed, strategic decisions in supplier management, setting a precedent for future procurement practices in the rapidly evolving EV battery sector. Regarding the contribution, the thesis was conducted in complete collaboration between the two authors. Both authors have been fully involved in every process and section integrated into the thesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".