Ford Motor Company: Reshoring Amid Escalating Tariffs –– Balancing Higher Costs With Supply Chain Resilience and Localized Incentives: Business School Case Study
Bibliographic record
Abstract
Sarah Chen reviewed the tariff impact spreadsheet one more time, the numbers stubbornly unchanged from her previous three reviews that morning. As Vice President of North American Manufacturing Operations at Ford Motor Company, she had managed through supply chain disruptions, semiconductor shortages, and pandemic shutdowns. But the challenge before her on this May morning in 2025 was different—it required not just operational agility, but a fundamental strategic choice about Ford’s manufacturing footprint for years to come. The catalyst was unambiguous: new Section 232 tariffs implemented in April 2025 imposed a 25% duty on imported automobiles and certain automotive parts.¹ For Ford, which manufactured approximately 20% of its North American volume in Mexico—including high-demand models like the Bronco Sport and Maverick at the Hermosillo plant and the Mustang Mach-E at Cuautitlán—the tariffs represented a potential $2.0-2.5 billion annual cost impact.² While the Trump administration had granted certain exemptions for components that couldn’t be sourced domestically, complete vehicles remained subject to the full tariff. Chen had three weeks to present her recommendation to Ford’s Executive Leadership Team. The decision would impact thousands of jobs on both sides of the border, require potentially billions in capital investment, and shape Ford’s competitive position in the rapidly evolving automotive market. As she gazed out her Dearborn office window toward the historic River Rouge Complex—itself a symbol of Ford’s manufacturing legacy—she considered the irony: the company that pioneered modern manufacturing efficiency now faced a choice between economic optimization and political-economic realities. What should Chen recommend, and how should she weigh the competing factors of cost, resilience, political positioning, and long-term strategic flexibility?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".