AI-Powered Segmentation and Prioritization of e-Mobility Presales Requests
Bibliographic record
Abstract
**Abstract.** The Tenders & Proposals team at ABB E-Mobility plays a key role in growing the business by answering presales requests. One of the main challenges they face is the absence of a structured way to prioritize those requests. Decisions are mostly based on individual experience, which can result in time spent on low-impact opportunities while higher-value ones are missed. To support the team, we created a decision-support tool powered by machine learning to prioritize quotes based on data rather than intuition. The baseline model is a Random Forest (RF), trained on past quotes and variables such as complexity level, price tier, urgency (Days Until Due = Due Date − Request Date), and total value. Features were cleaned, recategorized, and grouped when needed—for example, total value was grouped by business rule (0–$5M vs $5M–$25M). On top of that, a priority score was built from business rules. To improve detection of “Won” opportunities, we evaluated a Gradient Boosting (GB) variant and cross-validation. In offline tests, the RF baseline achieved 90.0% accuracy (weighted F1 0.89); a GB variant increased “Won” precision to 50.0% (recall 13.3%). *Preprint — not peer-reviewed. Version 1.0 (2025-09-17).*
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".