Improving Transportation Procurement in the Humanitarian Sector: A Data‐driven Approach for Abnormally Low Bid Detection
Bibliographic record
Abstract
Aid organizations choose their service providers through reverse auctions to decrease their operational costs, and many of them award the contracts to the lowest bidders, which often leads to aggressive bidding practices and compromised service quality. This is known as the abnormally low bids (ALBs) problem in public procurement. An ALB is defined as an unrealistically low bid submitted to win an auction, an amount at which the auctioned service cannot be provided reliably. The current literature on ALBs in the humanitarian sector is rather sparse. This study presents a data‐driven contract awarding framework that aims at eliminating ALBs so that service levels can be improved significantly. We conducted our analyses in the context of a developing country, where the transportation market data are almost non‐existent. We derived our research questions through an exploratory research performed in African headquarters of an International Humanitarian Organization located in Kenya, and we constructed our quantitative models based on interviews with humanitarian practitioners and representatives of the carriers. We collected historical transport rate data from numerous carriers that serve multiple shippers, and we developed a methodology that can objectively identify ALBs, based on lane and contract specifications that are derived from the market. Furthermore, we estimated the effect of ALBs on service levels and we compared different contract settings under simulated market conditions. The results of the simulation analyses demonstrate that mitigating ALBs would improve the service levels significantly more than the commonly used fuel surcharge clauses in contracts.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".