Improving Transportation Procurement in the Humanitarian Sector: A Data‐driven Approach for Abnormally Low Bid Detection

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.