Procurement is an essential operation of every organization, regardless of its size or domain. As such, aggregating the demands could lead to a better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and admin overhead.
This paper describes our experience on developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore, with procurement expenditure in the scale of 100’s of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns.
We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end-users during 2017, and later trialed and iteratively refined, before being rolled out in 2019.
The final performance achieved on past cases benchmark was: 100% precision (all aggregation opportunities identified by the engine were correct) and 71% recall (the engine correctly identified 71% of the past aggregation exercises that were transformed into bulk tenders). The performance for new opportunities pointed out by the engine was 81% (i.e., 81% of the newly identified cases were deemed useful cases for potential bulk tender contracts in the future). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7M annually.
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This research is supported by the Accelerate Grant. Grant number is not applicable.
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