Managing Procurement Spend Using Advanced Compliance Analytics

Chowdhary, Pawan; Ettl, Markus; Dhurandhar, Amit; Ghosh, Soumyadip; Maniachari, Gopikrishna; Graves, Bruce; Schaefer, Bill; Tang, Yu Hoe

doi:10.1109/icebe.2011.57

Cited by 3 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A naïve aggregation strategy could be implemented via clustering of PO items (and then by vendors) by suitably defining a text similarity measure (Chew 2017;Chowdhary et al 2011;Wang and Miller 2005). However, such a one-dimensional clustering approach has two issues: 1.…”

Section: Application Descriptionmentioning

confidence: 99%

“…To the best of our knowledge, only a few papers have been published in the specific field of demand aggregation for procurement (Chew 2017;Chowdhary et al 2011;Wang and Miller 2005). These papers use a naïve one-dimension aggregation strategy, which suffers from the "curse of dimensionality" (Bellman 1961;Beyer et al 1999;Kriegel, Kröger, and Zimek 2009), and from sensitivity to text descriptions variability and the effectiveness of the similarity measure used.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Using Unsupervised Learning for Data-driven Procurement Demand Aggregation

Shaham

Westerski

Kanagasabai

et al. 2021

AAAI

View full text Add to dashboard Cite

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to 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 administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds 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 was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.

show abstract

Section: Application Descriptionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Unsupervised Learning for Data-driven Procurement Demand Aggregation

Shaham

Westerski

Kanagasabai

et al. 2021

AAAI

View full text Add to dashboard Cite

show abstract

“…This notion is motivated by applications across multiple industries and domains. For example, many big businesses want to cluster their spend data [4] to find out areas of high/low spend and high/low non-compliance to be able to take the appropriate action. The appropriate action may be remedial in nature or they may want to reward certain entities.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Submodular Clustering with Actionable Constraint

Dhurandhar¹,

Gurumoorthy²

2014

Preprint

Self Cite

View full text Add to dashboard Cite

Clustering with submodular functions has been of interest over the last few years. Symmetric submodular functions are of particular interest as minimizing them is significantly more efficient and they include many commonly used functions in practice viz. graph cuts, mutual information. In this paper, we propose a novel constraint to make clustering actionable which is motivated by applications across multiple domains, and pose the problem of performing symmetric submodular clustering subject to this constraint. We see that obtaining a k partition with approximation guarantees is a non-trivial task requiring further work. * This research work benefited from the support of the AIRBUS Group Corporate Foundation Chair in Mathematics of Complex Systems established in ICTS-TIFR.1 This is relative to the objective we are minimizing/maximizing.

show abstract