ORAC

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The aim of the ORAC project is to research the use of machine learning, specifically neuralnetworks, techniques to significantly improve the accuracy of data classification within aspend analysis system. Spend analysis enables effective cost reduction and suppliermanagement by providing detailed all-round insight into procurement spend. Currently thestate-of-the art for automated data classification in such systems is 50%-70% accuracy (notlimited to machine learning approaches but typically using a combination of techniques) withconsiderable human intervention required to achieve 100% (this assumes the classification iscapable of addressing a wide range of market sectors). Initial investigation has indicated thatthe use of neural networks to pre-process the data could increase the automated accuracy to95%.The objectives of this project are to: 1) identify the commercial neural network technologiesthat can be adopted (Google’s technology being the preferred solution at present); 2) tailor theselected Neural Network (NN) to support the specific text structures used in spend analysis; 3)create a proof-of-concept demonstrator that will be used to characterize the capabilities of thenew categorization system (the new NN combined with the current Spend360 solution); 4)evaluate the capabilities and constraints of the NN-based solution and identify the issues thatneed to be addressed as part of a prototype evaluation activity.The primary benefit that will accrue from this new innovative approach is that it willsignificantly reduce the human involvement in the classification process and associated dataquality assurance. This will significantly reduce the time to complete the classification andimprove the accuracy. Together, this will result in cost reduction of the solution therebyenabling adoption by a range of organisations that find the current service cost too high.