ORAC

奥拉克

• 批准号: 720776
• 金额: $ 29.54万
• 依托单位: PLKD0907 LIMITED
• 依托单位国家: 英国
• 项目类别: GRD Development of Prototype
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=720776
• 关键词: ORAC

The aim of the ORAC development project is to advance the results drawn from the InnovateUK Proof of Concept research project. That research into the use of machine learning(specifically neural networks) techniques has shown that Spend360 can significantly improvethe accuracy of data classification within a spend analysis system. This enables effective costreduction and supplier management by providing detailed all-round insight into procurementspend by an organisation.Currently these systems are heavily dependent on human interaction and require considerabledomain-specific knowledge therefore accurate automation is essential. The current state-of-theart for automated data classification in such systems is only 50%-70% accurate and sorequires considerable human intervention to complete the task (this also assumes the autoclassification is capable of addressing a wide range of market sectors). Research by Spend360has shown that when these new machine learning approaches are combined with othertechniques, an accuracy of >95% could be achieved. The innovation will be based upon avariably-sized, multilayered neural network approach: specifically using a general purposeLong Short Term Memory (LSTM) network to map invoice descriptions (sequences of words)to UNSPSC codes.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 associated service costs toohigh. Furthermore, the automation will enable an organisation to undertake more detailed andvaried forms of spend analysis so trend analysis can be used to identify ways in which thespend profiles can be further optimised.

ORAC开发项目的目的是推进InnovateUK概念验证研究项目的成果。对机器学习（特别是神经网络）技术的研究表明，Spend 360可以显着提高支出分析系统中数据分类的准确性。这使得有效的成本降低和供应商管理通过提供详细的全方位洞察采购支出由一个组织目前，这些系统严重依赖于人类的互动，并需要可扩展的特定领域的知识，因此准确的自动化是必不可少的.目前，此类系统中自动数据分类的最新技术只有50%-70%的准确率，因此需要相当多的人为干预才能完成任务（这也假设自动分类能够解决广泛的市场领域）。Spend 360的研究表明，当这些新的机器学习方法与其他技术相结合时，可以实现>95%的准确率。创新将基于可变大小的多层神经网络方法：特别是使用通用长短期记忆（LSTM）网络将发票描述（单词序列）映射到UNSPSC代码。这种新的创新方法的主要好处是，它将大大减少分类过程和相关数据质量保证中的人工参与。这将大大减少完成分类的时间，提高分类的准确性.总之，这将导致解决方案的成本降低，从而使一系列发现当前相关服务成本过高的组织能够采用。此外，自动化将使组织能够进行更详细和更多样化的支出分析，以便趋势分析可以用于确定进一步优化支出配置文件的方法。