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Prediction of Enterprise Purchases using Markov models in Procurement Analytics Applications

机译：使用马尔可夫模型在采购分析应用中的企业购买预测

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摘要

Procurement is a set of activities and processes related to acquisition of goods and services through purchase orders placed by organization employees, from external contractors. This article describes practical experiments with procurement dataset of a major governmental organization in Singapore. In particular, we highlight the problems that emerge when trying to implement analytics for prediction of future purchases. The goal of such analytics is to deliver beneficial information to procurement office that plans and manages relationships with external sellers. In the article we describe the characteristics of the procurement dataset specifics and its implications on the future purchase problem that we attempt to solve using Markov chains model. Our analysis shows high diversity of purchase descriptions resulting in low ability to detect sequential patterns of purchasing officers. The solution presented in the article is additional dataset preprocessing involving use of hierarchical clustering. Our experiments with various similarity measures show an improvement allowing a practical deployment within our procurement analytics system prepared for the case study governmental organization.
机译：采购是通过组织员工，来自外部承包商的购买订单收购商品和服务的一系列活动和流程。本文介绍了新加坡主要政府组织采购数据集的实践实验。特别是，我们突出了在试图实施分析以进行预测以预测未来购买时出现的问题。此类分析的目标是为采购办公室提供有益信息，该办公室计划和管理与外部卖家的关系。在文章中，我们描述了采购数据集细节的特征及其对未来购买问题的影响，我们试图使用马尔可夫链模型解决。我们的分析显示了高度多样化的采购描述，导致检测采购官员的连续模式的能力低。本文中呈现的解决方案是涉及使用分层聚类的其他数据集预处理。我们具有各种相似措施的实验表明，允许在为案例研究政府组织准备的采购分析系统中进行实际部署的改进。

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