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Performance management is the next step after an organization's level of performance is measured. By observing the results of performance measurement, an organization is able to see where it stands regarding its operations and performance. In this paper, a Decision Support Network is introduced for effective procurement performance management and improvement. The procurement process involves many contributing Key Performance Indicators (KPIs) which determine the procurement performance. The focus of this paper is mostly on performance management. Any organization seeks further improvements in its operations. Hence, they proceed with making decisions which they hope might increase their credibility. Yet still the implications of such decisions are to be studied so that the organization can make such decisions with more confidence, by knowing the expected impact in advance. Once the model is built, it could be used for analyzing and foreseeing the result of any kind of decision in the organization. The fundamentals of Bayesian Networks are used to build the Decision Network (DN). This DN is built for procurement performance management in particular and an application of it is demonstrated through the ending of the paper. [ABSTRACT FROM AUTHOR]

A conceptual framework is proposed in this study to improve Entity Resolution in contact centers. It is stated in the paper that how RFID produce dirty data in CC's databases and how using customer service representatives (CSRs) via argumentation framework deal with issue. Leveraging the power of CSRs put this work as a crowdsourcing technique that combine human and machine together to rich to the high quality of data in CC's databases. [ABSTRACT FROM AUTHOR]

Contact centers, as the organization's touch point, have a considerable effect on customer experience and retention. It has been shown that 70% of all business interactions are handled in contact centers. A framework is proposed in this conceptual paper to build cleaned interactive customer recognition framework (CICRF) in CCs. CICRF consists of two integrated modules: cleansing and ICRF. The first module focuses on the detection and resolution of duplicate records to improve the effectiveness and efficiency of customer recognition. The second module focuses on interactive customer recognition in a customer database when there are multiple records with the same name. Cleansing module uses Semi-Automatic deduplication process by incorporating three main functions in its design, namely: DedupCrowd, DedupNN and DedupCSR. DedupCrowd is a function that provides training pairs of records for DeduppNN which is a deduplication based neural network. Researchers suggest leveraging human computing power in managing duplicate data which is scalable top the large size of contact centers data. However completion of crowdsourcing tasks is an error-prone process that affects the overall performance of the crowd. Thus, controlling the quality of workers is an essential step for crowdsourcing systems and for that I propose OSQC, an online statistical quality control framework, to monitor the performance of workers. DeduppNN is a neural network based deduplication method that uses output of DedupCrowd for the training purposes. DeduppNN has two features: first is that it is an online deduplication method which is essential for the purposes of customer recognition. Second is that in terms of costs it is much lower in comparison with DedupCrowd. The last function is designed for providing label to pairs when DedupNN is not sure about their label. The intuition behind this function is similar with active learning area which selects appropriate data for labeling. ICRF consists of three integrated sub-modules. The first sub-module (DedupNNSelect) focuses on the detection and resolution of duplicate records to improve the effectiveness and efficiency of customer recognition. The second sub-module determines the level of ambiguity in the recognition of an individual customer in a customer database when there are multiple records with the same name. The third submodule, depending on the level of determined ambiguity from the second module, recommends to the CSR the series of featurerelated questions that need to be asked of the customer for his/her recognition. [ABSTRACT FROM AUTHOR]