Today's business environment, survival and making profit in market are the prime requirement for any enterprise due to competitive environment. Innovation and staying updated are commonly identified two key parameters for achieving success and profit in business. Considerably supply chain management is also accountable for profit. As a measure to maximize the profit, supply chain process is to be streamlined and optimized. Appropriate grouping of various suppliers for the benefit of shipment cost reduction is proposed. Data relating to appropriate attributes of supplier logistics are collected. A methodology is proposed to optimize the supplier logistics using clustering algorithm. In the proposed methodology data preprocessing, clustering and validation process have been carried out. The Z-score normalization is used to normalize the data, which converts the data to uniform scales for improving the clustering performance. By employing Hierarchical and K-means clustering algorithms the supplier logistics are grouped and performance of each method is evaluated and presented. The supplier logistics data from different country is experimented. Outcome of this work can help the buyers to select the cost effective supplier for their business requirements.
The quality integrated data is crucial for data mining process. The existing approaches are used trust your friends and cry with wolves principle to resolve the data conflicts. These principles are taking the value of a preferred source and taking the most frequent value. However, it is a challenge for data integration to choose the most trustworthy data source and it is arbitrary to trust only certain source. To mitigate above issues, Data Fusion in Data Federation using Modified Discriminative Markov Logic Networks (DF-MDMLN) approach is proposed. Data fusion is to resolve the data conflicts among the data from different heterogeneous databases by utilizing multiangle features and knowledge of discriminative Markov Logic Network (MLN). The data fusion is used to improve the precision and recall of the end users' data set. E-shopping for computer peripherals application is considered for experimentation to analyze the performance of DF-MDMLN approach. Experiments on E-shopping data sets show the effectiveness of DF-MDMLN approach. It is observed that the precision and recall of data fusion has been improved by 40% and 27% respectively.
Accounts Payable (AP) is a resource-intensive business process in large enterprises for paying vendors within contractual payment deadlines for goods and services procured from them. There are multiple verifications before payment to the supplier/vendor. After the validations, the invoice flows through several steps such as vendor identification, line-item matching for Purchase order (PO) based invoices, Accounting Code identification for Non- Purchase order (Non-PO) based invoices, tax code identification, etc. Currently, each of these steps is mostly manual and cumbersome making it labor-intensive, error-prone, and requiring constant training of agents. Automatically processing these invoices for payment without any manual intervention is quite difficult. To tackle this challenge, we have developed an automated end-to-end invoice processing system using AI-based modules for multiple steps of the invoice processing pipeline. It can be configured to an individual client’s requirements with minimal effort. Currently, the system is deployed in production for two clients. It has successfully processed around ~80k invoices out of which 76% invoices were processed with low or no manual intervention.
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