Digital Mission

Finance

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Intelligent Order-to-cash Optimization

Current Situation

Organizations often find themselves wading through pools of disconnected order and customer information. This severely limits their ability to optimize billing and collection activities.

Goals and Objectives

Recently, companies have turned to structured machine learning to speed up/streamline order-to-cash processes, including advanced order management, AR automation, invoice remittance, reconciliation, recognition, and credit details. In addition, early adopters of machine learning have been able to eliminate a large amount of time spent on manual tasking while also decreasing the error rate.

Technology Deployed

Enterprise hardware, personal devices, cloud, Big Data and analytics, mobile, applications, business consulting, cognitive technologies, and IoT

Use Case Summary

Technology is used to develop, implement, and measure the order-to-cash processes.

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