Machine Learning in Digital Process Automation — Part I by Ralf Mueller

This is the first of a multi-part series of articles on how to use Machine Learning (ML) in Digital Process Automation and Integration applications in Oracle Integration Cloud. For this series, we’re using a mix of Oracle Cloud Services and implement a couple of use cases step by step. The first article (this one) focuses on setting up the Machine Learning environment and implementing the Machine Learning Model for our first use case.

Example Use Case

Approval Workflows are very popular examples of Process Automation applications since approvals are widely used in any organization, for example

• Approve hiring of employee in Employee Onboarding process
• Approve purchase of equipment in Procurement process
• Approve travel expenses in Travel Management process
• etc. etc.

The list can go on and on. For this series though, we take a use case from Sales and want to implement the following scenario

• Orders from customers are coming in from Sales Reps of a company
• Orders have, among other data, the following relevant information
  – Amount of the order
  – Quarter when the order was created (1, 2, 3, 4)
  – Customer Status (green, yellow, red)
  – Requested Discount by the customer (0–99%)
• Depending on the order information, we want to predict whether the order should be approved by a Sales VP for the given quarter.

This is a typical situation for any company where Sales Reps bring in orders from customers and a Sales VP has to decide, if the order should be approved for this quarter or not. Especially towards a quarter-end, this can become quite stressful for the Sales VP, so some automation here would improve the Sales Process significantly and could help to handle more orders. Read the complete article here.

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