In December of 2020 we had a Hackathon with SAP partners that was focused on using SAP Intelligent RPA with SAP Ariba. You can view the summary of that Hackathon in this blogpost from the Co-Innovation Lab. This Hackathon challenged partners to design and develop a use case for automation with Ariba. I have been writing a series of spotlights for the use cases presented in that Hackathon, you can find links to the other blogposts at the bottom of this post.
In our fourth post we will spotlight the use case from Deloitte. The team developed a bot that will help reduce the spend on non-catalog items from Ariba. There can be inefficiencies when placing orders using free text and when ordering items that are not in the catalog. By augmenting a buying team with a bot the team can increase speed, efficiency, and quality of their orders.
To accomplish this, the bot identifies purchase requests for non-catalog items and then searches for options that are within the catalog. Once it has identified an alternative option it will send those results to a buyer for manual approval. You can see the process flow in the graphic below.
In this use case the team was able to leverage Intelligent RPA to have a bot aggregate all the required information for a decision and add it as a comment to a purchase request. This allows the buyer to work in a significantly more efficient way where they have all the necessary information to make a decision in one place.
Contrary to most automation projects, this use case does not remove all manual work. There is still manual input required at the final approval step to ensure that the bot has performed correctly. However, with a bot doing everything besides the decision it does provide an opportunity to collect training data to potentially automate the entire process with machine learning and Intelligent RPA. For machine learning you need to have a sample set of data that can be used as a test and training set. This process could be configured to begin collecting outcome of the buyer’s manual intervention in order to build a catalog of sample data to potentially automate the end to end process.
To collect the data to build a sample set, there is a helpful activity from SAP Intelligent RPA, Business Activity Monitoring (BAM). BAM works as a sensor in your process and allows you to pass information back to the cloud factory for extraction. By including just one line of code to use the BAM activity, this sample data could be collected and later used for analysis or potentially machine learning. You can learn more about BAM from its page on the user guide.
By implementing this bot, Deloitte predicts an increase in catalog spend which provides a higher return on investment for the negotiated contracts. And, if you configure the bot to log all the items that were not available in the catalog, then buyers could work to make a deal and have those items available in the catalog.
Have a great day and happy bot building!
This is the fourth use case spotlight in the series, you can check out the first three posts in the links below:
To learn more about how BAM can be used with SAP Intelligent RPA, check out my previous blogpost with a dashboard populated with BAM:
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