Introducing Machine Learning in Meter Reading and Billing

This unit explains how Machine Learning is used in SAP S/4HANA Utilities. It is important for people in any market role and in any country.

SAP S/4HANA Utilities takes advantage of new ideas from the SAP Intelligent Enterprise when they are useful. Machine Learning helps solve problems with meter reading and billing to make processes more automated.

SAP uses intelligent technologies to automate and optimize business processes to improve the customer experience.

Intelligent technologies provide features like Machine Learning (ML), Internet-of-Things, and Realtime Analytics. These intelligent technologies are incorporated into applications through the SAP Business Technology Platform.

SAP S/4HANA is enriched by intelligent solutions such as SAP SuccessFactors, SAP Fieldglass, SAP Concur and SAP Ariba.

SAP S/4HANA Utilities is enriched by ML to use previous decisions from utilities metering expert agents and utilities billing expert agents to resolve implausible meter reading results and outsorted billing documents either automatically or by making a solution proposal.

Machine Learning scenarios can be categorized as Embedded or Side-by-Side.

Embedded Machine Learning algorithms do not heavily use memory or CPU power. In SAP S/4HANA Utilities, these algorithms help solve problems related to meter reading and billing. Embedded Machine Learning is built into SAP S/4HANA, using HANA ML from the infrastructure provider and application data for training models.

Side-by-Side Machine Learning algorithms need a large amount of data and significant CPU time for training models. To prevent heavy loads on the transactional SAP S/4HANA system, these scenarios are managed on SAP BTP. The trained model data is accessed through remote interfaces, especially for advanced use cases like image recognition, sentiment analysis, or deep learning for natural language processing, which rely on neural networks.

ISLM stands for Intelligent Scenario Lifecycle Management. It facilitates the integration and use of predictive functionality. Acting as a framework, ISLM handles the lifecycle management operations for Machine Learning scenarios. It can train an intelligent scenario and use the trained model to provide accurate inference results.

SAP Joule allows users to interact with SAP S/4HANA through a conversational interface that understands natural language. It uses Machine Learning to gain insights from historical data and past experiences. Additionally, SAP Fiori is incorporated to enhance the user interface with extra visual elements, such as confidence intervals or forecasting charts.

SAP S/4HANA Utilities needs a higher level of automation to handle exceptions during the meter reading and billing processes. To achieve this, it uses Embedded Machine Learning to process and resolve unusual reading results and billing documents that have been flagged as needing further review.

The benefits of the usage of ML are …

• a higher cash flow by exception resolution automation
• a higher operational efficiency by manual work support

In addition to the scenarios described in this unit, SAP S/4HANA Utilities provides further ML scenarios such as …

• intelligent collections by customer behaviour
• matching incoming payments to open items
• need-based product recommendation
• field service planning optimization
• asset maintenance optimization
• response generation for agents

A utilities company determines the meter reading results during the meter reading process, and these results are then used during the billing process. However, the meter reading results might sometimes be implausible, and the billing documents could be flagged for further review.

SAP S/4HANA Utilities uses Embedded ML, which covers …

• the usage of ISLM to enable predictive scenarios with training- and apply datasets to train and activate customer-specific predictive models
• the usage of CDS to access ML relevant data for implausible readings and outsorted billings (see next figure)
• the determination of the release confidence value for implausible readings and outsorted billings
• the release suggestion or the automatic release of implausible readings and outsorted billings

The following consumption views enable the usage of ML-relevant data for implausible meter reading results and outsorted billing documents. These are training datasets and apply datasets to determine their release confidence value.

The ML Training Dataset for Predicting Automatic clearing of improbable Meter Reading Results processes the data to form a forecast about the chances of automatically releasing unsupported meter reading results.

ML Training Dataset for Predicting the Automatic Release of Implausible Meter Reading Results

The ML Training Dataset for Predicting the Automatic Release of Implausible Meter Reading Results prepares the data for the creation of a prediction about the option of releasing implausible meter reading results automatically.

ML Training Dataset for Implausible Meter Reading Results

The ML Training Dataset for Implausible Meter Reading Results prepares the data for the ML training models in the implausible meter reading results environment.

ML Training Dataset for Outsorted Billing Documents

The ML Training Dataset for Outsorted Billing Documents prepares the data for the ML training models in the outsorted billing documents environment.

ML Apply Dataset for Outsorted Billing Documents

The ML Apply Dataset for Outsorted Billing Documents provides ML-relevant data for outsorted billing documents to access the apply dataset.

The characteristics to build the basis for training datasets and apply datasets could be …

• segments such as portion or billing class,
• amounts such as billing amount or previous billing amount,
• statistics such as billing amount average over 2 years.

The following video shows the ML processing for implausible meter reading results in SAP S/4HANA Utilities.

The following video shows the ML processing for outsorted billing documents in SAP S/4HANA Utilities.