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SAP Predictive Maintenance and Service, on-premise edition, delivers on the potential of the Internet of Things (IoT) and Big Data with out-of-the-box business applications to improve the health of assets and optimize their operation and servicing.

It runs on an extendable, high-performing data processing platform, which can process huge amounts of fused Information Technology (IT) and Operational Technology (OT) data using sophisticated machine learning algorithms. The standard product offers a flexible extensibility concept that allows you to adopt the capabilities you need.


SAP Predictive Maintenance and Service, on-premise edition, brings the power of Big Data and IoT into existing service processes to enable manufacturers of equipment to provide differentiating and higher margin service offerings to their customers and at the same time improve product quality by better understanding root causes of failures.


The IoT Applications of Asset Health Control Center and Asset Health Factsheet comprise the main components of SAP Predictive Maintenance and Service, on-premise edition.

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The Asset Health Control Center allows users to analyze a ‘fleet’ of assets to monitor health and prevent failures and initiate preventive counter measures that trigger service or maintenance notifications, orders or requests to enable a closed-loop maintenance and service process.

The Asset Health Factsheet provides a 360° view on a single asset. Together they provide the holistic management of asset health and decision support for maintenance schedules and resources optimization based on health scores, anomaly detection, spectral analysis and machine learning.

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Operationalized Analytics and Data Science Services


Insight Providers are micro-services that provide analytical and predictive functionalities that are consumed by applications and are shipped as part of the SAP Predictive Maintenance and Service, on-premise edition. Insight Providers consume data from the fusion services and implement the business logic to expose the insights. Customers can productize their domain knowledge as custom Insight Providers to enhance their competitive edge.

Applications are the user interaction shell or ‘mash-ups’ of Insight Providers, which each have one particular functionality, their own UI, and are technically built as stand-alone “micro services”. Customers or partners can develop custom applications and custom Insight Providers on top of the open and extensible SAP Predictive Maintenance and Service, on-premise edition platform.


SAP Predictive Maintenance and Service, on-premise edition, is a scalable, extensible and integrated solution built on a high-performing big data platform that provides:

  • IoT connectivity to enable device management, monitoring and data transfer utilizing various protocols
  • Scalable and cost-effective storage for time series data, cleansing and management utilizing ‘schema-on-read’ which results in minimum effort and maximum flexibility when uploading data
  • Distributed and fault-tolerant processing of batch and streaming data in analyses
  • IoT base services that includes sophisticated predictive modeling and analysis tools for the whole lifecycle of dealing with statistical models, discovering the data, “learning” such models, relearning them and scoring assets
  • Process integration with existing business systems to drive insight to action


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SAP Predictive Maintenance and Service, on-premise edition, goes far beyond traditional products and offers a rich set of analytical features, machine learning capabilities and functions for display to apply predictive insights in real time to optimize asset maintenance and servicing. The integration into SAP’s business suite makes it unique and offers customers a complete process from Thing to Insight to Action.

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2 Comments

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  1. Remo Tschupp

    Hi Jeremy, are you aware of any detailed documentation as there is for the cloud edition (e.g. system administrator guide) in order to get an overview on how such a big data platform would look like with PdMS on-premise?

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  2. Al Kafi Khan

    Dear @jeremytodd

    I am a Masters Student and doing my masters thesis on Predictive maintenance using HANA IOT platform. I’m facing difficulty with the dataset. ┬áMay I have the sample dataset of predictive maintenance?

    Thanks Advance

    Kafi Khan

    (0) 

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