SAP Asset Analytics
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Predictive Asset Analytics is the use of statistical modeling, data mining techniques, machine learning, and digital twin technologies to make predictions about future outcomes. A cloud deployment or cloud native solution is required to gain insights from sensor data and engineering simulations. With advances in data storage and loT sensor technology organizations can learn about each asset’s individual performance to predict asset productivity.
Improving reliability, performance, and safety are top priorities for organizations today. But organizations are also focusing on controlling costs and maximizing value from existing investments by supporting predictive maintenance and service. Predictive asset analytics is a solution for end-to-end asset management to provide early warning signals and give a diagnosis of equipment long before failure.
Key Capabilities of SAP Predictive Asset Insights include:
- A 360 view of assets: A holistic view of the asset model with a single predictive tool increasing visibility into master, transactional, performance, and loT data sensor data.
- Intuitive and scalable machine learning: Ability to detect abnormalities and predict failure without data scientist intervention, extending the intelligence of predictive maintenance by machine learning capabilities.
- Advanced analytics: Gain insights into failure modes and leading indicators by benefiting from purpose-made analytical capabilities augmented with predictive asset intelligence.
- Digital twin simulations: Apply ANSYS digital twin technology to analyze assets in action and over time in a live environment where engineering simulations support virtual sensors.
Predictive Asset Analytics is the use of statistical modeling, data mining techniques, machine learning, and digital twin technologies to make predictions about future outcomes. A cloud deployment or cloud native solution is required to gain insights from sensor data and engineering simulations. With advances in data storage and loT sensor technology organizations can learn about each asset’s individual performance to predict asset productivity.
Improving reliability, performance, and safety are top priorities for organizations today. But organizations are also focusing on controlling costs and maximizing value from existing investments by supporting predictive maintenance and service. Predictive asset analytics is a solution for end-to-end asset management to provide early warning signals and give a diagnosis of equipment long before failure.
Key Capabilities of SAP Predictive Asset Insights include:
- A 360 view of assets: A holistic view of the asset model with a single predictive tool increasing visibility into master, transactional, performance, and loT data sensor data.
- Intuitive and scalable machine learning: Ability to detect abnormalities and predict failure without data scientist intervention, extending the intelligence of predictive maintenance by machine learning capabilities.
- Advanced analytics: Gain insights into failure modes and leading indicators by benefiting from purpose-made analytical capabilities augmented with predictive asset intelligence.
- Digital twin simulations: Apply ANSYS digital twin technology to analyze assets in action and over time in a live environment where engineering simulations support virtual sensors.
Benefits of SAP Predictive Asset Insights are:
- Improves service effectiveness
- Lowers maintenance costs
- Increases asset availability
Vendors partners for predictive asset analytics are: SAP , PWC, KCT
Key Considerations for SAPinsiders are:
- Advanced Analytics and Performance Intelligence. In the recent state of the market for process automation, SAPinsiders indicated that intelligent automation and process intelligence are key capabilities they are planning to build in the next two years. This analyst insight explores some critical aspects to be aware of on your path to develop these capabilities.
- Achieve Business Clarity with Process Intelligence. Process intelligence provides an understanding of where process deficiencies exist and where leverage opportunities reside. SAPinsider sat down with Shoeb Javed, Chief Strategy and Product Officer for software testing firm Worksoft Inc., to discuss the latest Achieve Business Clarity with Process Intelligence process intelligence trends.
- Digital twins are on the Rise to Mitigate Risk. Companies are searching for greater efficiencies to improve operational efficiencies and be more resilient. In this analyst insight learn what digital twins are, how they work, how they have evolved, and benefits that they can bring to your organization. Optimization is where digital twins excel to test contingency plans and build resilience.
853 results
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Transforming to Intelligent Asset Management with SAP Solutions
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Dodd-Frank Act: Standardize Risk Management Processes with SAP BusinessObjects Risk Management 10.0
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Web-Based Asset Accounting with mySAP Financials
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Spotlight: Fixed Asset Revaluation with SAP’s RAAUFW02 Program
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SAP S/4HANA Embedded Analytics
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Panelists: Dmitry Kuznetsov, BI Consulting; Andrei Zhakhavets, Independent Consultant Sponsor: BI & HANA 2018 Read this Q&A transcript to learn about the embedded BI content delivered in SAP S/4HANA, such as analytic models and SAP Fiori tiles. If your organization is considering, evaluating, or planning the move to SAP S/4HANA, get answers to key topics, such...…
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Use SAP ECC 6 to Fulfill Accounting Requirements for Assets Impairment
Reading time: 12 mins
FinancialsLearn some configuration tips to help you overcome challenges in recoding the impairment loss on assets in an SAP system. Key Concept Asset impairment is a key requirement of various legal regulations to ensure that an organization’s financial statements show the realistic value of its assets. Proper accounting of asset impairment in the books is...…
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How to Simplify Access to Analytics for Everyone
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- SAP Asset Management
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SAP Asset Performance Management for Circular Manufacturing
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The manufacturing industry is a major contributor to carbon emissions, accounting for around 20 percent of global emissions. These emissions are primarily caused by energy usage during the manufacturing process. However, new approaches to designing, producing, and disposing of goods can reduce waste by eliminating it from products and processes, as well as by recycling…
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