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Avoid Unplanned Downtime with Predictive Analytics - SAP Industrial IoT Case Study
Avoid Unplanned Downtime with Predictive Analytics
SAP
Objectives • Create an innovative IT environment that supports the move toward a solution-provider business model • Enhance existing business processes and leverage the power of Big Data and predictive maintenance to become more proactive, customer oriented, and competitive • Leverage the SAP HANA® platform to transform and simplify the entire SAP® solution landscape
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The Data Tsunami for AGL Energy - SAP Industrial IoT Case Study
The Data Tsunami for AGL Energy
SAP
AGL Energy, one of Australia’s largest power retailers, is replacing its electromechanical meters with interactive smart meters in 2.2 million homes and 300,000 businesses throughout the state of Victoria, including its capital, Melbourne. This is nearly a 4,320-fold increase in daily data processing for electricity providers. It’s also the start of a “data tsunami,” swelling up from these 2.5 million smart meters and flowing into AGL Energy’s billing and operations systems.AGL’s Business Requirements- For wholesale energy: Improved settlement reporting- For merchant energy portfolio management: Better load forecasting, accuracy and data analytics / segmentation with better reporting- For analytics: Better forecasting and reporting
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Driving Two Million Vehicles with Real-Time Data - SAP Industrial IoT Case Study
Driving Two Million Vehicles with Real-Time Data
SAP
For ARI, customer service is key. Seven global customer contact centers handle about 4 million calls annually, and this sea of daily information needs to be analyzed, which took up to two days manually, so negatively affecting service levels. With the amount of data ARI collects doubling every 14 months, their in-house ETL data warehouse limited their ability to fully help customers reduce fleet costs and gain operational efficiencies.
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Embracing Business Success in Real Time  - SAP Industrial IoT Case Study
Embracing Business Success in Real Time
SAP
· Increase control over growing Big Data to improve business decisions · Manage data for 28,000 biotechnology stockkeeping units in the fields of microbiology, molecular biology, animal cell cultures, plant tissue cultures, and lab ware for laboratory chemicals · Accelerate report generation and analysis with real-time data
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