Accelerating the Industrial Internet of Things
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Siemens Case Studies Driven by Data

Driven by Data

Siemens
Driven by Data
Driven by Data
Driven by Data
Driven by Data
Driven by Data
Automotive
Product Development
Production - Manufacturing
Digitalization starts with the design of a product. Maserati needs to virtually create, simulate, and test their cars in a time efficient manner. The Maserati engineers needed to manage their project collaboratively across various locations.
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Maserati is an Italian luxury vehicle manufacturer established on December 1, 1914, in Bologna, with around 1,100 employees worldwide.
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Siemens provided a solution with CAD software NX and PLM software Teamcenter.
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Testing data, simulations
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IOT
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Cutting Edge (technology has been on the market for < 2 years)
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Siemens' integrated solutions for manufacturing helped Maserati obtain a benchmark in the automotive industry.
Time-to-Market - Programming time, production time, and installation time are shortened during the introduction of new products.
With NX and Teamcenter, Maserati reduced 30% development time.
Time-to-market time reduced to 16 months.
Hardware
Software
Horizontal applications are standardized (e.g., asset tracking). Vertical applications are tailored to specific needs (e.g., delivery fleet management).
APIs are the market enabler for IoT. They allow users to manage devices, enable data transfer between software, and provide access capabilities.
IoT analytics includes real-time or edge computing and batch analysis. Analytics can be behavioral, descriptive, predictive, or prescriptive.
Visualization solutions use dashboards, alerts, events, maps, and other tools to present easily comprehensible data to end users.
Data management solutions capture, index and store data in traditional database, cloud platforms, and fog systems for future use.
Services
System integrators link IoT component subsystems, customize solutions, and ensure that IoT systems communicate with existing operational systems.
IoT data management consultancies help to make sense of big data, decide which data to maintain and for how long, and troubleshoot IT issues.
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