Updates from Siemens

Siemens Case Study: Lean Digital Factory Project

By Gunter Beitinger – In October 2017, Siemens launched their Lean Digital Factory (LDF) program. Combining a group of experts from different business functions and technology units, its purpose is to define a conceptual holistic digital transformation roadmap for all factories of the operating company Digital Industries (DI).

To fully capture the value of using big data in manufacturing, the plants of DI needed to have a flexible data architecture which enabled different internal and external users to extract maximum value from the data ecosystem. Here, the Industrial Edge layer comes into the picture, which processes data close to the sensors and data source (figure).

The Industrial Edge and data lake concept will enable a more powerful solution than any other data storage and utilization concept:

  • The MDP will be a colossal storage area for all manufacturing data and will be tremendously powerful for all user levels
  • The MDP data platform is a centralized and indexed aggregation of distributed organized datasets
  • Big data will be stored in the MDP independently of its later use, this means as raw data
  • In combination with Industrial Edge, the MDP is the pre-requisite for effective and scalable cloud computing and machine learning
  • The Industrial Edge is used in this architecture for multiple purposes like data ingestion, pre-preparation, security-gate, real-time decisions.
  • Highly integrated, but module and service-based ecosystem functionalities.

In DI, it can be challenging to harness the potential of digitalization at full scale due to installed proprietary software solutions, customized processes, standardized interfaces and mixed technologies. However, at Siemens, this doesn’t mean that we ran a large standardization program before leveraging the possibilities of data analytics and predictive maintenance in our plants.

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To get rubber on the road at large scale, we required an architectural concept which allowed us to develop applications, scale up and transfer solutions from plant to plant, from engineering to shop floor as well as supplier to customer and reuse identified process insights from one application to another. more>

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