Optimizing the Supply Chain with a Digital Twin

Client Siemens Healthineers AG Data Science

Jan 2021,

“Not only do we get the improvement on the supply chain management, but it is also a very good opportunity for Siemens Healthineers to get to know and implement the agile working framework by working with SCRUM.”

Xinya Xu

Siemens Healthineers

Supply-Chain-Optimization-with-Data-Lake

The
Challenge

Siemens struggled with existing business intelligence tools because these tools were not good enough to manage the complex, sensitive and embedded supply chain data.

The
Solution

We built a digital twin of Siemens’ factory based on data lake, GraphQL and Kubernetes which integrates delivery, stock and material requirements data.

The
Impact

Optimizing Siemens’ complex healthcare supply chain through a proactive, user-friendly tool built in an agile and user-centric project.

How to handle complex, sensitive supply chain data from different sources in a corporate environment

Siemens struggled with existing business intelligence tools because these tools fail to display complex relations between data, especially when large amounts of data from different sources are involved. The challenge was to build a tool with a better, intuitive and more precise overview about the production and the process as well as to forecast possible supply bottlenecks.

Having one tool is not good enough, connecting more tools is too complicated

Adding to the challenge, the solution had to work within in a complex supply chain that involves sensitive data from multiple sources embedded in a large corporate infrastructure. Despite the strongly regulated healthcare sector, Siemens needed an agile and user-centric project management in order to quickly reach their goals.

Simplifying complex data for a better UX

In several user-centric Design Thinking iterations we developed a lean user interface which significantly enhanced the UX for the tool‘s users. For instance, production state and bottlenecks are visualized by graphs and a traffic light-based color-coding system, which is determined by comparing expected stock and demand. Further, users can filter and search easily, without being exposed to the complicated data structure.

Developing a data strategy and infrastructure

First, we helped to set up a data lake that gathered data from different sources. Then we were able to abstract away relevant data through a GraphQL API. Kubernetes completed the data infrastructure that allowed a dynamic and safe application of a new business intelligence tool that merged delivery data, material stock data and material requirements data in a real time digital twin.

Tackling the challenge in a four step process

  1. The first phase was the ideation con conceptional matter like the most important data, crucial functions, desired UI and other technical requirements.
  2. Next, we developed the proof of concept. It was a first version that allowed data processing as well as data integration from different sources.
  3. In the third phase, we developed an MVP that integrates the whole infrastructure, including data, design and additional functions like language options.
  4. Lastly, before deploying the digital twin, we went through an extensive two-weeks testing phase within the corporate environment.

A Digital Twin to optimize Siemens’ Supply Chain

Most importantly, through its agile and user-centric processes the digital twin project allowed Siemens Healthineers to quickly optimize their supply chain despite the heavily regulated healthcare sector.

Additionally, Siemens Healthineers is now able to avoid production stops, mistakes and bottlenecks. The proactive, user-friendly tool significantly enhances work processes for the users and enables employees to handle sensitive data in a complex firm infrastructure.

This is not just important for Siemens and its employees, but also for the whole ecosystem of their supply chain as it helps everyone involved to optimize co-dependent processes such as just-in-time delivery or make-to-order-production.

“Virtual Factory ensures a better transparency on the material situation and the material resupply process. It helps us to improve the flexibility to take reactions for bottlenecks, identify the weak spots at working processes and gain a more robust supply chain management.”

Xinya Xu
Siemens Healthineers

“Not only did we have the opportunity to develop a production-grade solution for an industry leader like Siemens but also marry the Siemens business knowledge with the latest implementation and hosting technology, resulting in a stable, scalable and relevant product.”

André Lubbe
Project Owner, Motius

Want to build a product of the future?

The
Challenge

Siemens struggled with existing business intelligence tools because these tools were not good enough to manage the complex, sensitive and embedded supply chain data.

The
Solution

We built a digital twin of Siemens’ factory based on data lake, GraphQL and Kubernetes which integrates delivery, stock and material requirements data.

The
Impact

Optimizing Siemens’ complex healthcare supply chain through a proactive, user-friendly tool built in an agile and user-centric project.

How to handle complex, sensitive supply chain data from different sources in a corporate environment

Siemens struggled with existing business intelligence tools because these tools fail to display complex relations between data, especially when large amounts of data from different sources are involved. The challenge was to build a tool with a better, intuitive and more precise overview about the production and the process as well as to forecast possible supply bottlenecks.

Having one tool is not good enough, connecting more tools is too complicated

Adding to the challenge, the solution had to work within in a complex supply chain that involves sensitive data from multiple sources embedded in a large corporate infrastructure. Despite the strongly regulated healthcare sector, Siemens needed an agile and user-centric project management in order to quickly reach their goals.

Simplifying complex data for a better UX

In several user-centric Design Thinking iterations we developed a lean user interface which significantly enhanced the UX for the tool‘s users. For instance, production state and bottlenecks are visualized by graphs and a traffic light-based color-coding system, which is determined by comparing expected stock and demand. Further, users can filter and search easily, without being exposed to the complicated data structure.

Developing a data strategy and infrastructure

First, we helped to set up a data lake that gathered data from different sources. Then we were able to abstract away relevant data through a GraphQL API. Kubernetes completed the data infrastructure that allowed a dynamic and safe application of a new business intelligence tool that merged delivery data, material stock data and material requirements data in a real time digital twin.

Tackling the challenge in a four step process

  1. The first phase was the ideation con conceptional matter like the most important data, crucial functions, desired UI and other technical requirements.
  2. Next, we developed the proof of concept. It was a first version that allowed data processing as well as data integration from different sources.
  3. In the third phase, we developed an MVP that integrates the whole infrastructure, including data, design and additional functions like language options.
  4. Lastly, before deploying the digital twin, we went through an extensive two-weeks testing phase within the corporate environment.

A Digital Twin to optimize Siemens’ Supply Chain

Most importantly, through its agile and user-centric processes the digital twin project allowed Siemens Healthineers to quickly optimize their supply chain despite the heavily regulated healthcare sector.

Additionally, Siemens Healthineers is now able to avoid production stops, mistakes and bottlenecks. The proactive, user-friendly tool significantly enhances work processes for the users and enables employees to handle sensitive data in a complex firm infrastructure.

This is not just important for Siemens and its employees, but also for the whole ecosystem of their supply chain as it helps everyone involved to optimize co-dependent processes such as just-in-time delivery or make-to-order-production.

“Virtual Factory ensures a better transparency on the material situation and the material resupply process. It helps us to improve the flexibility to take reactions for bottlenecks, identify the weak spots at working processes and gain a more robust supply chain management.”

Xinya Xu
Siemens Healthineers

“Not only did we have the opportunity to develop a production-grade solution for an industry leader like Siemens but also marry the Siemens business knowledge with the latest implementation and hosting technology, resulting in a stable, scalable and relevant product.”

André Lubbe
Project Owner, Motius

Want to build a product of the future?

The
Challenge

Siemens struggled with existing business intelligence tools because these tools were not good enough to manage the complex, sensitive and embedded supply chain data.

The
Solution

We built a digital twin of Siemens’ factory based on data lake, GraphQL and Kubernetes which integrates delivery, stock and material requirements data.

The
Impact

Optimizing Siemens’ complex healthcare supply chain through a proactive, user-friendly tool built in an agile and user-centric project.

How to handle complex, sensitive supply chain data from different sources in a corporate environment

Siemens struggled with existing business intelligence tools because these tools fail to display complex relations between data, especially when large amounts of data from different sources are involved. The challenge was to build a tool with a better, intuitive and more precise overview about the production and the process as well as to forecast possible supply bottlenecks.

Having one tool is not good enough, connecting more tools is too complicated

Adding to the challenge, the solution had to work within in a complex supply chain that involves sensitive data from multiple sources embedded in a large corporate infrastructure. Despite the strongly regulated healthcare sector, Siemens needed an agile and user-centric project management in order to quickly reach their goals.

Simplifying complex data for a better UX

In several user-centric Design Thinking iterations we developed a lean user interface which significantly enhanced the UX for the tool‘s users. For instance, production state and bottlenecks are visualized by graphs and a traffic light-based color-coding system, which is determined by comparing expected stock and demand. Further, users can filter and search easily, without being exposed to the complicated data structure.

Developing a data strategy and infrastructure

First, we helped to set up a data lake that gathered data from different sources. Then we were able to abstract away relevant data through a GraphQL API. Kubernetes completed the data infrastructure that allowed a dynamic and safe application of a new business intelligence tool that merged delivery data, material stock data and material requirements data in a real time digital twin.

Tackling the challenge in a four step process

  1. The first phase was the ideation con conceptional matter like the most important data, crucial functions, desired UI and other technical requirements.
  2. Next, we developed the proof of concept. It was a first version that allowed data processing as well as data integration from different sources.
  3. In the third phase, we developed an MVP that integrates the whole infrastructure, including data, design and additional functions like language options.
  4. Lastly, before deploying the digital twin, we went through an extensive two-weeks testing phase within the corporate environment.

A Digital Twin to optimize Siemens’ Supply Chain

Most importantly, through its agile and user-centric processes the digital twin project allowed Siemens Healthineers to quickly optimize their supply chain despite the heavily regulated healthcare sector.

Additionally, Siemens Healthineers is now able to avoid production stops, mistakes and bottlenecks. The proactive, user-friendly tool significantly enhances work processes for the users and enables employees to handle sensitive data in a complex firm infrastructure.

This is not just important for Siemens and its employees, but also for the whole ecosystem of their supply chain as it helps everyone involved to optimize co-dependent processes such as just-in-time delivery or make-to-order-production.

“Virtual Factory ensures a better transparency on the material situation and the material resupply process. It helps us to improve the flexibility to take reactions for bottlenecks, identify the weak spots at working processes and gain a more robust supply chain management.”

Xinya Xu
Siemens Healthineers

“Not only did we have the opportunity to develop a production-grade solution for an industry leader like Siemens but also marry the Siemens business knowledge with the latest implementation and hosting technology, resulting in a stable, scalable and relevant product.”

André Lubbe
Project Owner, Motius

Want to build a product of the future?

The
Challenge

Siemens struggled with existing business intelligence tools because these tools were not good enough to manage the complex, sensitive and embedded supply chain data.

The
Solution

We built a digital twin of Siemens’ factory based on data lake, GraphQL and Kubernetes which integrates delivery, stock and material requirements data.

The
Impact

Optimizing Siemens’ complex healthcare supply chain through a proactive, user-friendly tool built in an agile and user-centric project.

How to handle complex, sensitive supply chain data from different sources in a corporate environment

Siemens struggled with existing business intelligence tools because these tools fail to display complex relations between data, especially when large amounts of data from different sources are involved. The challenge was to build a tool with a better, intuitive and more precise overview about the production and the process as well as to forecast possible supply bottlenecks.

Having one tool is not good enough, connecting more tools is too complicated

Adding to the challenge, the solution had to work within in a complex supply chain that involves sensitive data from multiple sources embedded in a large corporate infrastructure. Despite the strongly regulated healthcare sector, Siemens needed an agile and user-centric project management in order to quickly reach their goals.

Simplifying complex data for a better UX

In several user-centric Design Thinking iterations we developed a lean user interface which significantly enhanced the UX for the tool‘s users. For instance, production state and bottlenecks are visualized by graphs and a traffic light-based color-coding system, which is determined by comparing expected stock and demand. Further, users can filter and search easily, without being exposed to the complicated data structure.

Developing a data strategy and infrastructure

First, we helped to set up a data lake that gathered data from different sources. Then we were able to abstract away relevant data through a GraphQL API. Kubernetes completed the data infrastructure that allowed a dynamic and safe application of a new business intelligence tool that merged delivery data, material stock data and material requirements data in a real time digital twin.

Tackling the challenge in a four step process

  1. The first phase was the ideation con conceptional matter like the most important data, crucial functions, desired UI and other technical requirements.
  2. Next, we developed the proof of concept. It was a first version that allowed data processing as well as data integration from different sources.
  3. In the third phase, we developed an MVP that integrates the whole infrastructure, including data, design and additional functions like language options.
  4. Lastly, before deploying the digital twin, we went through an extensive two-weeks testing phase within the corporate environment.

A Digital Twin to optimize Siemens’ Supply Chain

Most importantly, through its agile and user-centric processes the digital twin project allowed Siemens Healthineers to quickly optimize their supply chain despite the heavily regulated healthcare sector.

Additionally, Siemens Healthineers is now able to avoid production stops, mistakes and bottlenecks. The proactive, user-friendly tool significantly enhances work processes for the users and enables employees to handle sensitive data in a complex firm infrastructure.

This is not just important for Siemens and its employees, but also for the whole ecosystem of their supply chain as it helps everyone involved to optimize co-dependent processes such as just-in-time delivery or make-to-order-production.

“Virtual Factory ensures a better transparency on the material situation and the material resupply process. It helps us to improve the flexibility to take reactions for bottlenecks, identify the weak spots at working processes and gain a more robust supply chain management.”

Xinya Xu
Siemens Healthineers

“Not only did we have the opportunity to develop a production-grade solution for an industry leader like Siemens but also marry the Siemens business knowledge with the latest implementation and hosting technology, resulting in a stable, scalable and relevant product.”

André Lubbe
Project Owner, Motius

Want to build a product of the future?

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