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What MindConnect elements are available to connect to MindSphere?

Siemens Valued Contributor Siemens Valued Contributor
Siemens Valued Contributor

MindSphereSupplyChain.pngFood supply chain transparency with MindSphereDTM_UseCases.pngData sharing use cases


Within the next decades, the global food industry must feed a growing world population with limited arable land and water resources. It is very unlikely, that this will happen with traditional and regional food production. Instead, global food supply chains will become more important. Making them transparent for the consumer, efficient, sustainable and safe will be important challenges for all involved stakeholders.

Central to the success of food supply chains is the consumer’s trust in them and his interaction with them. In order be trusted, food supply must be transparent to the consumer. Today, consumers want to know, where food comes from, how it was produced and processed and what it contains. There is also an increasing trend for buying or ordering food via the internet. And even first examples for producing personalized food or co-creation of food with the consumer can be found.

Another important stakeholder in food production are regulatory bodies, especially those involved in food safety regulation and control. Efficient control of food safety in supply chains not only protects the people’s health, but also increases the consumers’ trust in food production.

Finally, all the stakeholders in food production and processing like farmers, food producers, retailers and logistics service providers need to improve their processes, both due to increasing competition and novel regulation. This requires enough insight into these processes. Obviously, this insight will come- not only from lots of data being collected but also from their analysis.


This is where digital twins come into play: these virtual counterparts of products, machines, processes and documents contain structured information which is essential for analysis and decision making. Let us look at the digital twin of a food product, say a pudding. Its main ingredients are milk, cream, sugar and flavor. The digital twins of milk and cream contain information about the raw milk, from which they were produced. This includes information about the farm and its cows, which produced the raw milk, information on the raw milk’s composition and information on the conditions during transport from the farm to the food producer. Similarly, for the flavor and sugar: their digital twins contain information about their origin, ingredients and production conditions. Part of this information is relevant for the end consumer, part of it for the food producer, e.g. the raw milk’s composition.

During pudding production, additional information is produced, e.g. the conditions during the production process and key performance parameters of the different production steps. Part of the data is related to the product which is produced, part of the data is related to the machines which are used for production. The latter enter the machine’s digital twin.

The owner of digital twin data will benefit of its analysis, of course. However, a lot of additional value can be created by sharing part of these digital twin data with selected partners. Part of this information exchange is required by regulation, part may be due to bilateral agreement:

  • A farmer may share laboratory data of milk and treatment of cows with the food producer he delivers
  • A food producer may share part of a machine’s digital twin data with the machine builder, e.g. for maintenance or remote operation
  • A food producer may also share part of a product’s digital twin data with a food safety agency for regular control without any paper work
  • A food producer may share part of the product digital twin with the retailer, e.g. type and origin of ingredients or shelf life expiration
  • A logistics service provider adds data on transport condition to the digital twin of a product
  • A retailer provides information on products to consumers, e.g. origin and type of ingredients, how these were produced and information on allergens

Sharing data with stakeholders along the food value chain can also be used to determine the aggregated consumption of e.g. energy or water of a final food product. By analyzing such data and deriving appropriate actions, supply chains can be made safer, more efficient and more sustainable.

The exchange of digital twin data is most efficiently done with suitable platforms. Currently, we are working with EIT Food ( partners Strauss Group (, Givaudan (, Fraunhofer ( and Technical University Munich ( on the development of such a platform. It supports setting up data models for digital twins, sharing selected digital twin data with specific partners and analyzing these data for specific use cases. The focus so far was on tracking & tracing, in 2019 the analysis of food safety events will be tackled.

We would like to invite you to a webinar presenting the Digital Twin of the Supply Chain solution. Givaudan and Siemens will talk about the benefits of the solution and how it has been implemented using MindSphere.

Please register for the webinar:

The solution will be presented on Hannover Fair 2019 as well. In addition see the Digital Twin of a chips supply chain with Blockchain live at Hannover Fair 2019, April 1st - 5th, hall 9 – Siemens F&B booth D35.