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AIoT Lab header

AIoT Lab Heilbronn

The AIoT Lab was set up in 2021 to help validate AIoT-enabled business ideas and do practical research on the key concepts described by the AIoT Framework. The goal of the AIoT Lab is to provide a collaboration platform for testing and validating some of the concepts which we are developing in the AIoT User Group. The primary motivation is to provide smart connected products and solutions that illustrate how the challenges in real world can be overcome. The AIoT Lab Heilbronn is operated by Ferdinand-Steinbeis-Institute at the Research Campus in Heilbronn. This setup ensures access to the academic network in Heilbronn, which can be easily extended to include the greater Stuttgart area and Baden Würtemberg. Technological as well as practical expertise is offered in the duality of research and entrepreneurship in order to create an environment that reflects the AIoT Playbook.

The AIoT Lab is planning to support additional industrial use cases in the digital.industry Working Group. The research focus is on making the creation of AIoT-enabled industrial solutions more efficient and better manageable for industrial users. The lab is also a significant contributor to the Working Group. Participants can propose new research topics or initiate micro testbeds. The lab provides AIoT expertise and access to an academic ecosystem.

Scope and Position

AIoT Lab Business perspective

AIoT lab focuses not only on the technological side of industry but also provides an opportunity on the business side as well by exploring various business capabilities and validating newer concepts. Therefore, the focus of AIoT lab on the business perspective which ultimately helps in growth and scalability of the products or solutions in industry.

Focus Area

AIoT Lab Focus
Value Stream Management (VSM)

Value Stream Management is a technique management that aims on growing of the flow of business value from customer perspective.

  • Processes and methods - The development and validation of a detailed Processes and methods is usually the first step in the journey towards developing a new smart, connected product or solution. New market entrants are looking at disruptive new models enabled by the combination of physical products with AIoT.
  • AIoT DevOps -The introduction of AI to the traditional development process is adding many new concepts, which create challenges for DevOps. The development of AI-based systems also introduces a number of new requirements from a DevOps perspective.
  • Metrics - Creation and tracking of key engineering metrics - including metrics for the AI-part of the system.

How can re-use of existing product/solution be maximized, especially from the point of view of smaller companies with limited R&D capabilities.

  • AI Model - An AI model is a program or algorithm that utilizes a set of data that enables it to recognize certain patterns. This allows it to reach a conclusion or make a prediction when provided with sufficient information.
  • Datasets- Training data is the data you use to train an algorithm or machine learning model to predict the outcome you design your model to predict.
  • Sensors / sensor packs - Sensor edge nodes are edge nodes that are specifically designed to process sensor data.
  • Infrastructure: The hardware for a smart, connected product must include all required physical product components. This means that it will include not only the edge/cloud/AI perspective but also the physical product engineering perspective. This will include mechatronics, a discipline combining mechanical systems, electric and electronic systems, control systems and computers.
  • Patterns

The ultimate goal is to ensure that the business can be scaled up to the level that matches the business and technical objectives. This is usually a step-by-step process, involving exploration, acquiring early adopters, and then continuous growth.

  • Organization - Ensuring organizational scalability is another key success factor for smart, connected products. Understanding how an organization can successfully grow and evolve alongside the product as it matures from idea to large-scale business is one of the main key points.
  • Architecture- the AIoT Playbook proposes to create and maintain a product/solution architecture that captures key requirements and design decisions in a consistent manner.
  • DevOps Processes -DevOps organizations breakdown the traditional barriers between development and operations, focusing on cross-functional teams that support all aspects of development, testing, integration and deployment. Successful DevOps organizations avoid overspecialization and instead focus on cross-training and open communication between all DevOps stakeholders.


Applying focus on such areas results in various business benefits for the organization from improved organizational processes to monetary benefits. Noticeable improvements is to be gained in the product/ solution area. Few such areas are mentioned below:

Business Benefits
  • Increased Revenue
  • profit
  • ARR/subscription revenues
  • process efficiency
  • OEE
  • TTM
  • Dev cost reduction
  • Managed complexity
  • Making sourcing decision easier


Domain Projects Summary Research Focus
Pneumatic system
Pneumatic systems AIoT-enabled system for automatic leak detection in pneumatic systems
  • AIoT VSM best practices
  • Global test network: scalability, AIoT devops
  • ML model re-use
Exploration Tools and best practices for digital innovation in automative
  • Playground: Rapid prototyping with VSS
  • Simulation: VSS-based simulation environment
VSM Value Stream Management
  • Best practices for metrics, modeling and system support
Elevator Monitor
Elevator Monitoring Use of AIoT for status monitoring and predictive maintenance of elevators
  • Sensor technology
  • Digital Twin abstractions
  • AI algorithms
Drones for Building Inspection
Facade Drone-based building facade inspection
  • Sensor technology
  • BIM (Building Information Models)
  • AIoT DevOps

Collaboration Benefits

Stakeholder Benefits
Enterprise Users
  • Outcome-oriented research + exploration
  • Technical validation of business ideas
  • Access to AIoT experts
  • AIoT methodology / hands-on
  • Technical guidance
Solution Providers
  • Customer feedback
  • Networking effects
Other research labs
  • Access to global network for real-world AIoT field tests
Universities / academia
  • Joint research
  • Joint publications