Software
STIWA: Partner at FFG-research project Interactive
Artificial intelligence in the production of the future
Struggling with Inefficiencies and Disconnected Systems?
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Faster ramp-up, reduced maintenance.
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Secure machine communication.
Without a clear system, chaos prevails in production, leading to inefficiencies and stress.
Real-time data insights reduce reliance on manual troubleshooting, minimizing effort and supporting all levels of expertise—resulting in smooth operations, smarter decisions, and consistent output.
CONNECT: Achieve Seamless Communication
With standardized interfaces, ensure bidirectional communication between your systems. How much longer can you afford to let inefficiencies hold you back?
MONITOR: Keep an Eye on What Matters
Stay informed with real-time insights into your production processes. Are you fully aware of what's happening on your shopfloor?
OPTIMIZE: Reduce Losses, Improve Precision
Quickly identify and address losses, deviations, and production fluctuations. How fast can you respond to production challenges with your current tools?
CONTROL: The Only HMI You Need
Take full command of your machine and production lines with a single, centralized user interface. Is your production ready for complete control?
Production delays, machine breakdowns, and overwhelming data make it hard to find the root cause.
We understand this frustration—because we’ve faced it too. That’s why we’ve developed tools to eliminate the guesswork. With real-time insights, you can take control of your production, resolve issues faster, and make confident, data-driven decisions that drive real results.
Our customers no longer deal with:
performance inefficiencies, data visibility challenges, or the lack of actionable data, enabling smoother operations and better outcomes.
The two-year research project Interactive wants to answer the question how to improve the quality of production with the help of artificial intelligence. STIWA works decisively on establishing the basics for a machine learning model, that is further developed and optimized by feedback of the user.
New and interactive learning methods
Goal of the project in the scope of the Austrian Research Promotion Agency is the research and development of new interactive learning methods that include humans in the machine learning process. “This way the comprehensibility and acceptance of statements is increased and at the same time the operator is given the possibility to change and develop the algorithms independently. Base for this is a customized processing of the most relevant data, as well as an intuitive and efficient illustration in the user interface to make feedback as easy as possible for the operator” explains data scientist Stefan Stricker, that is going to accompany this project decisively from the side of STIWA Group.
Machine learning model
The prototype developed in the scope of Interactive is based on data of two industrial application cases. It is essential here, that the data of the involved machines stays locally secured and only model data is joined in the algorithm according to Stricker: “This creates the foundation of establishing a common, robust machine learning model, that further develops and optimizes with the integration of various industrial applications.”
Own homepage informs about the project progress
The Austrian Institute of Technology (AIT), Siemens AG and the refrigerated cabinet and refrigeration technology provider Hauser GmbH are the partners in this research project, which is meant to last two years. An own website consistently inform about the project progress: www.interactive-project.info
3
MVP & PoC
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4
Rollout
Scale your validated solution with STIWA, ensuring a global rollout and impactful digital shopfloor transformation.
Partnering for Success
What Sets STIWA Software Apart?
Developed by manufacturers with decades of hands-on production experience, this adaptable and scalable software delivers transparency, reduces costs, and supports global standardization—all while maintaining high product quality. Built by experts who truly understand your industry challenges, ensuring it adapts to your needs at any stage.
STIWA Shopfloor Software goes beyond traditional MES solutions by working directly at the machine and control level. It can read PLC data in real time without requiring middleware. This deep integration ensures high-quality data acquisition without delays, making it ideal for analysis and optimization.
Advantage: Customers get a detailed and real-time data foundation, enabling precise production control, error analysis, and process improvements.
STIWA doesn't just focus on visualizing production data; it leverages Artificial Intelligence (AI) and Machine Learning to provide deep insights. The software automatically detects anomalies, bottlenecks, and optimization opportunities, offering actionable recommendations.
Example: AI can predict machine failures, reduce rework defect rates by up to 30%, and adaptively optimize processes.
Advantage: Customers can increase productivity, minimize downtime, and reduce costs through data-driven optimization.
The software is highly flexible and can be integrated into existing production lines (Brown-Field) as well as new, highly connected manufacturing setups (Green-Field). Its modular architecture allows seamless connection to ERP, MES, CAQ, or machine control systems.
Example: Older machines without modern IT interfaces can be integrated via retrofit solutions or IoT adapters, while highly automated systems communicate natively with the software.
Advantage: Companies can digitize step by step without large investments in new machinery while benefiting from modern analysis and optimization tools.
STIWA Shopfloor Software is modular in design, allowing it to adapt to different company sizes and requirements—from single machines to complex, globally connected production networks. Companies can start with selected modules and gradually expand the solution without the need to replace the entire system.
Example: A customer can initially implement real-time monitoring and later add AI-driven optimization or predictive maintenance without requiring a system overhaul.
Advantage: The software grows with business needs, ensuring investment security and keeping companies technologically up to date with continuous advancements.
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Software
STIWA: Partner at FFG-research project Interactive
Artificial intelligence in the production of the future
The two-year research project Interactive wants to answer the question how to improve the quality of production with the help of artificial intelligence. STIWA works decisively on establishing the basics for a machine learning model, that is further developed and optimized by feedback of the user.
New and interactive learning methods
Goal of the project in the scope of the Austrian Research Promotion Agency is the research and development of new interactive learning methods that include humans in the machine learning process. “This way the comprehensibility and acceptance of statements is increased and at the same time the operator is given the possibility to change and develop the algorithms independently. Base for this is a customized processing of the most relevant data, as well as an intuitive and efficient illustration in the user interface to make feedback as easy as possible for the operator” explains data scientist Stefan Stricker, that is going to accompany this project decisively from the side of STIWA Group.
Machine learning model
The prototype developed in the scope of Interactive is based on data of two industrial application cases. It is essential here, that the data of the involved machines stays locally secured and only model data is joined in the algorithm according to Stricker: “This creates the foundation of establishing a common, robust machine learning model, that further develops and optimizes with the integration of various industrial applications.”
Own homepage informs about the project progress
The Austrian Institute of Technology (AIT), Siemens AG and the refrigerated cabinet and refrigeration technology provider Hauser GmbH are the partners in this research project, which is meant to last two years. An own website consistently inform about the project progress: www.interactive-project.info