From platforms, solutions to AI-driven analytics: solving complex industry challenges with software innovation
Backed by deep domain expertise, our software companies take on critical industry specific problem spaces and deliver leading-edge software platforms, applications and advanced analytics solutions to optimize processes, identify operational inefficiencies, improve product quality, or to ensure operational safety.
IIoT Platforms (Vertical IoT)
Data Management Platforms
Software Solutions and Applications
AI/ML Powered Analytics (Vertical AI)
IIoT Platforms
The need for seamless IIoT data integration in a fragmented market
The Internet of Things (IoT) has advanced industries by enabling real-time data collection and analysis. However, the diversity of IoT devices and the data they generate can create significant integration challenges. Many companies struggle with siloed data, incompatible systems, different communication protocol standards, and complex integration processes for diverse sensor data or data from multiple machinery and equipment. This is particularly true in industries like semiconductor, photonics, SMT electronics, aerospace & defense, medical devices, and automotive, where precision, yields and efficiency are paramount.
Distributed assets and critical infrastructure further require flexible sensor integration and seamless data access to power unified, near real-time insights enabling active risk management and safety solutions for challenging industries including – energy/utilities, transportation, mining, and construction.
Vertical IIoT Platforms
Vertical IIoT platform solutions in our focus address these industry challenges, provide immediate access to real-time machine, sensor and any other data via plug-and-play solutions that collapse the complexity of machine, sensor or supplier data integration, automation and management (often as part of a larger integrated platform solution) – providing the necessary backbone for advanced analytics and insights that drive business value.
Operational pipelines
Centrally managed operational pipelines enabling end-to-end automations enhanced with in-stream AI/ML and the ability to operate across cloud and on-premise environments is a critical requirement for digital solutions in many industries and applications. Shedding the constraints of single environment architectures allow for scaled deployment that has hampered adoption of technology in many challenging applications.
Data Management Platforms
The path forward must also involve a deployment of modern, scalable and versatile data management platforms for complex data integration scenarios and single, uniform platforms that dismantle data silos, lack of context and inform decision making across an organization.
- Platforms enabling data contextualization (data of any type and source) for improved data quality enabling drill-down-capabilities (e.g. for root-cause analysis in advanced analytics) and further providing a richer and cleaner data training ground for more powerful ML-applications (industrialized ML). Contextualized data is easier to analyze and interpret, leading to more accurate and actionable insights.
- Low latency, self-managed turnkey platform solutions with integrated data pipeline management tools (reproducible data pipelines), MLOps, and geo-distributed datastore capabilities to analyze geographically distributed data sources (e.g. for global multi-site industrial use cases and network analytics insights)
- Ready-to-use platforms and tools that retain enriched data close to the source (edge) and that can aid-in and accelerate the development and implementation of customer-centric software applications and insights for real-world applications and use cases in our core industries
Software Solutions and Applications
By better leveraging data through empowered analytics, companies can optimize their operations and value chains, provide improved products/services and improve operational efficiency and safety.
- Modern MES/MOM and smart manufacturing systems for real-time monitoring and control of entire production processes with integrated serialized traceability and advanced analytics (often with an IIoT platform as part of the offering)
- Advanced yield management software
- Network performance and consumer analytics for broadband and mobile network operators
- Software and data analytics solutions enabling active risk management and safety solutions for Critical Infrastructure verticals
We are also looking for software and analytics tools that enhance the capabilities of advanced technology machinery (e.g. new imaging analysis software for inspection systems), test and validation software, and innovative solutions for automated software-controlled process workflows.
AI/ML Powered Analytics
Industrial AI, also known as AI in Manufacturing, is the use of artificial intelligence technologies to improve manufacturing processes. Industrial AI primarily leverages machine learning algorithms to analyze manufacturing data and provide further insights into the manufacturing operation.
This can help manufacturers to increase operational efficiency by reducing downtime, increase labor and machine utilization, reduce scrap rates, and improve overall product quality. Other vertical AI application areas in our focus include operational safety and asset efficiency in critical infrastructure verticals.
Our companies strive to put AI to work effectively. By leveraging ML/Deep Learning methodologies (*) further backed by deep domain expertise and application knowledge, they provide the data and analytics infrastructure and deliver actionable AI/ML-powered insights needed to drive real-world AI use cases within a clearly defined customer specification framework.
By making small and fast steps through pilots, testing, and simulations up to final model deployment – our teams have successfully deployed AI/ML-powered analytics and delivered quantifiable outcomes and benefits for manufacturers. Key use cases supported by our companies – with ML models in production – include quality/yield control, predictive maintenance, and damage predictions and preventions.
(*) supervised learning, reinforcement learning and unsupervised learning / deep learning (computer vision)
