The Fourth Generation Podcast Kudzai Manditereza - Industry40.tv

Peter Sorowka is a recognized expert in Industrial IoT and the technical architecture of data-driven industrial production. In 2015, he founded Cybus - a software company specializing in secure and governance-strong IIoT Edge and Smart Factory solutions.
As CEO of Cybus, he has been advising and guiding global enterprises towards decentralized, secure Smart Factory and data-driven Smart Services across various industries such as automotive and battery manufacturing, machinery and tool builders or metal processing.
 
OutlineIntroduction to Infrastructure as Code for Industrial DigitalisationIaC workflow for streamlining the deployment of industrial digital solutionsIaC for management of industrial software configuration Balance between UI and DevOps for OT EngineersWhat does High Availability and Scalability mean for OT?Effective data governance strategy for digital transformation?Cybus Connectware IaC architectural layoutAzure IoT operations vs HiveMQ and CybusIaC Case StudiesThe Future of IaC

Had the pleasure of hosting Jim Gavigan on my latest podcast episode, where we deep-dived into "Data-Driven Optimization in Process Industries."
We discussed leveraging data for efficiency, the challenges of data quality, and choosing between foundational principles and cutting-edge ML algorithms.
Jim also highlighted the significance of tools and strategies in this sphere, emphasizing the urgency of digitizing domain knowledge in the face of an impending knowledge drain.
Jim, is the President and Founder of Industrial Insight, Inc. where he helps industrial companies turn data into actionable information to deliver tangible results for their organization.
Here is the outline of our conversation:
✅ Principles of Data-Driven Process Optimization✅ Opportunities in data-driven optimization and use case✅ Challenges faced by industries when implementing data-driven optimization strategies?✅ Overcoming the hurdles of data quality and fidelity?✅ First principles vs. Multivariate data analysis vs. ML algorithms?✅ Evaluating readiness to effectively integrate AI/ML in process optimization✅ Tech stack for data-driven optimization✅ Impending knowledge drain, and capturing domain knowledge into digital tools.

By now, we're all aware of the profound impact Generative AI promises for manufacturing. Beyond just assisting engineers in application development, it equips managers with cutting-edge analytics and delivers invaluable error resolution insights to technicians, etc. - all through intuitive interactions.
That's why I'm excited about Tulip Interfaces' new "Frontline Copilot" which uses LLMs for natural language interaction between operators and manufacturing systems.
To truly comprehend its significance and the broader implications of Applied AI in manufacturing, I spoke with Roey Mechrez, PhD in my latest podcast episode.
Roey is the Head of AI and EMEA MD at Tulip Interfaces where he is overseeing Tulip's Machine Learning and Computer Vision strategy.
Here's the outline of our conversation:
Outline✅ Introduction to the Tulip Ecosystem✅ Composable, App-based solutions vs. Monolithic MES✅ Tulip for Process Engineer, Operator, Manager End Users✅ Technology stack for Modern Manufacturing✅ Tulip Ecosystem for Applied AI in manufacturing✅ Connecting shop-floor visuals to advanced analytics tools.✅ How Data is Shaping the Next Layer of Manufacturing✅ Tulip connectivity and AI integrations for building shop-floor solutions✅ Introducing Tulip Frontline Copilot: Why Generative AI Matters for Manufacturing✅ Natural Language Operator Interaction with Tulip Copilot, Use Cases✅ Integrating legacy machine data into modern systems with Machine Learning and Edge Connectivity✅ Computer Vision Capabilities and Third-party Integrations in the Tulip Ecosystem.✅ Driving Forces for AI Adoption in Manufacturing✅ The Future of AI in Manufacturing

For years, manufacturers have had to navigate in relative blindness, implementing improvements on an as-needed, reactive basis.
This approach, although functional, has been markedly inefficient and reactive, particularly in terms of process optimisation and asset reliability, two vital aspects of industrial operations that can profoundly impact efficiency and profitability.
Digital Twins represent a transformative shift from this reactive approach to a proactive, predictive one. They facilitate a deeper understanding of how systems behave, providing industrial operators with actionable insights that were previously unavailable.
To learn more about the application of digital twins in manufacturing, I had a podcast conversation with Erik Udstuen, who is the CEO and co-founder of TwinThread, a company that provides a digital twin platform that combines Industrial Data with Industrial AI in an integrated development environment for engineers and data scientists.
Here's the outline of our conversation
 
✅ Challenges driving Digital Twins adoption in modern manufacturing✅ Key Functions of Digital Twins in Manufacturing✅ Industrial AI Ops✅ Use Cases for Asset and Process Digital Twins✅ Connectivity standards for physical assets to digital twins✅ Best practices for modelling assets and processes for digital twins ✅ Effective infrastructure abstraction techniques for Digital Twin Implementation✅ ISA 88 / 95, and Data Modeling standards for Digital twins✅ Principles-based vs Machine Learning-based modelling for advanced analytics✅ Practical examples of successful digital twin applications in Manufacturing ✅ Selecting a digital twin platform and evaluating capabilities✅ TwinThread Digital Twin Integrated development environment

DataOps for Digital Transformation In Manufacturing. In this episode, Kudzai Manditereza interviews Aron Semle, the CTO of Highbyte. HighByte is an industrial software company founded in 2018 with headquarters in Portland, Maine USA. The company builds solutions that address the data architecture and integration challenges created by Industry 4.0. HighByte Intelligence Hub, the company’s award-winning Industrial DataOps software, provides modeled, ready-to-use data to the Cloud using a codeless interface to speed integration time and accelerate analytics.
 
Outline✅ What is DataOps, and Why is it relevant for Digital Transformation?✅Key Steps of Implementing DataOps for a digital transformation strategy.✅ The Role of DataOps in a Unified Namespace Architecture✅Typical Data Sources for Integrating into DataOps Pipeline✅ Effective practices for modeling, normalization, and contextualization of Industrial data.✅ Data Modeling Standards for DataOps, Pros, and Cons✅The role of MQTT and Sparkplug in a DataOps-based Strategy✅ The role of OPC UA in DataOps Strategy✅ DataOps for Historical Data in the Unified Namespace✅ REST API for real-time and transactional data harmonization✅ Best practices for DataOps deployment
 
 

As companies with industrial operations struggle to economically access data from intelligent devices located in remote and challenging environments, LoRaWAN presents itself as a cost-effective solution.
With the capacity to locally integrate industrial data and transfer it via a private LoRaWAN network over vast distances, LoRaWAN simplifies protocol conversion and enhances data recovery.
To learn more about the LoRaWAN for Industrial IoT applications I had a chat with Wienke Giezeman. Wienke is the CEO & Co-founder at The Things Industries, a scalable LoRaWAN solutions provider, and the initiator of The Things Network, the first crowdsourced free and open 'Internet of Things'
Here's the outline of our conversation.
✅ Introduction to LoRaWAN and The Things Stack✅ Common LoRaWAN Use Cases in Industrial IoT.✅ Typical architecture of a LoRaWAN solution for IIoT.What are the key edge components involved?✅ Key considerations for designing a LoRaWAN wireless sensor network for IoT.✅ Best practices for Integrating LoRaWAN data into IIoT Platforms and Controls Networks.✅ LoRaWAN gateway and node selection.✅ Security in LoRaWAN networks✅ The role of MQTT and other IIoT protocols in LoRaWAN deployments.✅ Enterprise and cloud integration capabilities of The Things Stack✅ Emerging trends or features in the LoRaWAN space.