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AI Powered Smart-Guidance for Smart Manufacturing: Nikunj Mehta - Founder & CEO, Falkonry

In this episode, we dive deep into the world of smart manufacturing with industry expert Nikunj Mehta from Falkonry. If you're curious about how data is transforming industrial operations and the future of maintenance and reliability, this episode is for you!

Here are some key takeaways:

82% of Failures Are Random
Nikunj explains that a staggering 82% of failures in industrial systems appear random. Without understanding their causes, manufacturers struggle to prevent them. This is where smart, data-driven actions come into play to improve decision-making and reduce failures​.

Condition-Based Actions: A Game Changer
In manufacturing, decisions often rely on experience, which can take years to accumulate. Condition-based actions allow manufacturers to make smarter decisions without needing decades of experience. By detecting and acting on real-time conditions, manufacturers can optimize maintenance, improve quality, and reduce emissions​.

Real-Time Data = Real-Time Decisions
From mining to steel production, the power of real-time data can revolutionize how we handle variations in materials, weather conditions, and equipment performance. Nikunj shares how timely insights enable proactive decision-making, reducing downtime and energy waste​.

Smart Guidance Systems
Smart manufacturing requires systems that can analyze data in real-time and offer actionable guidance. Think of it like a GPS for your factory: these systems navigate complex production challenges and direct optimal actions for maintenance, quality control, and emissions​.

What's Next for Smart Manufacturing?
Nikunj forecasts that the next step in manufacturing will be integrating smart guidance systems across various processes—from maintenance to quality assurance—allowing companies to move from reactive to proactive management​.

 

Visual Intelligence Applications in Manufacturing: Cyrus Shaoul - CEO, Leela AI

 

In our latest podcast episode, I had the pleasure of speaking with Cyrus Shaoul, CEO of Leela AI, about visual intelligence and its transformative impact on manufacturing operations. 

 

Here are some Key Takeaways:

 

1️⃣ Beyond Traditional Machine Vision

Unlike traditional machine vision systems that focus on product inspection, visual intelligence looks at the entire manufacturing process. It helps identify value-adding activities in real-time, ensuring operational excellence is met consistently.

 

2️⃣ Uncover Hidden Performance Insights

By integrating visual intelligence, companies can detect bottlenecks and wasted time during manual operations. In one case, Lila AI improved line capacity by 20% by identifying areas where standard operating procedures weren’t being followed.

 

3️⃣ Boost Safety & Compliance

With advanced monitoring, manufacturers can significantly reduce safety violations. One customer saw a 50% reduction in non-compliant events, leading to fewer accidents and a safer work environment.

 

4️⃣ Improving Quality Control

Visual intelligence doesn’t just ensure processes run smoothly; it improves quality control by catching invisible defects in real-time, boosting yields by 10%. This kind of proactive monitoring helps prevent costly mistakes that traditional methods might miss.

 

5️⃣ Faster, Data-Driven Decisions

With visual intelligence, data is constantly collected and analyzed, allowing teams to make real-time adjustments and enhance productivity, safety, and quality simultaneously. The ROI on this technology speaks for itself.

 

🎧 Tune in to hear the full conversation and explore how visual intelligence is reshaping the future of manufacturing.

 

Building Intelligent Digital Twins with Generative AI : Pieter Van Schalkwyk - CEO, XMPRO

In my latest AI in Manufacturing podcast episode, I had the pleasure of interviewing Peter, CEO of XMPRO where we discussed How to Build Intelligent Digital Twins with Generative AI.

Here are five key takeaways:

1. Digital Twins Are Evolving: What was once just a static data model has now become anticipatory. Digital twins are now being embedded with AI, moving from being reactive (responding to issues) to proactive (predicting issues before they occur).

2. Generative AI is Revolutionizing Business Processes: Generative AI models are not just helping manufacturers with personal productivity tasks like drafting emails—they’re driving large-scale process improvements. For example, EV manufacturers have used AI to reduce human involvement in generating specifications by 90%.

3. The ‘Utility’ of AI is Like Electricity: Much like the railroads and electricity in the past, generative AI is creating a new utility that industries can tap into without needing to build their own models from scratch, unlocking countless business opportunities.

4. The Rise of Multi-Agent Generative Systems: We’re now seeing the development of multi-agent systems where digital twins act as virtual employees. These agents can observe, reflect, and even recommend or take action based on real-time data, enhancing operational efficiency.

5. Start Small and Scale: Peter emphasized not trying to "boil the ocean" when implementing new technologies. Begin with small, incremental changes that can demonstrate value and build out from there.

Ep 43 Infrastructure as Code for Industrial IoT - [ Peter Sorowka, CEO Cybus GmbH]

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.

 

Outline

Introduction to Infrastructure as Code for Industrial Digitalisation
IaC workflow for streamlining the deployment of industrial digital solutions
IaC for management of industrial software configuration 
Balance between UI and DevOps for OT Engineers
What does High Availability and Scalability mean for OT?
Effective data governance strategy for digital transformation?
Cybus Connectware IaC architectural layout
Azure IoT operations vs HiveMQ and Cybus
IaC Case Studies
The Future of IaC

Ep 42 Data Driven Optimization in Process Industries - [ Jim Gavigan, President, Industrial Insight]

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.

Ep 41 Applied AI in Manufacturing - [ Roey Mechrez, Head of AI, and EMEA MD @Tulip]

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

Ep 40 Digital Twins for Process Optimisation and Asset Reliability - [ Erik Udstuen, CEO TwinThread]

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

Ep 39 DataOps for Digital Transformation In Manufacturing - [ Aron Semle, CTO Highbyte]

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

 

 

Ep 38 LoRaWAN for Industrial IoT Applications - [ Wienke Giezeman, The Things Industries]

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.

 

Ep 37 Data Modelling and Manufacturing Ontologies for Digital Twins - [ Erich Barnstedt - Microsoft ]

Digital transformation in manufacturing fundamentally involves transforming unprocessed data into valuable insights to guide business decisions through automated systems or human intervention.

Consequently, implementing a well-thought-out data modelling strategy is key to successful digital transformation as it helps to express the meaning of the data to digital systems.

To learn more about Data modelling for Industrial IoT in general and for Digital Twin use cases in particular, I had a podcast conversation with Erich Barnstedt.

Erich is the Chief Architect for Standards, Consortia and Industrial IoT in the Azure Edge and Platform team at Microsoft

Here's the outline of our conversation

✅ Importance of data modelling for Industrial IoT.
✅ Key Elements of an Effective IIoT Data Model
✅ Standardising Configuration Interface for OPC UA Connectivity Mapping
✅ Manufacturing Ontologies Reference Solution for Digital Twins
✅ UA Cloud Publisher and UA Cloud Twin for Mappping Industrial Assets to Azure Digital Twins using ISA95
✅ Significance of UA Cloud Commander at the Industrial Edge
✅ OPC UA Information Model Integration using UA Cloud Library
✅ Data Modelling Standards
✅ Web of Things for Endpoint and Interface Description of Industrial Assets.
✅ ChatGPT for fully automating onboarding of non-discoverable industrial assets
✅ Converting proprietary interfaces into OPC UA Information Model using UA Edge Translator.
✅ The role of the IEC/ISO in standardizing data models for IIoT
✅ The Scope OPC UA PubSub Over MQTT in Industrial IoT
✅ Metadata and Type Information in OPC UA PubSub
✅ Industrial Metaverse Reference Architecture with Open Interoperability Standards