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  • How to start a predictive maintenance project- with Tom Jacques

    How to start a predictive maintenance project- with Tom Jacques
    2025/05/22
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.What you’ll learn in this episode of Trend Detection:Why starting with the business problem—not the tech—is crucial for predictive maintenance successThe key differences between predictive maintenance and condition monitoring—and why it mattersHow to build a scalable PdM roadmap by starting small, proving value, and selecting the right assetsCommon misconceptions and roadblocks that can derail projects before they begin—and how to avoid themWhy cross-functional collaboration and on-site insight make all the difference when launching a PdM initiativeYou can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    28 分
  • Scaling Smarter: How BlueScope Steel Took Senseye Predictive Maintenance Global

    Scaling Smarter: How BlueScope Steel Took Senseye Predictive Maintenance Global
    2025/05/21
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.Global Rollout Strategy: BlueScope Steel shares how they scaled predictive maintenance across multiple international sites using Senseye, starting with a successful pilot in Australia and expanding to New Zealand, Thailand, and the US.Strong Executive Backing: From day one, the predictive maintenance initiative was backed by full executive support, with the goal of driving asset intelligence and reducing unscheduled delays across all manufacturing plants.Hands-on Leadership & Local Empowerment: Chris Wonson highlights the importance of staying hands-on and embedding predictive maintenance into local teams through tailored training initiatives like “Senseye School.”Overcoming Data & Cultural Challenges: The team discusses how they tackled inconsistent data, different infrastructures, and varying plant cultures through collaboration, standardization, and iterative learning.Measurable Business Impact: Predictive maintenance has delivered clear benefits, including increased equipment uptime, reduced maintenance costs, and improved plant understanding—proving the value of the initiative and fueling momentum for global expansion.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    40 分
  • The CMMS Playbook: Lessons from 25+ Years in Maintenance Tech - with Greg Christensen

    The CMMS Playbook: Lessons from 25+ Years in Maintenance Tech - with Greg Christensen
    2025/05/15
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.This episode discusses:Industry Expert Insights: Greg, host of CMMS Radio, shares his 25+ years of experience in maintenance management, CMMS, and business development, offering valuable perspectives on industry evolution.The Role of CMMS in a Digital Age: Discussion on the continued relevance of CMMS amidst AI advancements, the importance of foundational maintenance practices, and the risks of over-reliance on technology without strong processes.Implementation Challenges & Best Practices: Key mistakes organizations make when deploying CMMS, including lack of buy-in, poor vendor selection, and failure to maintain engagement post-implementation.Future Trends in Maintenance Tech: Predictions on AI-driven CMMS improvements, the potential for voice-enabled CMMS interaction, and a renewed focus on simplifying maintenance operations for efficiency.Real Talk on Vendor & Client Relationships: Greg emphasizes the need for vendors to be honest about their capabilities, the importance of asking tough questions before selecting a solution, and how strong partnerships drive real business outcomes.CMMS Radio: CMMSradio Follow Greg on LinkedIn: https://www.linkedin.com/in/greg-c-7b97648/You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    41 分
  • IoT Time - with Peter Schopf

    IoT Time - with Peter Schopf
    2025/05/14
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.This episode discusses:🎙️ Understanding AI Agents vs Assistants: Peter and Niall break down the often-confused terminology around AI – from assistants and copilots to true agents – using a four-quadrant model based on automation and system integration.🧠 AI Intelligence & Modularity: The episode explores the concept of intelligence in AI, comparing large language models to parts of the human brain, and discussing the future of AI as modular, with specialized models for reasoning, simulation, and memory.🔄 Strategic vs Operational AI Use: Peter highlights the untapped potential of using AI for strategic applications like organizational planning and cultural transformation, in contrast to the more common operational use cases like summarizing emails or generating reports.📈 Driving Adoption Through Assistants: Practical advice is given on starting with AI assistants before jumping to complex agents. Peter shares a successful use case at E.ON where prompt-based assistants significantly improved employee engagement and productivity.👥 Training & Culture Are Key: Emphasis is placed on meaningful AI training (especially for managers), fostering a feedback culture, and viewing AI more like a working student than a calculator – adaptable, contextual, and collaborative.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    36 分
  • Setting your organization up for predictive maintenance success - with ISPE and Johnathan Bonner

    Setting your organization up for predictive maintenance success - with ISPE and Johnathan Bonner
    2025/05/08
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.This session was originally recorded for ISPE and their Technical Tuesday session.Watch the video here: Technical Tuesday: Boosting Pharmaceutical Efficiency Through Scalable Predictive Maintenance Find out more information here: Homepage | ISPE | International Society for Pharmaceutical Engineering
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    47 分
  • Beyond 1% - Industrial AI for a better future? - a panel discussion

    Beyond 1% - Industrial AI for a better future? - a panel discussion
    2025/05/07
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.In this episode, we discuss:Summary:How AI is transforming industries beyond hype: Discover real-world examples of AI’s impact in industrial settings—from predictive maintenance at enterprise scale to visual AI for infrastructure monitoring.The link between AI and sustainability: Learn how AI isn’t just a tool for efficiency, but a key enabler of sustainability, driving carbon and cost savings by extending asset life, reducing waste, and avoiding unnecessary inspections.The “Beyond 1%” philosophy: Explore how Australia can play a far greater global role in decarbonisation by leveraging digitalisation and AI—not just focusing on its own emissions footprint.AI’s double-edged environmental impact: Understand both the environmental costs of scaling AI (like energy use in data centers) and the innovations that are helping to mitigate them through responsible infrastructure and smarter operations.Why data and vision matter: See how tapping into underutilized assets like camera networks and drones enables smarter, scalable monitoring—helping companies extract insights without adding hardware complexity.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    50 分
  • Predictive maintenance: It's a way of life not a technology - with Richard Jeffers

    Predictive maintenance: It's a way of life not a technology - with Richard Jeffers
    2025/05/01
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.In this episode, we discuss:From Breweries to Maintenance Strategy: Richard Jeffers shares his fascinating career journey from running and maintaining breweries to leading technical and solutions teams—highlighting how military leadership shaped his approach to asset management.Why Maintenance Needs a Seat at the Strategy Table: Richard explains why maintenance professionals must understand their company's commercial strategy to align maintenance decisions with business goals and manage risk effectively.Technology is Cheaper—But Strategy Still Wins: While predictive maintenance and IoT tools are more accessible than ever, Richard stresses that the fundamentals of a good maintenance strategy haven’t changed. Technology is a tool, not a strategy.Balanced Maintenance, Not Buzzwords: He emphasizes that a successful approach blends multiple techniques—including run-to-fail and predictive methods—based on business context and asset criticality.Leadership Drives Culture Change: A clear maintenance vision, strong leadership, and cross-functional collaboration are critical to embedding effective maintenance strategies and embracing new technologies across an organization.
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    36 分
  • The Cultural Shift: Embracing Predictive Maintenance - with Brad Parsons

    The Cultural Shift: Embracing Predictive Maintenance - with Brad Parsons
    2025/04/30
    Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.In this episode, we discuss:The Cultural Key to Predictive Maintenance: Brad Parsons, CEO of Rely Ability, joins the podcast to explore why cultural readiness—not just technology—is crucial for successful predictive maintenance adoption.Understanding Organizational Culture: Brad breaks down four dominant workplace cultures—collaborate, control, compete, and create—and explains how each affects technology adoption and transformation.Leadership & Metrics Matter: Leadership alignment and shared performance metrics between teams (like maintenance and production) are essential to eliminate conflict and drive collaboration around new initiatives.Capturing and Sharing Successes: Real-world examples, like averting catastrophic equipment failures, demonstrate how sharing early wins internally can help build momentum and drive broader cultural adoption.Beyond the Buzzword – What PdM Really Means: Predictive maintenance is more than tech—it’s a systems-level change requiring data trust, behavior shifts, and readiness to act on insights across the business.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance
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    38 分