Agile Predictive Monitoring: Lean Operations with AI & IoT

Examines the common pitfalls in digital transformation, such as automating flawed processes (the "Technology Trap").

Analyzes the factors contributing to the 70% global failure rate of digital projects and discusses the impact of cultural resistance and leadership misalignment.

Explores the economic mechanics behind the Artificial Intelligence of Things (AIoT).

Breaks down the layers of IoT architecture and clarifies the distinct categories of AI , emphasizing practical mathematical applications over generalized concepts.

Explains the "Digital Retrofit" methodology for integrating modern intelligence into legacy industrial machinery. Uses practical analogies to illustrate how external monitoring systems can be securely overlaid onto existing, fragile PLCs without disrupting core operations.

Details the total cost of ownership (TCO) associated with building internal hardware and software tools.

Analyzes hidden expenditures such as ongoing maintenance, security updates, and the impact of employee turnover on proprietary systems.

Introduces the Agile Predictive Monitoring (AgPM) framework and its alignment with Lean and Agile methodologies.

Discusses how AgPM functions as a "Digital Andon Cord" for waste identification and contrasts iterative Agile deployments with rigid "Waterfall" planning structures.

Compares the operational and financial differences between wired and wireless sensor deployments.

Analyzes the "Cabling Tax," demonstrating how conduit materials and specialized labor can significantly exceed the cost of the sensor hardware itself.

Details the design of hybrid network architectures, specifically combining LoRaWAN for low-bandwidth data and 5G/LTE for high-bandwidth data.

Compares technical specifications such as range, power consumption, and data throughput across different wireless protocols.

Provides a practical demonstration of deploying a wireless IoT sensor.

Reviews installation processes across different environments, including HVAC systems, SCADA architectures, and LoRaWAN networks, illustrating the timeline from physical installation to data transmission.

Examines the Unified Namespace (UNS) architecture as a solution to fragmented data silos.

Discusses how UNS acts as a centralized data broker , resolving discrepancies between ERPs, SCADA systems, and AI models to establish a single source of truth

Analyzes the impact of data hygiene on the accuracy of AI predictive models. Identifies common sources of data corruption, such as manual entry errors and data decay, and outlines preprocessing best practices required before data enters the AI layer.

Details the technical implementation of MQTT brokers within enterprise networks.

Explains the publisher/subscriber model, "Report by Exception" logic for bandwidth optimization, and methods for contextualizing data payloads for AI consumption.

Defines the architecture of Agentic AI and its distinction from traditional rule-based automation (RPA).

Breaks down the autonomous loop (Perceive, Reason/Plan, Act, Adapt) that enables AI agents to handle operational ambiguities and execute complex tasks.

Demonstrates the construction of an AI Maintenance Agent.

Analyzes how multi-agent groups (e.g., Data, Algorithm, Planning, and Learning agents) process inputs like vibration data to generate health scores, predictive alerts, and optimized maintenance schedules.

Examines the application of AI agents in logistics and fleet management.

Illustrates how an agent integrates diverse data sources—such as live traffic feeds, weather data, and vehicle status—to perform dynamic rerouting and resource allocation.

Reviews the deployment of an Energy Audit Agent for continuous facility monitoring.

Explains how the agent processes data from smart meters and BMS systems to identify anomalies, optimize HVAC/lighting setpoints, and automate carbon footprint reporting

Analyzes the five core pillars of AgPM: Flexibility, User Centricity, Scalability, Adaptability, and Inclusivity.

Details how the framework supports multiple simultaneous use cases and integrates with legacy systems using a hardware-agnostic approach.

Connects AgPM to traditional Lean manufacturing principles.

Explains how sensor and AI data function as a "Digital Andon Cord," enabling operational teams to map value streams, identify bottlenecks, and eliminate waste (Muda) proactively.

Discusses the application of Agile methodologies to technical operations.

Contrasts the limitations of 12-month "Waterfall" planning with the benefits of short iteration cycles, demonstrating how continuous feedback loops improve response times to operational changes.

Analyzes the varying objectives of stakeholders involved in digital projects: the Buyer (executives focused on ROI), the Builder (engineers focused on interoperability), and the Seller (advocates focused on strategic alignment).

Details criteria for evaluating systems, such as Time to Value (TTV) and edge computing capabilities.

Examines stakeholder communication strategies for technical projects.

Discusses methods for translating engineering metrics into business outcomes and addresses the "Shiny Toy" problem by demonstrating practical utility to floor-level maintenance teams.

Provides a framework for calculating the Return on Investment (ROI) of AgPM systems.

Identifies commonly overlooked cost-saving metrics, such as avoided quality defects, optimized spare parts inventory, and reduced preventative maintenance labor hours.

Analyzes a real-world case study of a legacy plastics manufacturing facility.

Reviews how the deployment of retrofit sensors and AI monitoring on steam and electrical infrastructure resulted in reduction in production interruptions and a 7-9 month payback period.

Examines a case study on implementing wireless IoT in a large-scale airport facility.

Details the progression from monitoring a single system (wastewater pumps) to scaling the architecture for smart metering, passenger comfort, and safety compliance across a heavily regulated environment.

Guides students through a final synthesis project.

Outlines the steps required to develop a comprehensive digital transformation roadmap, incorporating the concepts of data architecture, Agentic AI, Lean integration, and ROI calculation covered throughout the course.