Upon completion of this course, participants will be able to:

• Identify high-value IoT use cases across different sectors, particularly Industrial IoT (IIoT), supply chain, and smart cities.
• Understand the core architectural components of an IoT solution (Device, Network, Platform, Application).
• Develop a strategy for moving computation from the cloud to the **Edge** (Edge Computing) to reduce latency and bandwidth use.
• Manage the unique security, data privacy, and compliance risks associated with massive fleets of connected devices.
• Analyze the financial benefits of predictive maintenance, asset tracking, and remote monitoring applications.
• Formulate a scalable data ingestion and analytics strategy for high-velocity, high-volume sensor data.
• Build a business case for a connected operations initiative, focusing on clear and measurable ROI.

• Operations and Plant Managers
• Supply Chain and Logistics Executives
• IT and Network Infrastructure Directors
• Digital Product Managers for connected devices

The methodology focuses on technical and operational strategic planning. **Scenarios** involve designing a security architecture for a fleet of remote, unmanned assets (e.g., wind turbines or oil pumps). **Case studies** analyze successful IIoT implementations in manufacturing (e.g., condition monitoring) and logistics (e.g., cold chain monitoring). **Group activities** focus on mapping the full data journey—from sensor to decision—for a chosen supply chain process. **Individual exercises** require participants to calculate the potential cost savings of shifting from reactive to predictive maintenance for a major asset. **Syndicate discussions** debate the regulatory and ethical challenges of using IoT data for worker performance monitoring.

UNIT 1: The Strategic Landscape of IoT

• Defining IoT, IIoT (Industrial IoT), and the transition to connected operations
• Analyzing high-impact use cases: Predictive Maintenance, Fleet Management, and Asset Tracking
• The economic case: Moving from reactive repair to proactive, data-driven optimization
• The role of IoT in creating new service-based business models (Product-as-a-Service)

UNIT 2: IoT Architecture and Edge Computing

• Understanding the layers of the IoT stack: Sensors/Actuators, Gateways, and Cloud Platforms
• The strategic need for **Edge Computing**: Processing data locally for low-latency decisions
• Data ingestion pipelines: Handling high-velocity, time-series sensor data at scale
• Integrating IoT data streams with existing ERP, CRM, and core business systems

UNIT 3: Security, Privacy, and Device Management

• The unique security challenges of IoT (device identity, network segmentation, physical tampering)
• Strategies for secure over-the-air (OTA) device provisioning and firmware updates
• Data privacy and compliance in sensor data collection (e.g., location tracking, personal data)
• Developing a robust governance framework for device lifecycle management

UNIT 4: Analytics and Business Intelligence

• Applying Machine Learning and AI models to sensor data for anomaly detection
• Predictive Maintenance: Using time-series data to forecast equipment failure and optimize schedules
• Digital Twins: Creating virtual models of physical assets for simulation and optimization
• Data visualization techniques optimized for operational and real-time monitoring dashboards

UNIT 5: Implementation and Scaling

• Developing a phased IoT implementation roadmap with clear, measurable milestones
• Building the internal talent capabilities (e.g., OT/IT convergence) required for IIoT
• Evaluating vendor ecosystems and navigating the buy vs. build decision for platforms
• Calculating the business case and ROI for large-scale connected operations initiatives