Ford Motor Company is modernizing its global manufacturing network by unifying industrial data across plants, machines, and IT systems. To support AI-driven operations and predictive maintenance, Ford needed a standardized way to collect, organize, and govern real-time data from thousands of machines per plant across dozens of sites. The company selected HiveMQ as the backbone of its enterprise-wide Industrial IoT (IIoT) monitoring solution, enabling consistent visibility, rapid decision-making, and the foundation for intelligent manufacturing operations.
Modernizing a Fragmented Manufacturing Data Landscape
Ford’s legacy data infrastructure posed significant challenges including high operational costs, limited real-time data availability, and a distributed architecture that hindered scalability across their many operational sites. Each facility was structured differently, making it difficult to deploy global digital initiatives or feed enterprise AI models with the operational data they needed.
With limited visibility into machine health, maintenance was complex and resource-intensive, requiring costly onsite personnel to diagnose issues and often leading to unnecessary downtime. The fragmented architecture impeded efficient data utilization and timely decision-making needed to improve maintenance schedules, increase efficiency, and reduce costs.
Ford needed to modernize the way operational data was streamed to lay the groundwork for advanced analytics and AI use cases and activate intelligent operations. They set out to build an architecture that would reliably stream operational data with a real-time, standardized, and secure architecture that could operate 24/7 at global scale.
Establishing a Unified, Real-Time IIoT Data Backbone
Ford adopted HiveMQ at the core of its data architecture to establish a centralized Industrial IoT (IIoT) monitoring application. The HiveMQ Enterprise Platform enabled real-time, event-driven data streaming from various plant floor assets into a central platform that spanned its global manufacturing network.
“HiveMQ facilitates real-time communication, allowing for immediate fault detection, calculation of key performance indicators (KPIs), and instantaneous alert generation directly back to the plant floor.” said Gopalakrishnan Rajaram, Solution Architect - Industrial Systems at Ford. “This centralized approach streamlines data flow and operational oversight.”
The architecture ensures high availability and consistent governance across all facilities. This unified real-time data layer allows Ford to operationalize AI, scale predictive maintenance, and roll out new digital initiatives seamlessly across the business.
Transforming Global Operations with an AI-Ready Architecture
Ford created a repeatable, AI-ready architecture that is transforming how manufacturing teams work. The implementation yielded substantial improvements across several key areas - most notably significant cost savings.
“We achieved huge cost savings and improved operational efficiency by moving away from a distributed infrastructure,” said Gopal. “With HiveMQ we significantly reduced support and maintenance expenditures and enhanced operational productivity with the ability to address faults immediately, minimizing downtime.”
Ford achieved centralized monitoring and configuration, leading to simplified management processes. The solution's design allows for easy scalability to multiple plants, facilitating consistent data analysis and predictive insights across the enterprise. It also enables performance benchmarking across facilities, driving continuous improvement and alignment to best practices.
“The comprehensive data gathered through our HiveMQ IIoT monitoring system provides a fertile ground for developing and deploying a multitude of AI-driven use cases, leading to enhanced decision-making and operational efficiencies,” said Gopal.
Detailed Ford use cases powered by HiveMQ include:
Predictive Maintenance: Using real-time data to anticipate equipment failures and schedule maintenance proactively, minimizing downtime.
Quality Control: Real-time monitoring of production parameters to ensure consistent product quality and identify deviations promptly.
Asset Utilization Optimization: Gaining insights into asset performance and usage patterns to maximize operational efficiency and return on investment.
Energy Management: Monitoring energy consumption enterprise-wide to identify inefficiencies and optimize usage.
Supply Chain Visibility: Integrating IIoT data with supply chain operations for real-time tracking of materials and finished goods.
As Ford continues to expand its centralized HiveMQ architecture to several brownfield sites over the coming years, they are achieving scalable plant-to-enterprise visibility and a consistent operational intelligence layer. They are shifting from preventative to predictive maintenance, leading to reduced unplanned downtime.
Most importantly, they’ve built a standardized foundation that allows for faster deployment of new analytics and AI use cases. HiveMQ has become the foundation for Ford’s data-driven manufacturing transformation - enabling AI, improving operational reliability, and accelerating digital scalability.
