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Honda's Predictive Analytics Revolution: Boosting Profitability and Efficiency
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Electrical Grids
- Equipment & Machinery
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Onsite Human Safety Management
- Time Sensitive Networking
Services
- Data Science Services
- Testing & Certification
The Challenge
Honda Manufacturing of Alabama (HMA), the largest light truck production facility of Honda, was facing a significant challenge in terms of data utilization. Despite generating a vast amount of data from the assembly floor, the plant lacked the ability to leverage this data for insights into parts, equipment, and machines. This lack of visibility forced the team to adopt a reactive approach to troubleshooting, which was inefficient and often led to machine failure or interruptions in the production line. The inability to predict and proactively address issues was hindering the plant's efficiency, safety, and profitability.
The Customer
Honda Alabama
About The Customer
Honda Manufacturing of Alabama (HMA) is Honda's largest light truck production facility in the world. It is the sole producer of Honda's Passport SUV, Odyssey minivan, Pilot SUV, Ridgeline truck, and the V-6 engines that power them. The factory employs over 4,500 employees who work with a complex fleet of machinery to assemble cars from hood to hubcap. This includes building frames, painting car bodies, and intricately placing thousands of parts within each vehicle. The facility is known for its meticulous production process, which generates a significant amount of data.
The Solution
To overcome these challenges, Honda of Alabama turned to Splunk, a software platform that uses machine learning, IoT, and predictive analytics to turn data into actionable insights. Splunk was integrated across the factory, enabling Honda to proactively identify and solve problems before they escalated. The software's predictive capabilities transformed the plant's approach to problem-solving and innovation. For instance, Splunk's machine learning technology was used to predict and monitor equipment temperature when burning paint fumes, ensuring compliance with environmental standards and preventing shutdowns. Furthermore, visualized metrics and contextual event insights facilitated more collaborative problem-solving, significantly reducing the mean time to repair (MTTR).
Operational Impact
Quantitative Benefit
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