Splunk > Case Studies > Honda's Predictive Analytics Revolution: Boosting Profitability and Efficiency

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 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 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 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

The implementation of Splunk has brought about a transformative change in Honda's operations. The predictive capabilities of the software have enabled Honda to shift from a reactive to a proactive approach, significantly reducing the frequency of unknown incidents and midnight troubleshooting calls. The software has also facilitated cross-departmental collaboration, allowing teams to work together to solve issues faster. Moreover, the use of Splunk has led to a reduction in energy consumption and has freed up employees to focus on more strategic initiatives. In terms of safety, Splunk has been instrumental in tracking quality and upholding Honda's safety standards, ensuring the production of almost perfect products. The software has also been crucial in driving profitability by predicting equipment failure and enabling preemptive repairs, thereby saving significant costs associated with equipment downtime.

Quantitative Benefit

70% faster mean time to repair by expanding access to data
Increased profitability and efficiency through better uptime
Exceeded environmental standards by correlating machine, equipment, power, and IoT data