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

Honda's Predictive Analytics Revolution: Boosting Profitability and Efficiency

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 Honda's Predictive Analytics Revolution: Boosting Profitability and Efficiency - IoT ONE Case Study
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
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
  • 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

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