MachineMetrics > Case Studies > Fastenal Builds the Future of Manufacturing with MachineMetrics

Fastenal Builds the Future of Manufacturing with MachineMetrics

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 Fastenal Builds the Future of Manufacturing with MachineMetrics - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Real Time Analytics
  • Application Infrastructure & Middleware - Data Visualization
  • Functional Applications - Enterprise Asset Management Systems (EAM)
Applicable Industries
  • Mining
Applicable Functions
  • Discrete Manufacturing
Use Cases
  • Machine Condition Monitoring
The Customer
About The Customer
Fastenal is an American industrial supply company based in Winona, Minnesota. They provide companies with the fasteners, tools, and supplies needed to manufacture products, build structures, protect personnel, and maintain facilities and equipment.
The Challenge

Fastenal's objective was to better understand their machine downtime, utilization, quality issues, and to embrace cutting-edge manufacturing technology/process improvement capabilities to bring their team to the next level. However, there was a lack of real-time data, visualization, and actionable insights made this transition impossible.

The Solution

MachineMetrics was the solution. The fully automated machine monitoring and manufacturing analytics solution provided visualizations of real-time manufacturing production data, instant notifications, as well as historical analytics, allowing factory workers to make faster, smarter, more confident decisions based on real-time data. Data was collected from machine controls and machine operators for the entire production floor and then used to monitor machine conditions (faults, status, tool utilization), production (OEE & Machine Utilization), work-order status, quality tracking and downtime reasons that were auto classified or indicated by the operator via the touch screens. Real-Time Dashboards were also mounted on the production floor to provide an at-a-glance indication if jobs are performing at or below expectations (against Parts Goal or OEE metrics). The real-time and historical data collected allowed managers to not only track efficiency and quickly identify production bottlenecks that are related to specific machining operations but also helped them measure the effect of process improvements.

Data Collected
Asset Status Tracking, Downtime, Fault Detection, Machine Performance, Machine Utilization Rate
Operational Impact
  • [Efficiency Improvement - Asset Utilization]
    Increase in machine utilization.
  • [Efficiency Improvement - Productivity]
    Improved workforce productivity.
  • [Data Management - Data Transparency]
    Data transparency and reliability.
Quantitative Benefit
  • Achieved ROI in 25 days.

  • Experienced a 20% increase in machine utilization.

  • Produced 150,000 more parts than the previous year.

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