Suppliers United States MachineMetrics
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MachineMetrics

Manufacture with Confidence
United States
2009
Private
< $10m
11 - 50
Open website

MachineMetrics is the authority on real-time manufacturing analytics. When we started MachineMetrics, we identified undeniable gaps in current manufacturing processes: a lack of real-time production visibility and a lack of communication between machine operators and their managers. Roughly 50% of US companies admitting to not having a systematic roadmap toward Digital Manufacturing solutions nor automation and over 90% of companies have yet to attempt to integrate solutions to date. Many companies have attempted to develop solutions for this problem but it had yet to be truly solved. We spoke with 100+ manufacturers to gather their feedback on why these solutions did not fit their needs and how it could be designed better.

MachineMetrics was thus created; an IIoT manufacturing analytics and machine monitoring platform to help companies visualize where they stand now and overcome the challenges along the Digital Transformation journey to advance forward from reactivity, to proactivity, and to predictivity. Our fully automated machine monitoring solution provides 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.

We've simplified IoT for the shop floor and are empowering manufacturers to develop their roadmap toward manufacturing excellence with the easiest to use and simplest to integrate software on the market. MachineMetrics is driving manufacturing efficiency by more than 20% on average for our customers and we are proud to be developing the future of manufacturing alongside them.

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We collect data from machine controls and machine operators for the entire production floor and use this data to monitor machine conditions (faults, status, tool utilization), production (OEE & Machine Utilization), work-order status, quality Tracking and downtime reasons that can be auto classified or indicated by the operator with touch screen tablet interfaces mounted at the machine tool. Real-Time Dashboards are 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).

MachineMetrics provides robust and superior reporting features including OEE reports, job reports, downtime and quality pareto reports that can be compared by shift, machine, and when viewing individual job/part reports. Utilization, TEEP and OOE are also measured and visible in historical reports. The real time and historical data collected allows managers to not only track efficiency and quickly identify production bottlenecks that are related to specific machining operations but also helps measure the effect of process improvements.

MachineMetrics' mobile friendly, secure cloud application allows you to access this data from anywhere and requires no servers, automatic updates, and minimal IT expenses. All that's required is that machines are networked and accessible from our gateway device. Our highly adaptable system can be customized to fit any manufacturer's specific production needs.

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Fastenal, Carolina Precision Manufacturing, Carlson Products, Praxair, Benchmade

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MachineMetrics is a provider of Industrial IoT platform as a service (paas), application infrastructure and middleware, analytics and modeling, and functional applications technologies, and also active in the buildings, equipment and machinery, and mining industries.
Technologies
Platform as a Service (PaaS)
Connectivity Platforms
Data Management Platforms
Device Management Platforms
Analytics & Modeling
Predictive Analytics
Real Time Analytics
Application Infrastructure & Middleware
Data Visualization
Functional Applications
Enterprise Asset Management Systems (EAM)
Use Cases
Machine Condition Monitoring
Predictive Maintenance
Functions
Discrete Manufacturing
Industries
Buildings
Equipment & Machinery
Mining
MachineMetrics’s Technology Stack maps MachineMetrics’s participation in the platform as a service (paas), application infrastructure and middleware, analytics and modeling, and functional applications IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies3
Fastenal Builds the Future of Manufacturing with MachineMetrics
Fastenal Builds the Future of Manufacturing with MachineMetrics
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.
MachineMetrics helped Carolina Precision saved over $1.5M on machine monitoring
MachineMetrics helped Carolina Precision saved over $1.5M on machine monitoring
Gary Bruner, the president of Carolina Precision Manufacturing, a contract manufacturer that specializes in small-diameter, close-tolerance CNC Swiss turned parts, had a problem. Over breakfast that morning, Gary had logged onto the MachineMetrics monitoring system on his laptop at home to check the status of his machines assigned to a lights-out operation, but saw immediately that two of his machines were not in production. “What’s wrong with machines 35 and 36?” he thought to himself. Upon arrival to the shop, Gary learned from his operator on duty that this type of hold up was nothing out of the ordinary, and was in fact a product of inefficient startup procedures that had simply never been analyzed or augmented previously. In an industry with razor thin margins, Gary understood that the keys to growth and success were in efficiency and quality. He understood the importance of keeping tabs on production stats, job status, uptime, and setup. However, there was no way to know how well machines were doing in real time. What was causing this additional downtime? Furthermore, CPM’s current methods of measurement and data collection were not only time consuming, but had quickly becoming outdated. Historically, CPM had an employee dedicated to the collection of utilization data. This employee would walk around to each of the machines, collect scrap tickets post-production, talk to operators, and record yesterday’s data into their current ERP system; not to mention that this manual data collection was prone to errors, and would take upwards of 2 hours per day. Without the ability to visualize their results, the recorded data was not very actionable.
MachineMetrics Helped Carlson Products to Increase Efficiency by over 20%
MachineMetrics Helped Carlson Products to Increase Efficiency by over 20%
There’s always a sort of lag in the information we get from the ERP. It’s not real time, its not updated every second. We were able to see what happened yesterday, what happened last week but not what was happening right now, and we would always have to somehow fill that gap. Also, to get data into the ERP we were relying on a human being to accurately define and manually input the information to enter it into our database. All of the real-time monitoring systems that Carlson Products had seen were only dedicated to machining. They had chats with some of their equipment providers and the solutions they attempted to provide them with were not visually appealing, not web-based and felt quite rudimentary. They did attempt to do manual tracking before MachineMetrics, and they got so much junk data that they just stopped trying. They kept a Microsoft access database where employees would track this information themselves but with all the huge outliers, data it went to hell and a handbasket. The most important thing Carlson Products wanted was to look at was historical labor data information: whether or not the jobs were on target. Tracking down the supervisors and operators to understand why they weren’t on target was quite time consuming, not to mention most of their employees had difficulty remembering why problems had occurred when reviewing the issues reactively. There was no ability to identify systematic issues vs singular issues. Also, the lack of historical data made it difficult for them to calculate their utilization as well as their potential capacity.
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