Episode 26 of The Gunn Show
In episode 26 of The Gunn Show, Greg McHale joins Tony and helps us all understand what “Production Intelligence” really means. Greg explains where machine data can really help us improve our overall production, up time, and all the hidden benefits we may not calculate on a daily basis.
“We came at software development from the outside, with no view as to what software has done from a data collection standpoint for the manufacturing industry in the past. We didn’t carry the luggage of how complicated and cumbersome data collection needed to be. We came at it from an angle of solving problems and shortcomings. No operator input is a major element of our approach to the problem.
Every manufacturing president I talked to said ‘if you give my operator one more thing to do that doesn’t involve running the machine, I’m going to strangle you.’ I said, ‘OK, we won’t do that!” Not carrying the baggage of previous assumptions can evolve into an incredible product experience, and one that is much different from what people are used to in the industry. That philosophy is where our success is coming from.“
—Greg McHale, CTO and co-founder, Datanomix
In the interview, Greg talked Tony through the three steps of how Datanomix Automated Production Intelligence works:
- When Datanomix connects to a machine controller, we know exactly what part number is being manufactured. The software builds up performance standards tied to a combination of the part number and the machine. Those standards can be human driven, robotically driven, or palletized.
- We then look at fully burdened cycled times—your classic button-to-button time. This could include human touch time: blowing off chips, load and unload. Or it could include the cutting time. If robotically fed, the cycle time will be robot turnaround time. And on pallets, that’s going to be unique to which pallet is in operation. Again, our software keeps all that data tied to the part number and machine combination.
- Under all the different applications and scenarios, the software—with no effort from the operator—works out patterns for each part number to understand:
- What’s a good level of performance?
- What’s the real level of performance?
- How does the performance affect cost?
- How does the machine run a specific part number?
- What are my greatest areas of opportunity?
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