In lean manufacturing, machine operator efficiency (MOE) is the performance of an employee who operates industrial machinery. The operator's efficiency is measured as the time spent producing product divided by the time the operator is on duty. For example: if an operator is assigned to run a CNC machine tool for seven hours, but they only have four hours' worth of continuous uninterrupted output of workpieces—their MOE rating is 57% (4 divided by 7) for this seven-hour period of time. There is a similar lean manufacturing KPI called overall equipment effectiveness (OEE). The major difference between OEE and MOE is that the OEE rating is on the machine and the MOE is on the person. MOE is a measure of operator performance only, regardless of the type of machine or the speed of the machine they are working on. MOE only measures the operator's ability to keep the machine running continuously (load and unload parts faster that the automatic cycle time of the machine). The MOE rating of an operator will travel with them as they moves from one machine to another. This is easily accomplished because the MOE rating is a universal calculation of time and not reliant on the complex OEE calculations of loading, availability, performance, and quality. Industrial dashboards can be used to display statistics for MOE ratings on each operator. To boost overall profitability for a manufacturing plant, machine operators are sometimes compensated with a salary bonus based on their MOE ratings. (Wikipedia).
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From playlist Machine Learning
Mechanical Engineering: Ch 13: Virtual Work Applications (8 of 39) What is Mechanical Efficiency? 1
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain, define, and calculate (u=0) what is mechanical efficiency, where friction equal 0. Next video in this series can be seen at: https://youtu.be/5xV8gzvHZGs
From playlist MECHANICAL ENGINEERING 13 VIRTUAL WORK
Introduction To Machine Learning | Machine Learning Basics for Beginners | ML Basics | Simplilearn
Machine Learning is a trending topic nowadays. This Introduction to Machine Learning video will help you to understand what is Machine Learning, importance of Machine Learning, advantages and disadvantages of Machine Learning, what are the types of Machine Learning - supervised, unsupervis
Machine learning's next evolution: Machine teaching
Microsoft researchers are at the forefront of machine teaching: an effort to help people without a machine learning background teach their systems to get better based on experience. Learn more about machine teaching: http://blogs.microsoft.com/next/?p=54083
From playlist Machine Learning
Power and Efficiency | Dynamics
https://goo.gl/1L3j2W for more FREE video tutorials covering Engineering Mechanics (Statics & Dynamics) The objectives of this video tutorial are to introduce the concept of power & efficiency followed by completing an exemplary problem using the knowledge of power concepts. At first, the
From playlist SpoonFeedMe: Engineering Mechanics (Statics & Dynamics)
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Automation
Simple Machines (1 of 7) Pulleys; Defining Forces, Distances and MA
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From playlist Mechanics
Look out: Robots could soon teach each other new tricks
Learning method could help them do chores or disarm bombs. Learn more: http://scim.ag/2q2LBHz
From playlist Robots, AI, and human-machine interfaces
Support Vector Machine Optimization - Practical Machine Learning Tutorial with Python p.24
In this tutorial, we discuss the optimization problem that is the Support Vector Machine, as well as how we intend to solve it ourselves. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
From playlist Machine Learning with Python
Tim Harris: "Systems Challenges in Graph Analytics"
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From playlist Turing Lectures
Stanford Seminar - Accelerating ML Recommendation with over a Thousand RISC-V/Tensor Processors...
Dave Ditzel is the founder and executive Chairman of Esperanto Technologies Inc. This talk was given on March 2, 2022. TAccelerating ML Recommendation with over a Thousand RISC-V/Tensor Processors on a 7nm Chip To accelerate Machine Learning Recommendation and other workloads, Esperant
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
DDPS | Towards automatic architecture design for emerging machine learning tasks | Misha Khodak
Hand-designed neural networks have played a major role in accelerating progress in traditional areas of machine learning such as computer vision, but designing neural networks for other domains remains a challenge. Successfully transferring existing architectures to applications such as se
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
November 1, 2006 lecture by William Dally for the Stanford University Computer Systems Colloquium (EE 380). A discussion about the exploration of parallelism and locality with examples drawn from the Imagine and Merrimac projects and from three generations of stream programming systems.
From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
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From playlist Classical and Quantum Transport Processes : Current State and Future Directions (ONLINE)2022
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From playlist DevOps Training Videos
Stanford Seminar: HPC Opportunities in Deep Learning - Greg Diamos, Baidu
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From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
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From playlist Engineering MAE 91. Intro to Thermodynamics
The Magic of Computer Virtualization | TDNC Podcast #123
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From playlist Tech Podcasts | This Does Not Commute
Machine Learning Certificate from Cornell University
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From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
Nanoscale thermal machines: from classical kinetics to quantum coherences by Dvira Segal
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From playlist Classical and Quantum Transport Processes : Current State and Future Directions (ONLINE)2022