Production System Performance

2010 ◽  
pp. 255-275
Keyword(s):  
Production System ◽  
System Performance

Improving Production System Performance Using Overall Equipment Effectiveness

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Lance Clarence ◽  
Wan Muhammad Noor Sarbani Mat Daud
Keyword(s):  
Production System ◽  
System Performance ◽  
Intelligent System ◽  
Global Market ◽  
Equipment Failure ◽  
Intuitive Knowledge ◽  
Analysis Tools ◽  
New Process ◽  
Knowledge Delivery

In the competition among organization on the global market, no organization will tolerate losses. Overall Equipment Effectiveness (OEE) overall is a new process in which the efficiency of a system is calculated and complicated manufacturing issues are truly simplified to simple and intuitive knowledge delivery. It thinks about the exceptionally important measures of productivity. An endeavour has been done to measure and analyse existing Overall Equipment Effectiveness (OEE) at company Kirino in hope to reduce unplanned downtime losses on equipment failure and tooling damage to maximize the productivity. The methods used to analyse these various causes were analysis tools and Intelligence Systems. After knowing the causes of various activities that leads to high rates of defects, then recommendations for improvements that could be used by company Kirino were ready to be made using intelligent system as a medium of solution

scholarly journals Production System Performance Identification Using Sensor Data

2018 ◽  
Vol 48 (2) ◽  
pp. 255-264 ◽  
Author(s):  
Jing Zou ◽  
Qing Chang ◽  
Yong Lei ◽  
Jorge Arinez
Keyword(s):  
Production System ◽  
System Performance ◽  
Sensor Data

Improving Production System Performance Using Overall Equipment Effectiveness

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Lance Clarence ◽  
Wan Muhammad Noor Sarbani Mat Daud
Keyword(s):  
Production System ◽  
System Performance ◽  
Intelligent System ◽  
Global Market ◽  
Equipment Failure ◽  
Intuitive Knowledge ◽  
Analysis Tools ◽  
New Process ◽  
Knowledge Delivery

In the competition among organization on the global market, no organization will tolerate losses. Overall Equipment Effectiveness (OEE) overall is a new process in which the efficiency of a system is calculated and complicated manufacturing issues are truly simplified to simple and intuitive knowledge delivery. It thinks about the exceptionally important measures of productivity. An endeavour has been done to measure and analyse existing Overall Equipment Effectiveness (OEE) at company Kirino in hope to reduce unplanned downtime losses on equipment failure and tooling damage to maximize the productivity. The methods used to analyse these various causes were analysis tools and Intelligence Systems. After knowing the causes of various activities that leads to high rates of defects, then recommendations for improvements that could be used by company Kirino were ready to be made using intelligent system as a medium of solution

scholarly journals Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems

2017 ◽  
Vol 114 (5) ◽  
pp. 926-931 ◽  
Author(s):  
Alexandra N. Kravchenko ◽  
Sieglinde S. Snapp ◽  
G. Philip Robertson
Keyword(s):  
Production System ◽  
System Performance ◽  
Management Practices ◽  
New Technologies ◽  
Cropping Systems ◽  
Block Design ◽  
Field Scale ◽  
Conventional Management ◽  
Management Interventions ◽  
Yield Gaps

Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based–organic, management practices for a corn–soybean–wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot- and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.

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