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Combining slash bundling with in-woods grinding operations

Combining slash bundling with in-woods grinding operations

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Time study data was collected to calculate hourly production (bone dry ton (BDT)/productive<br />

mach<strong>in</strong>e hour (PMH)) us<strong>in</strong>g standard time study techniques for each element <strong>in</strong> a mach<strong>in</strong>es<br />

operation cycle by stop watch (Olsen et al. 1998). A <strong>bundl<strong>in</strong>g</strong> cycle <strong>in</strong>cludes travel<strong>in</strong>g to the<br />

<strong>slash</strong> pile, followed by multiple grapples and sw<strong>in</strong>gs of <strong>slash</strong> to the <strong>in</strong>-feed table, and ended<br />

when the bundle produced was cut free from the mach<strong>in</strong>e. A load<strong>in</strong>g cycle started from travel<strong>in</strong>g<br />

to a bundle, followed by sw<strong>in</strong>g<strong>in</strong>g empty to the bundle, grappl<strong>in</strong>g the bundle, sw<strong>in</strong>g<strong>in</strong>g loaded<br />

back to the conta<strong>in</strong>er, and ended by compact<strong>in</strong>g bundle <strong>in</strong>to the conta<strong>in</strong>er. A hook-lift truck<strong>in</strong>g<br />

cycle <strong>in</strong>cludes travel<strong>in</strong>g empty to the harvest unit, position<strong>in</strong>g the truck for load<strong>in</strong>g, load<strong>in</strong>g a<br />

conta<strong>in</strong>er filled <strong>with</strong> bundles, travel<strong>in</strong>g loaded to the centralized gr<strong>in</strong>d<strong>in</strong>g site, and<br />

unload<strong>in</strong>g/dump<strong>in</strong>g the bundles. Three types of delay time, <strong>in</strong>clud<strong>in</strong>g operational delays,<br />

mechanical delays and personal delays, were recorded.<br />

Regression models for delay-free cycle time were developed us<strong>in</strong>g the pre-identified <strong>in</strong>dependent<br />

variables associated <strong>with</strong> each cycle. The collected time study data were screened for normality<br />

and outliers us<strong>in</strong>g histograms and residual plots, and were used to develop predictive equations<br />

by runn<strong>in</strong>g multiple regressions us<strong>in</strong>g ord<strong>in</strong>ary least squares estimators, performed <strong>in</strong> R 2.4.1<br />

statistical software program (R 2006). The f<strong>in</strong>al predictive models only <strong>in</strong>clude variables that<br />

were statistically significant (p-value

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