A Study Of Changes In The Logging System And Labor Productivity With Thepromotion Of Mechanization In Forestry PDF Download

Globally the forest harvesting industry is becoming increasingly mechanized. Driving this trend is the desire to increase productivity and reduce cost, as well as to improve labor-related issues. With mechanization comes an in-forest platform for the introduction of state-of-the-art communication and measurement technologies, and powerful on-board computers. These systems have the potential to increase efficiency and value gain from the whole forestry supply chain. However the performance to-date of mechanical harvesting systems has not lived up to their full potential, particularly with respect to value recovery. One of the potential reasons for poor value recovery performance is the level of accuracy of stem diameter and length measurements on harvesters. Numerous studies have looked at the level of error in both the diameter and length measurements made by mechanical harvester/processors; however, few have looked at the economic impacts of these errors. The modeling work done in this dissertation showed that for the operations studied the value loss was between 3% and 23% due to measurement errors. Further analysis showed that increasing the precision of the length and diameter measurements would provide gains from reducing the measurement error rates. One potential way of reducing the error rates is to introduce new scanning and forecasting procedures that would maintain or improve net value recovery. Five procedures were evaluated. It was shown that there was no economic advantage in partially scanning the stem. Breakeven capital investment costs were calculated for new scanning, forecasting, and optimization equipment. They ranged between zero and US$2,120,000 depending on tree species, markets, scanning speed, volume scaling rules, and scanning procedure. Even with perfect information about the stem, the computer that controls the bucking solution still requires correct cutting instructions. These instructions are needed to obtain the optimal output log distribution that will maximize the return to the log suppliers while still meeting market and operational constraints. New algorithms were developed for efficiently planning and implementing these cutting instructions. This dissertation demonstrated that the optimal output log distribution can be affected by measurement errors, work methods and bucking procedures.