Using advanced analysis techniques to benchmark forest harvesting systems : a study of the New Zealand forest industry.

The concept of benchmarking is applied to businesses and industries for the continuous measurement and improvement of production systems and organizational performance. This makes it important to continuously measure and improve the operational performance in order for any industry to maintain its local and global competitiveness in the ever-changing global business environment. Data analysis techniques has continued to develop allowing a greater level of in-depth analysis of operational data, an example being data envelopment analysis (DEA), a frontier analysis method established in non-parametric framework. New Zealand has a large forest industry with about 1.7 million net stocked plantation forest area, 30.7 million m3 of harvested timber and $5.47 billion in value of export forest products. The New Zealand forest harvesting sector has an existing benchmarking system containing cost and productivity data with over 1000 unique entries on contracted forest harvesting operations in New Zealand from 2009-2015. This thesis shows that advanced operations techniques can be used to analyse the forest harvesting sector by measuring the relative harvesting efficiency of independent logging contractors; identifying external factors that influence the technical efficiency of forest harvesting operations; and including the operating environment factors in the evaluation process of harvesting operations performance. DEA, a non-parametric frontier benchmarking technique is applied in the analyses. Using DEA on the existing benchmarking database, the relative operational efficiency of independent logging contractors was estimated. Five inputs, which accounted for about 77% variation in the harvesting productivity (output), were used to develop the DEA production models. Output-orientation under the assumption of constant and variable returns to scale were used to estimate the relative aggregate, pure technical and scale efficiencies, and the measure of excessive use of inputs by the contractors. Optimal input usage and output targets were estimated under variable returns to scale for the inefficient contractors to move to the efficient frontier. The results indicate that the majority of logging contractors operated at or near scale efficient level while the main source of inefficiency in the industry is both technical and managerial. Analysis shows that if all inefficient contractors operate at the optimal input and output levels, and were provided with stand and terrain conditions that best suited their operations, on average, system productivity could increase by 45% from 28.7 to 52.2 tons/SMH. The DEA suggests that investment in technology and human capital could improve the overall efficiency of the logging industry. Although inputs usage are key to the productivity of harvesting operations, factors external to the managerial control of contractors could influence their performance. A two-stage approach that incorporates DEA and regression analysis was used to determine the influence of external factors on the technical efficiency of harvesting operations based on the New Zealand benchmarking dataset. The external factors considered include the size of operation, forest terrain, log sorts, piece size and the forest region. The results indicate that the size of operation, forest terrain, log sorts and piece size, all significantly (p 0.01). The result shows that the ability of a harvesting crew to utilize its inputs to achieve desired output level is not only influenced by discretionary factors but also by the operating environment. Using a forest company-specific database, a multi-step DEA procedure is applied to 67 forest harvesting contractors to estimate their managerial efficiency while taking into account the influence of the operating environment. The performance of the contractors is evaluated using seven inputs, one output and three operating environment factors. The result shows a significant difference between the mean managerial efficiency of the crews before and after controlling for the influence of the operating environment, the latter being higher by 12%. This study provides evidence that without accounting for the influence of the operating environment, the resulting DEA efficiency estimates will be biased; overestimating the performance of crews in favourable environment and underestimating that of those in more difficult environment