Purpose of Review

The forest engineering sector has in the last few decades derived its operational decisions from advanced data-driven processes as opposed to the basic input–output relationship. This has been with the aim of increasing operational efficiencies, maximizing productivity, and achieving sustainable forest operations. This study surveys the application of data envelopment analysis (DEA), an advanced operations analysis technique, in the broad field of forest engineering, focused on maximizing machinery input usage and productivity as it relates to the harvesting and processing of primary and secondary wood products. The review analyzes DEA journal publications via common online literature databases up until June 2022 with the aim of identifying and synthesizing research progress in the field for beneficial practical business applications.

Recent Findings

A total of 38 scientific articles were reviewed, and they all emphasized applications of existing DEA models as opposed to theoretical development of the models. The forest utilization sector appears to be the predominant area of DEA application in forest engineering practice with the year 2021 having the highest number of publications. Furthermore, conventional DEA models comprising of Charnes, Cooper, and Rhodes (CCR) and Banker, Chames, and Cooper (BCC) models are the most commonly applied DEA methodological approach, thus resulting in simple technical performance estimates. This is unexpected given that more robust DEA models have been developed over the years. As expected, more attention has been given to the analysis of determinants of efficiency in forest engineering production technologies.

Summary

Untapped opportunities exist for researchers and practitioners in the application of DEA in forest engineering including recent derivatives of conventional DEA models, possible development of application suites specifically for forest engineering-related operations, and the development of dedicated benchmarking databases with more relevant production data for individual production technologies. This could provide opportunities for data-driven decisions by policy makers and managers in terms of harvest contractor selection, technology deployment, sustainability benchmarks, and for improving production efficiency.

中文翻译：

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数据包络分析在林业工程中的应用综述

审查目的

在过去的几十年里，森林工程部门的运营决策源自先进的数据驱动流程，而不是基本的投入产出关系。这样做的目的是提高运营效率、最大限度地提高生产力并实现可持续森林运营。本研究调查了数据包络分析 (DEA) 的应用，这是一种先进的操作分析技术，在广泛的森林工程领域中的应用，重点是最大限度地提高与初级和次级木制品的采伐和加工相关的机械投入使用和生产率。该综述通过通用在线文献数据库分析了截至 2022 年 6 月的 DEA 期刊出版物，旨在识别和综合该领域的研究进展，以实现有益的实际商业应用。

最近的发现

总共审查了 38 篇科学文章，它们都强调现有 DEA 模型的应用，而不是模型的理论发展。森林利用领域似乎是 DEA 在森林工程实践中应用的主要领域，2021 年的出版物数量最多。此外，传统的 DEA 模型包括 Charnes、Cooper 和 Rhodes (CCR) 和 Banker、Chames 和 Cooper (BCC) 模型，是最常用的 DEA 方法，因此可以进行简单的技术性能估计。鉴于多年来已经开发出更强大的 DEA 模型，这是出乎意料的。正如预期的那样，森林工程生产技术效率的决定因素分析受到了更多关注。

概括

对于研究人员和从业者来说，DEA 在森林工程中的应用存在着尚未开发的机会，包括传统 DEA 模型的最新衍生品、专门针对森林工程相关操作的应用套件的可能开发，以及为个人开发包含更相关生产数据的专用基准数据库。生产技术。这可以为政策制定者和管理者在采伐承包商选择、技术部署、可持续性基准和提高生产效率方面做出数据驱动的决策提供机会。