Purpose of Review

Forest and biomass utilization is of great significance in the context of global change toward renewable energy and products. Supply chain management has been proven as an effective path to improve economic and environmental performance of forest and biomass products. However, the existing studies are fragmented and task-oriented. In this paper, we aim to systematically form a data-oriented modeling and analytics summary including the framework and toolbox for enhancement of forest and biomass supply chain management.

Recent Findings

With the coming of the big data era, data analytics methods and tools for forest and biomass supply chain management are progressively updated. Conventional analytical methods are facing challenges, though most of them are still currently dominant in practice. The big data era provides promising opportunities for data-driven methods and tools, based on which the data-oriented modeling framework is gradually emerging.

Summary

Generally, rapid development of computing capability and algorithms greatly facilitates the application and accuracy of the simulations and optimization, while built-in databases play an important role in the data analytics. Macro-enabled spreadsheet-based models and tools are still popularly used in practices due to their ease-of-use. Specifically, development of advanced techniques improves upon the efficiency and accuracy of conventional time-motion study approaches. A statistically appropriate experimental design and the underlying assumption verification are essential to obtaining reliable results. Traditionally, suitability modeling has been the trusted approach for optimal facility siting, but the emerging probability modeling is a promising data-driven approach in the context of the big data era. Linear programming modeling is still dominating in the supply chain optimization, while non-linear programming modeling is emerging due to the rapid development of algorithm and computational capacity. Techno-economic analysis (TEA) and life cycle assessment (LCA) provide important results regarding the economic and environmental efficiency of various forest and biomass supply chain systems. However, TEA and LCA results can vary due to modeling approach, data availability, and differences among underlying methods and databases. Monte Carlo simulation could be a fundamental approach to examining the uncertainty issues in both TEA and LCA.

中文翻译：

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增强森林和生物量供应链管理的数据分析

审查目的

在全球向可再生能源和产品转变的背景下，森林和生物质的利用具有重要意义。供应链管理已被证明是改善森林和生物质产品的经济和环境绩效的有效途径。但是，现有研究是分散的和面向任务的。在本文中，我们旨在系统地形成面向数据的建模和分析摘要，包括用于增强森林和生物量供应链管理的框架和工具箱。

最近的发现

随着大数据时代的到来，用于森林和生物量供应链管理的数据分析方法和工具正在逐步更新。常规分析方法面临挑战，尽管其中大多数目前仍在实践中占主导地位。大数据时代为数据驱动的方法和工具提供了广阔的机遇，在此基础上，面向数据的建模框架逐渐兴起。

概要

通常，计算能力和算法的快速发展极大地促进了仿真和优化的应用和准确性，而内置数据库在数据分析中起着重要作用。基于宏的基于电子表格的模型和工具由于易于使用，因此仍在实践中广泛使用。具体而言，先进技术的发展提高了传统时空学习方法的效率和准确性。统计上合理的实验设计和基本的假设验证对于获得可靠的结果至关重要。传统上，适应性建模一直是最佳设施选址的可信赖方法，但是在大数据时代的背景下，新兴的概率建模是一种有前途的数据驱动方法。线性规划建模仍在供应链优化中占主导地位，而非线性规划建模则由于算法和计算能力的快速发展而兴起。技术经济分析（TEA）和生命周期评估（LCA）提供了有关各种森林和生物质供应链系统的经济和环境效率的重要结果。但是，TEA和LCA结果可能因建模方法，数据可用性以及基础方法和数据库之间的差异而有所不同。蒙特卡洛模拟可能是检查TEA和LCA中不确定性问题的基本方法。技术经济分析（TEA）和生命周期评估（LCA）提供了有关各种森林和生物质供应链系统的经济和环境效率的重要结果。但是，TEA和LCA结果可能因建模方法，数据可用性以及基础方法和数据库之间的差异而有所不同。蒙特卡洛模拟可能是检查TEA和LCA中不确定性问题的基本方法。技术经济分析（TEA）和生命周期评估（LCA）提供了有关各种森林和生物质供应链系统的经济和环境效率的重要结果。但是，TEA和LCA结果可能因建模方法，数据可用性以及基础方法和数据库之间的差异而有所不同。蒙特卡洛模拟可能是检查TEA和LCA中不确定性问题的基本方法。