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Data Analytics for Enhancement of Forest and Biomass Supply Chain Management

  • Forest Engineering (R Spinelli, Section Editor)
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Abstract

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.

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Funding

This research work is supported by the US Department of Agriculture National Institute of Food and Agriculture AFRI Competitive Grant No. 2019-67020-29287.

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Authors and Affiliations

  1. Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV, 26506, USA

    Xufeng Zhang, Jingxin Wang, John Vance & Yuxi Wang

  2. College of Engineering and Technology, Northeast Forestry University, Harbin, 150040, People’s Republic of China

    Jinzhuo Wu

  3. Idaho National Laboratory, Idaho Falls, ID, 83415, USA

    Damon Hartley

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  1. Xufeng Zhang
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Correspondence to Jingxin Wang.

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Jingxin Wang, Xufeng Zhang, John Vance, Yuxi Wang, Jinzhuo Wu, and Damon Hartley declare that they have no conflict of interest.

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Zhang, X., Wang, J., Vance, J. et al. Data Analytics for Enhancement of Forest and Biomass Supply Chain Management. Curr Forestry Rep 6, 129–142 (2020). https://doi.org/10.1007/s40725-020-00111-w

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