Correlation Between Key Parameters of Wood Biomass Reverse Supply Chains and Their Impact on Total Biomass Acquisition Levels
Source abstract
Abstract
Effectiveness is the prism through which supply chains are defined.Different management methods can be used to measure supply chain effectiveness, such as life cycle assessment or sustainable supply chain management.In order to properly model and manage reverse supply chains for wood biomass, the structure of the supply chains was examined.For this purpose, a correlation analysis of the basic parameters of wood biomass reverse supply chains was carried out.After their identification, the level of mutual correlation was verified using Pearson's correlation coefficient.The coefficient of determination r 2 was also verified for the two parameters with the highest degree of interaction -the sales of wood chips and the purchase of wood biomass.To improve the verification, scatter plots were made to further confirm the degree of interaction.As a result of the analysis of the parameter interdependencies and a discussion of the results of other international studies, the parameters were found to be applicable in modelling effective reverse supply chains of wood biomass.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Forest Biomass Utilization and Management · Forest Management and Policy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (13)
Show source-provided references
1. Abejón, R., Bala, A., Vázquez-Rowe, I., Aldaco, R., and Fullana-i-Palmer, P. [2020] “When plastic packaging should be preferred: Life cycle analysis of packages for fruit and vegetable distribution in the Spanish peninsular market,” Resources, Conservation and Recycling, Vol. 155, p. 104666.10.1016/j.resconrec.2019.104666 ↗
2. Accorsi, R., Manzini, R., Pini, C., and Penazzi, S. [2015] “On the design of closed-loop networks for product life cycle management: Economic, environmental and geography considerations,” Journal of Transport Geography, Vol. 48, pp. 121–134.10.1016/j.jtrangeo.2015.09.005 ↗
3. Al-Babtain, A. A. [2010] “Optimal Control of Reverse Logistics Model with Logistic Demand and Return Rates,” Applied Mathematical Sciences, Vol. Volume 4, Issue 49-52, p. Pages 2561-2568.
4. Cordella, M., Tugnoli, A., Spadoni, G., Santarelli, F., and Zangrando, T. [2008] “LCA of an Italian lager beer,” The International Journal of Life Cycle Assessment, Vol. 13, No.2, pp. 133–139.10.1065/lca2007.02.306 ↗
5. Do, M.-H., Huang, Y.-F., Department of Marketing and Logistics Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung, 41349 Taiwan, R.O.C, and Faculty of Economics, Tay Nguyen University, Vietnam. [2022] “Evaluation of parameters for the sustainable supply chain management: a Taiwanese fresh-fruit sector,” AIMS Environmental Science, Vol. 9, No.1, pp. 16–32.10.3934/environsci.2022002 ↗
6. Fernández Campos, P., Trucco, P., and Huaccho Huatuco, L. [2019] “Managing structural and dynamic complexity in supply chains: insights from four case studies,” Production Planning & Control, Vol. 30, No.8, pp. 611–623.10.1080/09537287.2018.1545952 ↗
7. Kannegiesser, M., and Günther, H.-O. [2014] “Sustainable development of global supply chains—part 1: sustainability optimization framework,” Flexible Services and Manufacturing Journal, Vol. 26, No.1–2, pp. 24–47.10.1007/s10696-013-9176-5 ↗
8. Kolinski, A., Sliwczynski, B., and Golinska-Dawson, P. [2017] “The assessment of the economic efficiency of production process — Simulation approach,” 24th International Conference on Production Research, pp. 283–289.
9. Lee, Y. H., Golinska-Dawson, P., and Wu, J.-Z. [2016] “Mathematical Models for Supply Chain Management,” Mathematical Problems in Engineering, Vol. 2016, pp. 1–4.10.1155/2016/6167290 ↗
10. de Leeuw, S., Grotenhuis, R., and van Goor, A. R. [2013] “Assessing complexity of supply chains: evidence from wholesalers,” International Journal of Operations & Production Management, Vol. 33, No.8, pp. 960–980.10.1108/IJOPM-07-2012-0258 ↗
11. Mouronte-López, M. L. [2021] “Modeling the Public Transport Networks: A Study of Their Efficiency,” Complexity, (J.-H. Pan, ed.), Vol. 2021, pp. 1–19.10.1155/2021/3280777 ↗
12. Pardoe, I. [2012] Applied regression modeling, Wiley, Hoboken, NJ.
13. Statistics Poland. [2022] “Statistical Yearbook of Forestry,” Statistical Office in Białystok.