Gestão & Produção
https://www.gestaoeproducao.com/article/doi/10.1590/0104-530x5677-20
Gestão & Produção
Seção Temática: Transformação Digital, Manufatura Inteligente e Gerenciamento da Cadeia de Abastecimento 4.0

Forestry 4.0: a framework for the forest supply chain toward Industry 4.0

Silvicultura 4.0: Um framework para a cadeia de suprimentos florestal no contexto da Indústria 4.0

Yan Feng; Jean-François Audy

http://dx.doi.org/10.1590/0104-530x5677-20 Gest. Prod., vol.27, n4, 5677, 2020
PDF
SciELO
Downloads: 0
Views: 757

Abstract

Abstract: Forest industry plays an important role in global economy and has significant influences in our lives and the environment that we live in. With the rapid advancement of digital technologies and industrial transformations towards Industry 4.0, similar trend has been found in the forest industry and especially on its forest procurement side. Forestry 4.0 has been proposed as research initiatives in recent years. However, publications have largely focused on the digital technologies. This article is aimed at presenting a framework to provide a holistic view of Forestry 4.0 from a forest supply chain perspective. The framework consists of four major components including the digital technologies pertinent to each of the supply chain business activities; the network infrastructure; the next generation system intelligence; and the collaborative forest supply chain digital ecosystem. These components are essential for the forest industry transformation to become truly interconnected among its supply chain actors. Some economic, environmental, and social expected benefits of Forestry 4.0 are discussed as well as potential impacts and challenges.

Keywords

Forestry 4.0, Industry 4.0, Smart forestry, Forest industry supply chain, Collaboration, Digital ecosystem

Resumo

Resumo: A indústria florestal desempenha um papel importante na economia global e tem influências significativas em nossas vidas e no meio ambiente em que vivemos. O rápido avanço das tecnologias digitais e as transformações industriais em direção à Indústria 4.0 também tem afetado a indústria florestal, principalmente os processos relacionados às compras florestais. A silvicultura 4.0 tem sido alvo de iniciativas de pesquisa nos últimos anos. No entanto, as publicações concentraram-se amplamente nas tecnologias digitais. Este artigo tem como objetivo apresentar um framework para fornecer uma visão holística da Silvicultura 4.0 a partir de uma perspectiva da cadeia de suprimentos florestal. O framework consiste em quatro componentes principais, incluindo as tecnologias digitais pertinentes a cada uma das atividades de negócios da cadeia de suprimentos; a infraestrutura da rede; a inteligência do sistema de próxima geração; e o ecossistema digital da cadeia de suprimentos florestais colaborativa. Esses componentes são essenciais para que a transformação da indústria florestal, na direção de uma verdadeira interconexão entre todos os atores da cadeia de suprimentos. Alguns benefícios econômicos, ambientais e sociais esperados do Silvicultura 4.0 são discutidos, bem como os impactos e desafios potenciais.

Palavras-chave

Silvicultura 4.0, Indústria 4.0, Silvicultura inteligente, Cadeia de suprimentos da indústria florestal, Colaboração, Ecossistema digital

Referências

Adelantado F., Vilajosana X., Tuset-Peiro P., Martinez B., Melia-Segui J., Watteyne T. Understanding the Limits of LoRaWAN. IEEE Communications Magazine. 2017;55(9):34-40.

Aldred A. H., Bonnor G. M. Application of airborne lasers to forest surveys. 1985.

Andersson G., Flisberg P., Lidén B., Rönnqvist M. RuttOpt: a decision support system for routing of logging trucks. Canadian Journal of Forest Research. 2008;38(7):1784-96.

Atobishi T., Gábor Szalay Z., Podruzsik S. Cloud computing and big data in the context of industry 4.0: opportunities and challenges.. 2018.

Audy J.-F., D’Amours S., Rönnqvist M. Planning methods and decision support systems in vehicle routing problems for timber transportation: a review. 2012.

Audy J.-F., D’Amours S., Rousseau L.-M., Favreau J., Marier P. Virtual transportation manager: a web-based system for transportation optimization in a network of business units.. 2007.

Bauernhansl T., ten Hompel M., Vogel-Heuser B. Industrie 4.0 in produktion, automatisierung und logistik: anwendung, technologien und migration. Design principles for industrie 4.0 scenarios: a literature review. 2014.

Blocker M., Mundoch I., Bromley K., Geissbauer R., Vedso J., Schrauf S. Industry 4.0: building the digital enterprise: forest, paper and packaging key findings. 2016.

Bungart S. Industrial Internet versus Industrie 4.0. Produktion: technik und Wirtschaft für die deutsche Industrie. 2014.

Choudhry H., O’Kelly G. Precision forestry: a revolution in the woods. 2018.

Cimino C., Negri E., Fumagalli L. Review of digital twin applications in manufacturing. Computers in Industry. 2018;113.

Main characteristics of Industry 4.0. 2016.

D’Amours S., Ouhimmou M., Audy J.-F., Feng Y. Forest value chain optimization and sustainability. 2016.

Davis J., Edgar T., Porter J., Bernaden J., Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering. 2012;47(20):145-56.

Epstein R., Karlsson J., Rönnqvist M., Weintraub A. Forest transportation.. Handbook of operations research in natural resources. 2007:391-403.

Internet of Things forecast. 2018.

Eriksson J., Rönnqvist M., Iwarsson Wide M., Hallberg I. Transportation and route planning: Åkarweb: a web-based planning system.. 2003:48-57.

Evans P. C., Annunziata M. Industrial Internet: pushing the boundaries of minds and machines. 2012.

Finlay S. Artificial intelligence and machine learning for business: a no-nonsense guide to data driven technologies.. 2017.

State of the World’s forests: enhancing the socioeconomic benefits from forests.. 2014.

Forsberg M., Frisk M., Rönnqvisty M. FlowOpt: a decision support tool for strategic and tactical transportation planning in forestry. International Journal of Forest Engineering. 2005;16(2):101-14.

Gingras C., Cordeau J.-F., Laporte G. Un algorithme de minimisation du transport à vide appliqué à l’industrie forestière. Information Systems and Operational Research. 2007;45(1):41-7.

Gingras J.-.F., Charette F. FPInnovations’ Forestry 4.0 Initiative. 2017.

Hennick C. Network infrastructure: the ‘glue of Industry 4.0. CIO. 2019.

Hermann M., Pentek T., Otto B. Design principles for industrie 4.0 scenarios: a literature review. 2015.

Hu H., Wen Y., Chua T. S., Li X. Toward scalable system for big data analytics: a technology tutorial. IEEE Access: Practical Innovations, Open Solutions. 2014;2:652-87.

Johnson K. N., Stuart T. W. FORPLAN version 2: mathematical programmer’s guide.. 1987.

Kagermann H., Wahlster W., Helbig J. Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industry 4.0 Working Group.. 2013.

Kershaw Jr. J. A. J. R., Ducey M. J., Beers T. W., Husch B. Forest mensuration. 2017.

Kokenge K. S. Opportunities and challenges for decision support systems in log truck scheduling and dispatching. 2011.

Lacey L. A., Goettel M. S. Current developments in microbial control of insect pests and prospects for the early 21st century. Entomophaga. 1995;40(1):3-27.

Lee J., Bagheri B., Kao H. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters. 2015;3:18-23.

Lim K., Treitz P., Wulder M., St-Onge B., Flood M. LiDAR remote sensing of forest structure. Progress in Physical Geography. 2003;27(1):88-106.

Luenendonk M. Industry 4.0: definition, design principles, challenges, and the future of employment. 2017.

Ma J. Internet-of-Things: technology evolution and challenges. 2014.

Manger J. Forestry 4.0: digitalization in the forest industry. 2018.

Marques A. S., Rasinmäki J., Soares R., Amorim P. Planning woody biomass supply in hot systems under variable chips energy content. Biomass and Bioenergy. 2018;108:265-77.

McCann J., Moore M. 5G: everything you need to know. 2019.

Miragaia C., Borges J. G., Rodrigues F. A., Rodriguez L. C. Uma aplicação do sistema inFlor na gestão de dados florestais. 1999.

Mirowski L., Ghaffariyan M. R., Wise A., Acuna M., Turner P. Reducing transport costs through optimised transport planning: a case study using the FastTRUCK software tool.. 2016.

Overview of Canada’s forest industry. 2016.

Hermann M., Pentek T., Otto B. Accelerating U.S. advanced manufacturing: report to the President. Design principles for industrie 4.0 scenarios: a literature review. 2014.

About the Remsoft spatial planning system. Allocation optimizer user guide. 2005:9-14.

Reynolds K. M., Rodriguez S., Bevans K. User guide for the ecosystem management decision support system, Version 3.0.. 2003.

Reynolds K. M., Twery M., Lexer M. J., Vacil H., Ray D., Shao G., Borges J. G. Decision support systems in forest management.. Handbook on decision support system. 2007.

Rönnqvist M. Optimization in forestry. Mathematical Programming. 2003;97(1-2):267-84.

Rönnqvist M. OR challenges and experiences from solving industrial applications. International Transactions in Operational Research. 2012;19(1-2):227-51.

Rosset C., Brand R., Caillard I., Fiedler U., Gollut C., Schmocker A., Weber D., Wuillemin E. MOTI: L’inventaire forestier facilité par le smartphone. 2014.

Sanchez-Iborra R., Cano M. State of the art in LP-Wan solutions for industrial IoT services. Sensors. 2016;16(5):708.

Savola J., Rummukainen H., Jokinen O. KUORMA: a collection of APS-algorithms for forest industry wood transport. ERCIM News. 2004;56:29-31.

Scholz J., De Meyer A., Marques A. S., Pinho T. M., Boaventura-Cunha J., Van Orshoven J., Rosset C., Künzi J., Kaarle J., Nummila K. Digital technologies for forest supply chain optimization: existing solutions and future trends. Environmental Management. 2018;62(6):1108-33.

Schrauf S., Berttram P. Industry 4.0: how digitization makes the supply chain more efficient, agile, and customer-focused. 2016.

Siipilehto J., Lindeman H., Vastaranta M., Yu X., Uusitalo J. Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and preharvest measurement tool EMO. Silva Fennica. 2016;50(3):1568.

ForestHQ. 2019.

Trestima forest: see the wood for the trees. 2018.

Veile J. W., Kiel D., Müller J. M., Voigt K. I. Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management. 2019;31(5).

The future of jobs: employment, skills and workforce strategy for the fourth industry revolution. 2016.

Yuan C., Zhang Y., Liu Z. A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Canadian Journal of Forest Research. 2015;45(7):783-92.

5ff70c570e882542175aeabd gp Articles
1806-9649 (Eletrônico) 0104-530X (Impresso)
Gest. Prod. ©2024 Todos os direitos reservados.

Gest. Prod.

Share this page
Page Sections