Decision making in the forest value chain

本提案的主要目标是开发决策支持工具，提高 未来加拿大森林生物经济价值链通过分析。分析使组织能够将 通过将数据转化为信息，将信息转化为 洞察力，以更好地决策和改善结果。另一个关键目标是利用大数据， 结合分析技术和工具，以减少污染，从而促进 森林社区，使物流和运输业务更有效率。这项建议是 FORAC 4.0研究计划的一部分，并从成功的研究工作进行了延伸， 在过去的15年里，研究联盟。工业界领袖和学术界人士都发现了这些问题 处理。在我们这边，我们组建了一个由来自工业界的研究人员和学生组成的多学科团队， 工程、商业分析、森林工程和计算机科学领域。在此过程中，我们发现了一个 研究和技术转让成功关键因素：与工业伙伴密切合作。这些 合作伙伴将为案例研究提供问题见解、数据、设施和结果分析，并成为 技术转移13个多学科和团队培训的HQP将是一个重要的成果， 该提案（4博士，5名硕士，4实习）。

The primary objective of this proposal is to develop decision support tools, enhancing the competitiveness of future Canadian forest bioeconomy value chains through analytics. Analytics enable organizations to turn complex challenges into substantial opportunities by transforming data into information and information into insight for better decision-making and result improvement. Another key objective is to leverage Big Data in conjunction with analytics techniques and tools to reduce pollution, thereby stimulating the viability of forest-based communities and making logistics and transportation operations more efficient. This proposal is part of the FORAC 4.0 research program and extends from successful research efforts conducted by the research consortium over the last 15 years. Both industrial leaders and academics have identified the problems addressed. On our end, we assembled a multidisciplinary team of researchers and students from the industrial engineering, business analytics, forest engineering, and computer science fields. In so doing, we identified a key factor for success in research and technology transfer: a close collaboration with industrial partners. These partners will provide problem insight, data, facilities, result analysis for case studies, and be receivers of technology transfer. Thirteen multidisciplinary and team-trained HQP will be an important outcome during this proposal (4 Ph.D., 5 M.Sc., and 4 internships).