On the role of data analytics and fuzzy sets theory in healthcare supply chain management.

关于数据分析和模糊集理论在医疗保健供应链管理中的作用。

• 批准号: RGPIN-2020-06214
• 负责人: Appadoo, Srimantoorao
• 金额: $ 1.89万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743934
• 关键词: role; data; analytics; fuzzy; sets

Previously, numerous researches have demonstrated the benefits of incorporating data analytics, fuzzy sets, and predictive analysis in the fields of logistics & SCM. It has been made certain via various researches that healthcare SCM is complicated and sensitive. To my understanding, to cope with the complexity of those problems, healthcare SCM demands a practical decision-making mechanism. Today's integrated healthcare SCM are technically and logistically challenging to operate via classical deterministic optimization theory. The importance of developing fuzzy data driven decision optimization models along with predictive analysis have been recognized in the healthcare SCM and there are documented evidences signifying the same. The data driven approach we proposed, will offer substantial value in areas such as distribution optimization, and healthcare SCM. My research program will primarily focus on health care SCM, supply chain data analytics, and fuzzy sets theory. At the heart of this grant application, I will be using data analytics, and fuzzy methods to improve the decision-making process. The application of data analytics, and fuzzy sets theory in an integrated healthcare environment not only has the potential to reduce healthcare costs but can also facilitates real-time monitoring. I believe that our data driven approach will also be able to assist in influencing productivity, and innovation in different sectors of the economy. My research team, and I will be testing novel hypotheses, by borrowing methods & concepts from various fields of research and apply them effectively. The primary contributions of this research are threefold. First, we aim to comprehend the various issues faced by most of the healthcare organizations, and the issues they faced associated to real-time monitoring of activities. Second, we hope that our research will contribute to theory building by providing a better perspective to decision-making. Third, we will develop & test the effectiveness of the Big-Data driven optimization models. Results & findings will be shared with the academic community via submission to top journals, conferences, invited talks, and classroom discussions. We anticipate being able to publish around 15 research papers in leading journals based on the research carried out. This research will contribute to the application of data analytics, and fuzzy quantitative methods. These research areas will impact both local, and national economies. The proposed research will provide opportunities for training of highly qualified personnel. Also, the development of data analytics will bring-out many research opportunities regarding the application of data analytics. Highly qualified personnel will learn theories, models, examining novel analytical approaches, practices, and opportunities. Our future research work may as well include the integration of vehicle routing problem in healthcare supply chain during emergency.

在此之前，许多研究已经证明了在物流和供应链管理领域结合数据分析，模糊集和预测分析的好处。各种研究表明，医疗供应链管理是复杂而敏感的。据我所知，为了科普这些问题的复杂性，医疗保健供应链管理需要一个实用的决策机制。今天的综合医疗保健供应链管理在技术上和物流上都具有挑战性，通过经典的确定性优化理论。开发模糊数据驱动决策优化模型和预测分析的重要性沿着在医疗保健供应链管理中得到了认可，并且有文件证据表明这一点。我们提出的数据驱动方法将在分销优化和医疗保健SCM等领域提供实质性价值。我的研究计划将主要集中在医疗保健供应链管理，供应链数据分析和模糊集理论。在这项拨款申请的核心，我将使用数据分析和模糊方法来改善决策过程。数据分析和模糊集理论在综合医疗环境中的应用不仅有可能降低医疗成本，而且还可以促进实时监控。我相信，我们的数据驱动方法也将有助于影响不同经济部门的生产力和创新。我和我的研究团队将通过从各个研究领域借用方法和概念并有效地应用它们来测试新的假设。 这项研究的主要贡献有三个方面。首先，我们的目标是了解大多数医疗机构面临的各种问题，以及他们面临的与活动实时监控相关的问题。其次，我们希望我们的研究将有助于理论建设提供一个更好的角度来决策。第三，我们将开发和测试大数据驱动的优化模型的有效性。结果和发现将通过提交给顶级期刊，会议，邀请演讲和课堂讨论与学术界分享。我们预计能够根据所开展的研究在领先期刊上发表约15篇研究论文。该研究将有助于数据分析和模糊定量方法的应用。这些研究领域将影响地方和国家经济。拟议的研究将为培训高素质人员提供机会。此外，数据分析的发展将带来许多关于数据分析应用的研究机会。高素质的人员将学习理论，模型，研究新的分析方法，实践和机会。我们未来的研究工作也可能包括紧急情况下医疗供应链中车辆路径问题的集成。