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

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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.