Design and Management of Responsible and Optimal Global Supply Chains for Retailing

我的长期研究目标是解决以下问题：我们如何使用数据、模型和决策算法，最有效地为客户提供更高的便利，同时对社会负责？在短期内，我计划与学生和现实生活中的公司合作开展以下两个研究项目。为零售业设计负责任的基于订阅的个性化服务：像Loblaws和亚马逊这样的知名零售商是人类历史上最大的消费者数据库，因为他们可以同时访问消费者和制造商。然后，他们可以使用高级数据分析来揭示客户洞察，并准确地提供消费者-产品匹配（即，提供个性化）。这对消费者有好处;但是，零售商获取这些数据的问题引起了道德上的关注。在设计个性化时考虑隐私和其他社会问题的研究很少见。我们在这项研究计划中的目标是为食品行业开发最佳的基于订阅的个性化服务，超越典型的客户和供应链数据以及利润最大化目标，还包括隐私和健康问题。这项研究将建立在我们正在进行的关于使用机器学习开发零售个性化系统以及研究隐私对产品定位的影响的工作的基础上，但将从理论和应用角度推进我们的理解。 管理供应链以实现对环境负责的零售创新：个性化导致零售商的产品种类激增，这可能对社会产生不利影响。最好的例子是快时尚行业，由于过度用水和碳排放以及促进轻率消费而导致环境危机。尽管一些新的商业模式创新试图应对这些挑战，但关于如何通过结合需求和供应角度来负责任地管理相关供应链的研究很少。在这项研究计划中，我们将分析这些创新，鼓励替代类型的所有权机制，例如，租，而不是购买新的项目。我们在这方面的主要目标之一是评估创新的确切环境风险。此外，有一定的供应链优化面临这些模型的定价，采购和库存管理的挑战，尚未得到充分解决的文献。我们的研究经验与租赁模式的耐用品在各个零售部门将是有益的，这一研究计划。上述计划将是第一批制定框架的计划之一，该框架可以帮助加拿大两个经济上重要的零售行业（食品和时尚）实现盈利，而不会对其长期未来构成威胁。它还将有助于建立一个更健康和更可持续的加拿大社会。

My long-term research objective is to address the following issue: how can we use data, models and decision-making algorithms to most efficiently provide higher convenience for customers while being socially responsible? In the short-term, I plan to carry out the following two research programs in collaboration with students and real-life firms. Designing responsible subscription-based personalization for retailing: Well-known retailers like Loblaws and Amazon today are the biggest repository of consumer data in human history because of their access to both consumers and manufacturers. They can then use advanced data analytics to uncover customer insights and accurately provide consumer-product matches (i.e., provide personalization). This has benefits for consumers; but, ethical concerns have been raised regarding retailers' access to such data. Research that considers privacy and other social issues while designing personalization is rare. Our goal in this research program is to develop optimal subscription-based personalization services for the food sector that goes beyond typical customer and supply chain data and profit maximization objective to also include privacy and health concerns. This research will build on our ongoing work about developing retail personalization systems using machine learning and about studying the impact of privacy on product positioning, but will advance our understanding from theoretical and applied perspectives. Managing supply chains for environmentally responsible retail innovations: Personalization creates proliferation in retailers' product variety that can potentially have adverse impact on the society. Prime example is the fast-fashion sector that is blamed for creating an environmental crisis due to excessive water usage and carbon emissions as well as for promoting frivolous consumption. Although some new business model innovations attempt to tackle these challenges, there is little extant research about how to responsibly manage the associated supply chains by incorporating both demand and supply perspectives. In this research program, we will analyze these innovations that encourage alternate types of ownership mechanisms, e.g., rentals, rather than purchase of new items. One of our primary goals in this context will be to assess the precise environmental risk of the innovations. Moreover, there are certain supply chain optimization challenges facing these models-pricing, purchasing and inventory management-that have not yet been addressed adequately in the literature. Our research experience with rental models for durable products in various retail sectors will be useful for this research program. The above program will be among the first to develop a framework that can help two economically important retail sectors for Canada (food and fashion) to be profitable without being a threat to its long-term future. It will also help in building a healthier and more sustainable Canadian society.