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，在对社会负责的同时，最有效地为客户提供更高的便利？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：今天，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存储库，因为它们可以接触到消费者和制造商。然后，他们可以使用高级数据分析来发现客户洞察，并准确地提供消费者与产品的匹配(即，提供个性化)。这对消费者来说是有好处的；但也有人对零售商获取此类数据提出了伦理方面的担忧。在设计个性化时考虑隐私和其他社会问题的研究很少。我们在这个研究项目中的目标是为食品行业开发基于订阅的最佳个性化服务，超越典型的客户和供应链数据和利润最大化目标，同时也包括隐私和健康问题。这项研究将建立在我们正在进行的使用机器学习开发零售个性化系统和研究隐私对产品定位的影响的工作的基础上，但将从理论和应用的角度推进我们的理解。为对环境负责的零售创新管理供应链：个性化导致零售商产品种类的激增，这可能会对社会产生不利影响。最好的例子是快时尚行业，它被指责因过度用水和碳排放造成环境危机，以及促进轻率消费。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负责任地管理相关供应链的现有研究很少。在本研究计划中，我们将分析这些鼓励替代类型所有权机制的创新，例如租赁，而不是购买新项目。在这方面，我们的主要目标之一将是评估这些创新的准确环境风险。此外，这些模型还面临着某些供应链优化挑战--定价、采购和库存管理--这些挑战在文献中还没有得到充分的解决。我们在不同零售部门耐用产品租赁模式方面的研究经验将对本研究计划有所帮助。上述计划将是首批制定框架的计划之一，该框架可以帮助加拿大两个具有重要经济意义的零售部门(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、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.