Majumdar, Mayukh (2022-04). Essays on Resource Planning and Utilization to Achieving Operational Efficiency in Service Organizations. Doctoral Dissertation.
Thesis
The broadening of scope in Operations Management (OM) has facilitated new opportunities to study resource utilization and planning in service organizations for higher operational efficiency. In this dissertation, I investigate operational issues in online retailing logistics, healthcare, and social media. In my first essay, I focus on supply logistics in online retailing by analyzing safety stock allocation and transshipments to minimize their operational cost. Using a stochastic optimization framework, my proposed approach solves the problem optimally for smaller networks and offers a decomposition method for larger networks. I demonstrate its potential to save significant outbound shipping costs for an online retailer. In my second essay, I examine Accountable Care Organizations (ACO), a network of healthcare providers who collaborate to offer high-quality care at a reduced cost. To address their performance issues, I analyze the role of provider composition in delivering primary care and ACO experience on performance. Using a dataset of ACOs under Medicare, I find that (i) ACOs achieve better performance by utilizing more nurse practitioners, (ii) having more primary care services delivered by specialists does not result in better quality and (iii) high performing ACOs focus more on savings in the initial phases under any risk model and consider quality initiatives in the higher risk model. Collectively, the results provide guidelines on improving ACO performance. In my third essay, I examine how firms can deliver better content to their target audience via social media posts and maximize user engagement. Developing social media posts with compelling features that capture users' attention is a vital task, albeit a challenging one. I propose a datadriven optimization framework for analyzing and publishing social media posts across multiple platforms. This framework captures users' preferences via analytics to develop better content for planning horizon under a firm's limited budget. In that regard, I identify the number of features for each platform to be included in social media posts and schedule them throughout the planning horizon in the context of maximizing user engagement. The models and results in the third chapter can help firms improve their social media campaign and achieve higher user engagement.
