It focuses on the behavior of consumers and explores the role of heterogeneity in their valuation for the product and cost of purchasing via group-buying in the functioning of group-buying services as a price-discrimination device. Finally, the role of group-buying services in improving channel coordination under asymmetric information is studied in Chapter Four. This analysis shows that the availability of group-buying services provides an opportunity for the manufacturer to reduce the informational rents of the retailer arising from its private information about the market condition. Interestingly, the manufacturer can avoid paying these rents and regains the first-best profitability when asymmetry in information exists regarding the relative sizes of consumer segments. In other settings (e.g., when asymmetric information exists regarding consumers' price sensitivity), leveraging the group-buying mechanism nevertheless allows the manufacturer to design a contract that requires lower rents and improves channel coordination to some extent.

This paper develops a model of consumer returns policies. In our model, consumers face valuation uncertainty and realize their valuations only after purchase. There is also aggregate demand uncertainty, captured using the conventional newsvendor model. In this environment, consumers decide whether to purchase and then whether to return the product, while the seller sets the price, quantity, and refund amount.Using our model, we study the impact of full returns policies (e.g., using 100% money back guarantees) and partial returns policies (e.g., when restocking fees are charged) on supply chain performance. Next, we demonstrate that consumer returns policies may distort incentives under common supply contracts (such as manufacturer buy-backs), and we propose strategies to coordinate the supply chain in the presence of consumer returns. Finally, we explore several extensions and demonstrate the robustness of our findings.

In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.