Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Collusion by Data-Driven Algorithms and its Impact on Supply Chain Performance
Xiaoyue Yan, Elena Belavina, Karan Girotra
Cornell University, United States of America
This study explores multi-agent data-driven decision-making in a supply chain with vertical competition. We find that the sample average approximation and kernel optimization approaches can tacitly collude, reduce double marginalization, improve profits for all participants while protecting consumer welfare. Besides, non-coordinating contracts can even reduce more double marginalization than coordinating ones in the data-driven setting, especially when the feature distribution exhibits skewness.
An optimistic-robust approach for omnichannel inventory management
Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna, Brian Quanz, Dhruv Shah, Chandra Narayanaswami
IBM, United States of America
We propose a novel bimodal inventory optimization (BIO) model and algorithm to position inventory across a retail chain to meet time-varying omnichannel demand. While prior Robust optimization (RO) models emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of potential positive outcomes. Experiments project a profitability gain of 15% for BIO over RO on real-life data from a major retail chain.
Understanding the sales impact of automobile features in new and used-car markets
Hojun Choi1, Ahmet Colak2, Sina Golara3, Achal Bassamboo1
1Northwestern University (Kellogg School of Management); 2Clemson University (Wilbur O. and Ann Powers College of Business); 3Kennesaw State University (Coles College of Business)
The automotive sales has focused on selling an experience or a lifestyle via features. Features are optional add-ons that increase product attractiveness (from stereo audio to cruise control) and basis for purchasing decision. While previous studies conducted car-level analysis on sales, the effect of features has remained unexplored. We examine features' collective impact on sales time along new and used-car segments, propose a two-stage estimation framework, and suggest policy recommendations.
