Seminar by Professor Suresh P. Sethi

【Date & Time】
Monday, October 31st, 2022 / 2pm - 3pm (JST)
ISM Tachikawa Campus Seminar Room 5 (D313 on the 3rd floor) + Zoom (Hybrid seminar)
Please register at the following google form to receive the zoom link:
Professor Suresh P. Sethi (University of Texas at Dallas)
Hierarchical and Mixed Leadership Games for Dynamic Supply Chains: Applications to Cost Learning and Co-op Advertising
We consider two applications of dynamic stochastic supply chains. The first application is a decentralized two-period supply chain in which a manufacturer produces a product with benefits of cost learning, and sells it through a retailer facing a price-dependent demand. The manufacturer’s second-period production cost declines linearly in the first-period production, but with a random learning rate. The manufacturer may or may not have the inventory carryover option. We formulate the problem as a two-period Stackelberg games and obtain their feedback equilibrium solutions explicitly. We then examine the impact of mean learning rate and learning rate variability on the pricing strategies of the channel members, on the manufacturer’s production decisions, and on the retailer’s procurement decisions. We show that as the mean learning rate or the learning rate variability increases, the traditional double marginalization problem becomes more severe, leading to greater efficiency loss in the channel. We obtain revenue sharing contracts that can coordinate the dynamic supply chain. The second application studies a novel manufacturer-retailer cooperative advertising game where, in addition to the traditional setup into which the manufacturer subsidizes the retailer's advertising effort, we also allow the reverse support from the retailer to the manufacturer. This is modeled as a mixed leadership game in which one player is a leader on some decisions and a follower on other decisions. We find an equilibrium that can be expressed by a solution of a set of algebraic equations. We then conduct an extensive numerical study to assess the impact of model parameters on the equilibrium.