Behavioral Simulation of Blockchain-Enabled Market for Supplier Capacity Trading among Retailers

Abstract

We study a supply chain distribution system and investigate experimentally operations of markets where retailers can trade digital claims (tokens) on the supplier’s capacity. Subjects play the role of retailers, have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier’s capacity. Following demand realization, retailers trade tokens with each other in markets implemented as double-sided, single-price, blind, batch auctions. We compare six behavioral treatments, featuring two wholesale prices and three market sizes. As expected, markets reduce leftovers and shortages. Interestingly, market-clearing prices are anchored to wholesale prices and do not signal the value of goods in large markets. Players deploy novel ordering and trading strategies that differ from the transshipment literature. We identify strategies by applying unsupervised machine learning algorithms. In one strategy, players buy a few claims, and after demand realization, use the market to satisfy it. Other players buy more claims than the maximum demand, and once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty but expose players to liquidity and mistakes risks. A third strategy in which players order from the supplier initially as if expecting the market to be cleared cooperatively is more profitable. This strategy diversifies demand and market risks. The introduction of markets causes “pull-to-the-mean” effect and increases order variability. Thus, markets can cause the Bullwhip Effect. Retailers’ and the supply chain’s average profits are higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. 

(joint work with V. Babich of Georgetown University and Daniel Hellwig and Kai Wendt of WHU – Otto Beisheim School of Management). 

Accepted in Management Science.