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

We study a supply chain distribution system (comprising a supplier and multiple retailers) and investigate experimentally how the trading by retailers of digital claims (tokens) on the supplier's capacity affects supply-demand mismatches. Creating markets for such tokens relies on advances in blockchain technology. Subjects play the role of retailers, who have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier's capacity. Following demand realization, retailers trade these tokens with each other. We conduct six behavioral experiments (featuring two wholesale prices and three market sizes) and find that markets reduce excess inventory and shortages. 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 transshipper strategies, as studied in the literature. Applying unsupervised machine learning algorithms, we classify these strategies. In one strategy, players, whom we call spot buyers, buy a few claims initially, and after demand realization, use the market to satisfy it. Other players, whom we call spot sellers, buy more claims initially than the maximum demand, and once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty. Despite this benefit, the transshipper strategy is more profitable, due to lower exposure to liquidity shocks and trading mistakes. With markets, the initial orders to the supplier exhibit the pull-to-the-mean effect, and at the same time, have greater variability because of spot buyers and sellers. Retailers' average profit is higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. Our results provide guidance for the introduction of blockchain-enabled markets in practice