Adoption of blockchain technology in a two-stage supply chain: Spillover effect on workforce

Although blockchain technology (BCT) has been applied to supply chain practices, the theoretical understanding of its impact is limited. Motivated by the practice of an apparel supply chain, we study the application of BCT in a supply chain model to enable the effective and efficient implementation of trade credit (called “smart credit”) and demand information transparency. In this model, a retailer (“she”) and a supplier (“he”) manage their own inventories to satisfy autocorrelated customer demands and fill downstream orders during a planning horizon; the supplier strategically plans the number of full-time workers for manufacturing workforce consumption before the horizon begins. By analyzing this model, we try to address BCT’s operational value to supply chain firms and the spillover effect on the supply chain workforce. The spillover effect is expected to be negative because the BCT will replace human workers. We find that BCT’s operational value to the retailer is positive because the smart credit benefits her; however, the value to the supplier may not be positive because the smart credit hurts him and information transparency improves. The condition for a positive value depends on the type of demand autocorrelation: when the demands are positively correlated, the condition is simple, namely, the autocorrelation coefficient is higher than a threshold; when the demands are negatively correlated, the condition becomes complicated. BCT’s spillover effect on the supply chain workforce also differs according to the type of demand autocorrelation. Contrary to intuition, we find that the spillover effect can be positive (i.e., more full-time workers are hired) when demand is negatively correlated. The underlying reason is that the supplier updates his manufacturing process under BCT and this updated process’ variability can be amplified when the demands are negatively correlated; this leads the supplier to hire more full-time workers to hedge against the increasing uncertainties in the manufacturing workforce consumption. Finally, we conduct a numerical study based on a dataset collected from the apparel supply chain to provide more managerial insights.