Ordering Decisions with an Unreliable Supplier under the Carbon Cap-and-Trade System

Abstract

The global focus on carbon reduction has intensified, prompting numerous high-energy-consuming enterprises to venture into the carbon cap-and-trade system. However, in recent years, the emergence of destabilizing factors has introduced disruptions to supply chains. The study addresses the two-stage ordering problem for a manufacturer under the carbon cap-and-trade system. In the first stage, the manufacturer engages in green investments and places orders with both an unreliable and a reliable supplier. After updating demand forecast information in the second stage, orders are placed with the backup supplier, and carbon allowances are settled at the end of the period. Under these conditions, three supply scenarios of the unreliable supplier are considered: time-varying supply with imperfect demand updates, all-or-nothing supply with imperfect demand updates, and time-varying supply with perfect demand updates. Optimal ordering decisions are provided for each scenario. We find that when demand updates are imperfect, the manufacturer will invariably engage with the unreliable supplier. However, when demand updates are perfect, the manufacturer may choose to forgo the unreliable supplier. Next, we analyze the influence of carbon trading prices on ordering decisions in these scenarios. We find that when the probability of disruption is substantial, dual sourcing must exist in the first stage under the all-or-nothing supply. Finally, we conduct numerical analysis by utilizing parameters, such as carbon trading prices, as referenced in the existing literature. Through numerical analysis, we find that opting for the all-or-nothing supplier becomes economically advantageous for the manufacturer when the backup supplier is profitable. Conversely, when the backup supplier is not profitable, the manufacturer tends to opt for the unreliable supplier with time-varying supply. Moreover, optimal profit for the manufacturer is not achieved when demand updates are perfect.