Effectiveness analysis of the data-driven strategy of AI chips supply chain considering blockchain traceability with capacity constraints

Blockchain technology, known for its ability to trace product information, and the added value of sales data to upstream companies under capacity constraints, are the focal points of our study. We incorporate both blockchain technology and sales data to investigate the efficacy of data-driven strategies in the context of blockchain traceability and capacity constraints within the chip supply chain. The term 'blockchain-driven strategy' refers to the adoption of blockchain-based traceability systems by manufacturers to enhance consumers' trust in the chip product information. The 'data-driven strategy' encompasses the practice of manufacturers collecting consumer purchase data related to chips to analyze consumer product preferences. Our findings reveal that both the blockchain-driven strategy and the combined blockchain and data-driven strategy are influenced by various factors, including the saturation level of service capacity in downstream companies, the saturation level of supply capacity in upstream companies, basic market demand, price competition, and service competition. These factors directly impact the profits of both upstream and downstream companies. Moreover, the unit value of demand data carries implications for wholesale prices, the level of blockchain traceability, and the profit of upstream companies. It also affects retail prices, sales service levels, and profits for downstream companies. Implementing a data-driven strategy results in increased wholesale prices and elevated levels of blockchain traceability for upstream companies, while downstream firms experience higher retail prices and improved sales service levels. When the cost of the upstream company is relatively low, adopting a data-driven strategy is advisable. In contrast, downstream companies consistently lean towards adopting data-driven strategies. We further evaluate the effectiveness of the data-driven strategy by taking into account both the implementation cost and the unit value of data.