Joint production and demand planning in reverse logistics with blockchain influences

Purpose This paper aims to formulate a joint production and pricing model within a blockchain-enabled reverse logistics system. The authors’ analysis reveals that the optimal production quantity follows a benchmark-type policy: it decreases with the inventory level, while the optimal planned mean demand increases with inventory. These structural results are derived through theoretical analysis and further validated through numerical simulations. Design/methodology/approach This paper develops a joint production and pricing model in a blockchain-enabled reverse logistics system. The analysis reveals that the optimal production quantity follows a benchmark-style policy – decreasing with inventory – while the optimal planned mean demand increases with inventory. The authors examine how three blockchain-influenced factors – demand multiplier, demand variability and reverse logistics cost – affect firm decisions and expected profit. Findings Numerical results show that a higher demand multiplier enhances profitability and justifies greater production. In contrast, increased demand variability, as may arise from blockchain-induced uncertainty, reduces expected profit but leads to higher production as a hedge. Higher reverse logistics costs prompt the firm to reduce production, lowering profits. Research limitations/implications Notably, the planned mean demand exhibits nonmonotonic behavior across these dimensions, reflecting the complex pricing trade-offs under blockchain dynamics. The findings highlight both the opportunities and operational risks introduced by blockchain. Originality/value This paper formulates a joint production and pricing model within a blockchain-enabled reverse logistics system. The authors analysis reveals that the optimal production quantity follows a benchmark-type policy: it decreases with the inventory level, while the optimal planned mean demand increases with inventory. These structural results are derived through theoretical analysis and further validated through numerical simulations.