This study examines a supply chain optimization model for perishable products, addressing the challenge of demand uncertainty through a hybrid approach that combines blockchain technology with multi-criteria decision-making (MCDM) methods, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The model aims to address dual objectives: reducing waste and managing inventory effectively. A crucial aspect of the model is the selection of suppliers, which is vital for mitigating risks related to product failures and delivery issues. Supplier performance management is incorporated to ensure that vendor choices meet various performance criteria. To account for demand uncertainty, the study employs chance constraint programming (CCP) to develop optimization models that incorporate uncertainties and randomness. The research evaluates two scenarios: one with blockchain technology and one without. Blockchain enhances information sharing between manufacturers and retailers, thereby reducing demand uncertainty and improving forecast reliability. The model is validated through a real-world case study of a dairy product manufacturer, with results obtained using the CPLEX solver. Findings reveal that blockchain implementation significantly reduces costs associated with holding, shortages, and production while also improving overall inventory management. Sensitivity analysis is performed to assess the impact of confidence levels on model performance, showing that higher confidence levels result in reduced costs. The study demonstrates the potential of combining blockchain, TOPSIS, and chance constraint programming to enhance supply chain efficiency and suggests future research directions, including the integration of sustainability factors into green production systems.

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