A data-driven approach to optimize a blood supply chain within the Industry 5.0 framework: A stochastic optimization model

Abstract

The advancement of technology, environmental concerns, and COVID-19 has driven attention toward Industry 5.0 (I5.0). However, there has been no research on blood supply chain network design (BSCND) issue using the I5.0 pillars. To cover this gap, this work develops a multi-objective model to organize a viable blood supply chain (VBSC) based on I5.0 dimensions. The concept of viability is central to our model, encompassing three crucial aspects: agility, resilience, and sustainability. In a pioneering move, this model incorporates technologies, such as Augmented Reality (AR) and Internet of Things (IoT) systems. The autoregressive integrated moving average (ARIMA) model predicts customer demand to increase resilience in this chain. Additionally, a stochastic approach is adopted to determine the maximum number of trained workers, a key input for the proposed model. This stochastic model is then transformed into a deterministic form using Chance-Constraint Programming (CCP) and solved efficiently through the Augmented Epsilon-Constraint (AEC) method. A case study conducted in Mazandaran validates the feasibility and effectiveness of the model, emphasizing the significance of sensitivity analysis and managerial insights. Noteworthy outcomes of the study include the reduction of medical errors through worker training initiatives, as well as the mitigation of expired blood and total costs through the integration of IoT and AR systems within the supply chain network.