The Just-in-Sequence (JIS) approach is evidencing advantages in efficiently managing variety-driven costs, and reducing the risk of disruption in sourcing, manufacturing companies and third-party logistics. This has increased its implementation in the manufacturing industry, especially in highly customized manufacturing sectors such as the automotive industry. However, despite its growing interest by manufacturers, scholarly research focused on JIS still remains limited. In this context, little has been done to study the effect of JIS on the fluidity of supply chains and processes of logistics suppliers as well as providing them with a decision making tool to optimise the sequencing of their deliveries. Therefore, the aim of this paper is to propose a genetic algorithm to evaluate different decision policy scenarios to reduce risks of supply disruptions at the final assembly line. Consequently, an algorithm considering a periodic review of the inventory that assumes a steady demand and short response times is developed and applied. Design/methodology/approach: Based on a literature review and real-life information, an abductive reasoning was performed and a case study application of the proposed algorithm conducted in the auto-industry. Findings: The results obtained from the case study indicate that the proposed genetic algorithm offers a reliable solution when facing variability in safety stocks that operate under assumptions such as: i) fixed costs; ii) high inventory turnover; iii) scarce previous information available concerning material requirements; and iv) replenishment services as core business value. Although the results are based on an auto-industry case study, they are equally applicable to other global supply chains. Originality/value: This paper is of interest to practitioners and academics alike as it complements and supports the very limited scholarly research on JIS by providing manufacturers and 3PL suppliers competing in mass customized industries and markets a tool to support decision-making. Implications for the design of modern supply chain fluidity in the manufacturing industry are also exposed and future research streams presented.