The resilience of a supply chain reflects its capability to withstand disruptions, adapt to changing conditions and recover its operational performance within an uncertain and dynamic environment. This capability has become essential for modern global supply chains that are continuously exposed to major risks such as natural disasters, geopolitical conflicts and pandemics. Resilience requires the implementation of cost-effective strategies to mitigate the impact of disruptions and support recovery processes. However, because resilience primarily focuses on maintaining or restoring performance following individual disruptions within a limited time horizon and a closed-system setting, it may be insufficient to address recurrent, long-lasting, and progressively escalating disruptions such as the Covid-19 pandemic. Consequently, supply chain viability has emerged as a new research paradigm that extends resilience by emphasizing long-term survivability as a strategic objective alongside profitability. In this context, the present study focuses on the design of viable supply chain networks that jointly optimize profitability and viability through the implementation of multiple resilience strategies including backup supply, supplier fortification and emergency inventory. Specifically, we propose a novel viability metric that captures long-term survivability by limiting the duration during which service performance remains below a specified target level across all disruption scenarios. To incorporate this metric into the decision-making process, we develop a bi-objective, stochastic, multi-period supply chain network design model that determines supplier selection and factory location decisions, while balancing profitability and viability objectives. A real-life pharmaceutical industry case study is then used to validate the proposed model and investigate the trade-off between profitability and viability.