This study develops a multi-stage stochastic program to determine optimal selection of suppliers, sourcing strategy, and order allocation in a multi-period supply chain planning under operational and disruption risks. Option contract and spot market are considered as two alternatives for sourcing a critical raw material under an uncertain decision-making environment. Final products’ demand and the material's market price are stochastic, and capacity of unreliable suppliers varies randomly because of possible disruptions. For uncertainty modeling, discrete scenarios are generated via a simulation approach and then, a scenario reduction technique is used to construct a suitable scenario tree. To obtain a resilient planning, fortification of suppliers and option contract are exploited as mitigation strategies. Further, the level of supply chain's risk is quantified through a risk measure and limited by risk constraints. Computational results are presented on a real-life supply chain including multiple mines as suppliers and zinc-smelting plants that produce zinc ingots and powder. Using the computational studies and sensitivity analysis, the applicability of the stochastic model, the performance of risk-measurement policies, and the importance of mitigation strategies are investigated to drive some managerial insights.