PurposeManaging supply chain risk is a crucial element in ensuring the long-term sustainability of any organization or industry. As such, identification of risks and deploying their mitigation strategies should be the focal point to sustain in the long run. The risks that are faced by food processing supply chains are gaining prominence, given more consumers requiring higher quality products while ensuring traceability. In essence, this research focuses on the supply chain risks and mitigation strategies in the spice industry of an emerging economy, Sri Lanka.Design/methodology/approachThis paper integrates two popular multi-criteria decision-making (MCDM) techniques, such as the analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the supply chain risks and to derive their mitigation strategies for the spice industry.FindingsFindings show that “inability to meet quality requirements” has been established as the most significant risk in the Sri Lankan spice industry. On the other hand, “vertical integration” (backward integration) has been discovered as the key mitigation strategy to ameliorate the effects of supply chain risks in this sector.Research limitations/implicationsThis study is exploratory, and more empirical data and statistical analyses are needed to further validate the outcomes of the study.Originality/valueDespite being one of the largest trade exporters in Sri Lanka, the spice industry gets scant attention to the identification and mitigation of the risks. The authors explored the supply chain risks in the spice industry and then prioritized the suitable mitigation strategies using an integrated AHP-TOPSIS method.

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The rapid onset of the COVID-19 epidemic has brought the manufacturing process to a halt. The problem is especially serious for deteriorating products because demand for these items is not consistent and the product's worth has diminished with time. Many deteriorating product industries are now looking for an appropriate and effective disruption recovery plan to help them recover. However, a survey of the literature suggests that there has been little research done on developing an effective inventory production model for deteriorating products exposed to COVID-19 pandemic risks. This research intends to develop a disruption recovery model that considers demand as a time-dependent quadratic function to find out the optimum number of orders. Two different heuristic algorithms named: Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) have been employed to solve the model and it has been found that WOA performs better in terms of convergence. The numerical findings indicate that the price inclination rate for the component price and selling price played a pivotal role to maximize net profit. It is expected that by employing the proposed model of this research, the industry managers will be greatly benefitted to obtain quick recovery from the COVID-19 disruption risk for the deteriorating goods and retain financial stability.

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