The research aims to examine strategic planning in smart supply chains using the Internet of Things (IoT) and predictive analytics to address logistics crises. The research adopted a descriptive analytical approach, with data collected from a random sample of 100 supply chain managers and employees across the industrial (40%), commercial (35%), and logistics (25%) sectors. The data was collected using a questionnaire focusing on the Internet of Things, predictive analytics, and crisis management. The data were analyzed using SPSS to extract frequencies, percentages, means, and standard deviations. The results showed that the Internet of Things enhances product traceability (mean 4.2) and reduces inventory errors (mean 4.0), but faces the challenge of high costs (mean 4.3). Predictive analytics, on the other hand, supports proactive decision-making (mean 4.2), despite the need for advanced technical skills (mean 4.3). The results also confirmed that these technologies enhance supply chain resilience (mean 4.2), while also posing challenges related to privacy and security (mean 4.3). The findings underscore the importance of integrating these technologies to improve operational efficiency and reduce risk, while addressing costs, developing technical skills, and enhancing cybersecurity. Recommendations include increasing investment in the Internet of Things, developing training programs, improving security, incorporating predictive analytics into planning, and encouraging cross-sector collaboration to ensure effective integration and sustainable performance.
A Mon, study studied this question.