Effects of Artificial Intelligence Integration on Supply Chain Forecasting Accuracy in Tanzania

Purpose: The aim of the study was to assess the effects of artificial intelligence integration on supply chain forecasting accuracy in Tanzania. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: A study investigating the effects of artificial intelligence (AI) integration on supply chain forecasting accuracy in Tanzania revealed significant improvements in predictive capabilities and operational efficiency. By incorporating AI technologies such as machine learning and predictive analytics into supply chain forecasting processes, businesses experienced enhanced accuracy in demand forecasting, inventory management, and resource allocation. The utilization of AI algorithms enabled the identification of patterns and trends within large datasets, facilitating more informed decision-making and reducing forecasting errors. Furthermore, AI-driven forecasting models exhibited adaptability to dynamic market conditions and provided timely insights for proactive supply chain management. Implications to Theory, Practice and Policy: Theory of technological determinism, information processing theory and resource-based view theory may be use to anchor future studies on assessing the effects of artificial intelligence integration on supply chain forecasting accuracy in Tanzania. Practitioners should collaborate with researchers to exchange insights and best practices related to AI integration in supply chain forecasting. Policymakers should collaborate with industry stakeholders to establish regulatory frameworks that promote responsible AI adoption in supply chain management.