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This study aims to determine and analyze the influence of Supply Chain Management, Service Quality, Operational Innovation, and Information Technology on Operational Performance in logistics companies in Indonesia. The type of research used is quantitative with a descriptive-verification approach. The population of this study were operational employees in national logistics companies such as JNE, J&T Express, SiCepat, and Pos Indonesia, with a sample of 100 respondents selected through a purposive sampling technique. Data collection was carried out through an online questionnaire using a Likert scale of 1–5. Data analysis was carried out using multiple linear regression using SPSS. The results of the study indicate that Supply Chain Management, Service Quality, and Information Technology have a positive and significant effect on Operational Performance, while Operational Innovation has no significant effect.

This study investigates the use of artificial intelligence (AI) to improve operational performance in Zimbabwean road haulage enterprises, with a focus on driver training as a moderator. As the logistics industry faces new difficulties, AI technologies have great promise for increasing efficiency and decision making. However, the usefulness of these technologies is determined by the skill levels of the drivers using them. This study demonstrated how extensive driver training improves the capacity to comprehend AI-generated insights, resulting in better route management, lower operating costs, and increased safety. This study examines how AI affects key performance variables such as cost savings, productivity, customer happiness, and environmental sustainability, using real data from road haulage companies. Key findings demonstrate how AI is transforming decision-making, improving operational effectiveness, and optimizing routes. The research highlights several noteworthy obstacles in addition to their obvious advantages, such as budgetary limitations, difficulty in obtaining pertinent data, and the need for more regionalized AI solutions. The findings, which are based on case studies and performance data from diverse enterprises, indicate that (i) organizations that invest in both AI and driver training benefit from a synergistic impact, resulting in higher operational outcomes, (ii) there is need to combine technical improvements with human experience to achieve maximum performance in Zimbabwe's competitive road-haulage market and finally (iii) this study offers helpful recommendations for successfully integrating artificial intelligence (AI) into haulage processes, along with insights into best practices and alternative approaches to overcome current obstacles. This study emphasizes the importance of context-specific solutions in emerging regions, enhancing the expanding corpus of knowledge on AI applications, particularly in logistics.