Artificial Intelligence Applications And Performance Of Logistic Companies In Kenya

Abstract

The integration of Artificial Intelligence applications in logistics has revolutionized the transport sector by enhancing efficiency, optimizing operations, and improving overall performance. In Kenya, Logistics Companies play a critical role in the movement of goods across long distances, yet they face numerous challenges, including operational inefficiencies, high costs, and inconsistent service quality. The study examined the effect of leveraging on artificial intelligence applications in promotion of performance of Logistics Companies in Kenya. In this study, a descriptive research approach was utilized, and the target group consisted of 4725 individuals. Among these individuals, there were 269 managers of long-distance transport services and 4456 drivers of long-distance vehicles. The Yamane Formula was utilized in order to determine a sample size of 376 respondents. For the purpose of selecting the respondents, stratified random sampling was utilized, in which participants from each stratum were chosen through the execution of simple random sampling. In order to collect quantitative data from both the drivers of long-distance vehicles and the management of firms that operate long-distance vehicles, questionnaires were deployed. According to the model summary, it was demonstrated that machine learning, telematics, the internet of things, and big data are capable of explaining 68.6% of the performance of Logistics Companies of long-distance vehicles. The remaining 31.4% of the performance can be described by other variables that were not included in this study. One further thing that the findings demonstrate is that the beta coefficient for machine learning was positive. The findings demonstrate that telematics possessed a beta coefficient that was both positive and significant, which is an indicator that enhanced telematics may lead to enhanced logistical performance. The beta coefficient for the internet of things was found to be positive and significant, which indicates that an increase in the utilization of the internet of things is likely to result in an improvement in the efficiency of the logistics of long-distance vehicles for transportation agencies. Last but not least, it was demonstrated that the large data had a beta coefficient that was both positive and negligible. This indicates that any change in this variable would result in a change in performance that was not substantial for the logistics of long-distance vehicles that are managed by transportation agencies. Taking into consideration these data, the researchers concluded that enhanced machine learning might potentially result in enhanced performance of transportation agency logistics for long-distance vehicles. Additionally, the findings of this study concluded that enhanced telematics could potentially result in enhanced performance of transportation agency logistics for long distance cars. In addition, the findings of this study indicate that the implementation of internet of things could potentially result in enhanced performance of transportation agency logistics for long-distance vehicles. In conclusion, the findings of this study indicate that the performance of transportation agencies in terms of the logistics of long distance vehicles is unaffected by changes in big data. This study recommends that long distance vehicles companies do not need to invest resources in big data since it does not have a major influence on the performance of Logistics Companies of long-distance vehicles.