Artificial Intelligence Technologies And Supply Chain Performance Of Manufacturing Firms In Kenya

Abstract

Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. Adoption of artificial intelligence technologies has been taunted as an enabler of organizational performance. Therefore, the current study sought to assess the level of adoption of AI technologies and their effect on the performance of supply chains of manufacturing firms in Kenya specifically in the automobile subsector. The study was based on socio technical theory and technology organization environment theory. The study adopts descriptive design targeting the seventeen automobile companies in Kenya. Census method was used to select all 153 functional officers in; Finance, Human resource, ICT, Logistics, SCM, Legal, R&D, Security and Operations since the population was small. Data was collected through use of questionnaires send via Google form, analyzed through descriptive and inferential statistics. The finding of the study is presented in tables. It’s expected that the study findings will find use among researchers, policy makers and managers of the manufacturing firms. Key findings of the study are that; all the three artificial intelligence technologies (IOT, Data analytics, Sensors and Drones) have a positive and significant influence on supply chain performance of manufacturing firms in Kenya. Besides government regulations moderating the relationship between AI technologies and supply chain performance. It is recommended that manufacturing firms need to embrace more AI embedded technologies for better supply chain responsiveness, flexibility, reliability and low operational costs. Further research needs to be undertaken on more AI tools and in other institutions so as to verify the study findings.