Supply Chain Management Practices And Performance Among Public Research Institutions In Kenya

Abstract

Supply chain management (SCM) is identified as a strategic tool to firms to improve organizational performance and secure competitiveness. The study had three objectives to achieve: to establish the extent of supply chain management practices implementation among public research institutions in Kenya, to determine the impact of SCM practices on the performance of research institutions in Kenya and to evaluate the challenges faced by public research institutions in Kenya while adopting SCM . The research design involved a descriptive research of public research institutions in Kenya. Data was collected using questionnaire through drop and pick later method. Tables were used to analyze all the three objectives. The findings were presented on tables. The following were the major findings obtained from the data analysis. Regarding SCM practices, it was found that while a number of SCM best practices had been adopted and implemented to a great extent, majority of the practices have been implemented to a moderate extent. It is worth noting that involvement of major suppliers in planning; an important best practice had been implemented only to a small extent. Thus few best practices had been fully implemented with majority still lagging behind. Regarding the relationship between SCM practices and firm performance, three variables out of the seven, namely logistics, lean suppliers and information technology were found to have strong statistically significant relationships with performance. The other three variables, namely Green supply chain practices, Long term supplier relationships and outsourcing were found to have weak relationships which were not statistically significant. The present study used only publicly funded research institutions, future studies should consider expanding their scope to include other private research institutions. It would be interesting to see whether similar results can be obtained using quantitative measures.