Big Data Analytics and Supply Chain Performance of Network Facilities Providers in Kenya

Abstract

Big data analytics (BDA) has become a very important concept in light of the ever increasing generation of data in the world. BDA has various applications one of which is supply chain performance (SCP) through techniques such as demand forecasting, trend analysis and customer behaviour analysis among others. This research studied BDA and SCP of network facilities providers in Kenya. The study‟s objectives were; to identify to what degree network facilities providers in Kenya are using BDA in supply chain; to determine the relationship between BDA adoption and SCP of network facilities providers in Kenya and to ascertain challenges faced by network facilities providers in Kenya in adoption of BDA in their supply chain.The study used descriptive cross-sectional research design and collected primary data using web-based questionnaire which were administered via mail to the entire target population of 57 network facilities providers in Kenya. The number of responses received was 39 which formed a substantial portion of the populations and thus analysis was comprehensively done using descriptive statistics and multiple regression analysis. Results from the study suggest that network facilities providers in Kenya have adopted BDA in their supply chain to a moderate extent with descriptive analytics adopted the most. Further, it was found that there is a positive relationship between BDA and SCP though to a very low extent. BDA affects SCP attributes; reliability, agility and cost by 24.7%, 27.7% and 14.2% respectively. While the dimensions of BDA were jointly found to be good predictors of reliability and agility, they were statistically insignificant on cost. BDA was found to be time consuming and there was evidence that lack of top management commitment (TMC) and limited resources were the biggest challenges. Lack of expertise was the least experienced challenge among the network facilities providers. The research recommends more investment on powerful, faster and sophisticated computers and technologies to aid swift capturing, storing and processing of big data to solve the time consumption dimension of BDA. This can be done by top management‟s improved commitment and thus allocation of resource to the integration of BDA in SCM. The limitation of the study is that it adopted a simplistic multiple regression model to establish the association between BDA and SCP yet there are other variables that exist which affect this relationship and ought to be included in the analysis. Future research needs to improve this model to give it stronger explanatory power by including TMC as a mediating, mediated of intervening variable. Top management commitment comes out as a major challenge in this study and it therefore, needs to be explored further in light of the role of institutional pressure towards enhancing a firm‟s BDA adoption.