Descriptive Supply Chain Analytics and Performance of Large Manufacturing Firms in Kenya

Purpose: This study examined the influence of descriptive analytics on the performance of large manufacturing firms in Kenya, focusing on inventory situation reporting, demand context visualization, and purchase pattern analysis. Methodology: Anchored on resource dependence theory, the study adopted a mixed-methods research design. Mixed-methods research design integrates both qualitative and quantitative research approaches in the processes of data collection, analysis, and presentation. The target population for this study was 461 large manufacturing firms. Data was collected from a sample of 210 respondents out of a sample of 222, representing a (94.5%) response rate. Reliability and validity were confirmed through Cronbach's alpha (α > 0.70), KMO, and Bartlett's tests. Findings: Descriptive analytics constructs were widely practiced, with inventory situation reporting and purchase pattern analysis being the most embraced. Approximately 59% of respondents rated descriptive analytics as effective in improving performance of large manufacturing firms. Regression analysis confirmed a positive and significant relationship (R = 0.660, R² = 0.436, Beta = 0.734, p < 0.001), indicating that 43.6% of performance variation can be explained by descriptive analytics. Unique Contribution to Theory, Policy and Practice: This study serves as a foundational reference point for practitioners and policymakers alike. By delineating the pathways through which analytics enhance transparency and competitiveness. Policy makers and regulatory agencies must mandate that firms establish rigorous guidelines for the selection and utilization of analytic tools. The study establishes that sustainable performance is contingent upon the synergistic application of the analytical pillars, which should be institutionalized as follows: descriptive analytics: utilizing historical data to establish operational baselines and performance visibility.