Supply Chain Analytics and Resilience in Roofing Sheet Manufacturing Firms in The Nairobi Metropolitan Area, Kenya
Abstract
The global and local supply chains have faced many challenges, ranging from economic fluctuations to global pandemics. Political instability worsened Kenya’s situation, resulting in a decline in manufactured goods exports to EAC partners. One of the most affected sub-sectors is roofing sheet manufacturing, hence the need to bolster supply chain resilience (SCR) among the players in the sub-sector. The study sought to examine the influence of demand analytics, logistics operations, inventory analytics, and supplier-related risks analytics on the supply chain resilience of roofing sheet manufacturing firms in NMA. Further, the study was anchored in Knowledge-based View, Systems Theory, Resource-based View, and Technology Acceptance Model. The study employed the cross-sectional survey research design and used a target population of 324 employees including 54 managers and 270 employees within procurement department (unit of observation) drawn from all 54 roofing sheet manufacturing firms in NMA (unit of analysis). Data were collected using a structured questionnaire designed in line with the independent and dependent variables. A pilot test involving 18 respondents assessed the instrument’s validity and reliability. Data obtained from responses were coded and analyzed using SPSS Version 29. The data was analyzed and visualized using descriptive statistics, including mean and standard deviation. Correlation analysis was used to determine the strength and direction of the linear relationship between variables in the matrix. Regression analysis was used to find out how changes in the independent variable affect the dependent variable. The findings revealed that demand forecasting, logistics operations analytics, inventory management analytics, and supplier-related risk analytics significantly influence supply chain analytics in roofing sheet manufacturing firms within the NMA. Logistics operations had the greatest influence on supply chain resilience (β = 0.142, p < 0.05), followed by demand forecasting (β = 0.093, p < 0.05), inventory management analytics (β = 0.035, p < 0.05), and supplier-related risk analytics (β = 0.033, p < 0.05). The study concluded that supply chain analytics influences supply chain resilience. The study also recommends that firms effectively integrate supply chain analytics into their operations to enhance supply chain resilience.
Keywords: Supply Chain Analytics, Resilience, Roofing Sheet Manufacturing Firms, Nairobi Metropolitan Area, Kenya
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