Abstract:
Manufacturing industry is an important sector in Kenya as it makes a substantial contribution to the country’s economic development. The industry is one of the key economic pillar in the vision 2030 geared to make the nation a middle level income country by the year 2030. The agricultural and manufacturing sector recorded a significant drop in growth from 4.7% to 1.6% and 2.7% to 0.2% respectively according to the World Bank economic update 2022. The general objective of the study was to establish the influence of logistics optimization techniques on performance of manufacturing firms in Nairobi County, Kenya. Specifically, the study sought to establish the influence of fleet management on performance of manufacturing firms in Nairobi County, Kenya and to evaluate the influence of technological integration on performance of manufacturing firms in Nairobi County, Kenya. The study adopted descriptive research design. This study targeted senior management employees (1 top management employee, 2 middle level management employees and 3 lower management employees) in all the 105 firms. The total target population was therefore be 630 employees. The study’s sample size was reached at using Krejcie and Morgan sample size determination formula. The 239 respondents were chosen with the help of stratified random sampling technique. This study relied on both primary and secondary data. Primary data was collected through use of semi structured questionnaires. The study also conducted pilot test to test the validity and the reliability of the data collection instrument. The data collection instrument generated both qualitative and quantitative data. The study used both descriptive and inferential statistics for data analysis with the aid of Statistical Package for Social Sciences (SPSS version 25). Descriptive statistics such as mean, standard deviation, frequency and percentages were used in this study. In relation to inferential statistics, the study used correlation analysis. This was used to establish the relationship between the independent and the dependent variables. Data was then presented in tables, bar charts and pie charts. The study findings revealed that all techniques significantly positively impacted firm performance, with technological integration having the highest coefficient (B = 0.403, p = 0.000), followed by fleet management (B = 0.338, p = 0.000). The findings conclude that effective logistics optimization enhances operational efficiency, reduces costs, and improves overall firm performance. It is recommended that firms invest in advanced logistics technologies, improve fleet management practices to maximize these benefits and drive sustainable growth.
