Thesis:

Analysis of supply chain risk management on the Performance of selected food manufacturing Firms in Kiambu County, Kenya

Unmanaged Supply Chain risks may result to huge losses for companies. Over the years many organizations including the food manufacturing companies have suffered due to low or poor risks management. There are numerous sources of risk that can arise either internally within a company or from the external business environment due to increased uncertainty in the business climate and the growing complexity of supply chains, which heightens the likelihood of disruptions. There has been a rising trend of supply chain risks in food manufacturing companies. The primary aim of this study is to investigate supply chain risk management and the performance of food manufacturing firms in Kenya. The specific objectives include determining how logistics systems impact the performance of food manufacturing firms, examining the effects of operational flexibility on their performance, establishing the influence of strategic sourcing, and analyzing the impact of supply chain re-engineering on the performance of food manufacturing firms in Kiambu County, Kenya. The theories that support this study are System theory, Contingency Theory, Strategic Choice Theory and Complex Adaptive System Theory. The study targets Population will be 191 employees from 29 selected food manufacturing firms in Kiambu County, Kenya and 20 outlets. The employees will consist of managing directors, productions and operations managers, procurement managers and logistics managers as the key respondents. Since the population is small a census shall be done. The research approach will entail a descriptive design. A preliminary investigation involving 10 food manufacturing firms in Murang’a County will be carried out to evaluate the reliability and validity of the measuring instruments. The reliability of the study instrument will be assessed using Cronbach's alpha, with a threshold of 0.7 indicating satisfactory reliability. The study will primarily gather quantitative data, which will be analyzed using various statistical methods. Response rates will be determined, and descriptive statistics such as mean, median, standard deviation, and proportions will be computed using Statistical Package for Social Sciences (SPSS) version 28. Both qualitative and quantitative methodologies will be employed to analyze the collected data. Content analysis will be utilized for qualitative data, while descriptive statistics like frequencies, percentages, mean, and standard deviation will be employed for quantitative data. Additionally, inferential statistics such as Pearson Correlation and multiple regression analysis will be conducted to examine relationships between variables. Specifically, the Pearson Correlation coefficient will be utilized to ascertain the associations among variables.