Decision Making Approaches and Operational Performance of Large Manufacturing Firms in Nairobi County

Abstract

The decision-making processes and operational effectiveness of large industrial enterprises in Nairobi County were the main subjects of this study. The study's goals were to iassess ithe iextent ito iwhich large manufacturing companies in Nairobi County use decision-making techniques when making operational decisions and to ascertain the impact of these approaches ion ithe ioperational iperformance iof imanufacturing companies in Nairobi County. The study employed a idescriptive icross-sectional iresearch approach and conducted a survey. Data were gathered from operations managers, production managers, or their counterparts at significant manufacturing enterprises in Nairobi City County using web-based google form surveys. 46 people were included in the sample, stratified by manufacturing subsectors. To individual managers, all of the surveys were distributed by email and WhatsApp. There were forty responses in total, and it was decided that they could be analyzed. Both descriptive and inferential statistics were employed in the study's analysis. According to the respondents' background data, we had more men than women working in the targeted departments. The respondents were in a good position to supply the information the researcher was looking for because they had a decent degree of education and had worked for the individual companies for long periods of time. Findings show that significant manufacturing enterprises in Nairobi City County apply the identified decision-making processes to a moderate to a considerable level, as indicated by three or more. Dependent decision-making models are the most often employed, whereas avoidant decision-making models are the least. The second goal was to investigate the connection between large manufacturers' operational success and their decision-making processes. According to the study's multiple regression model's positive coefficients, the dependent and rational decision-making techniques and operational performance are positively correlated. Though the latter was not statistically significant, it was discovered that intuitive and avoidant decision-making processes had a negative association with operational performance. The methods used to make decisions have a greater overall impact on operational performance. The study recommends that manufacturing firms avoid using an intuitive decision-making approach but instead rely on multi-criteria methods by employing methods and tools available for aiding decision-making. They could also use ia igroup idecision isupport isystem i(GDSS), ian iinteractive icomputer-based tool that helps ia inumber iof idecision-makers i(working itogether iin ia igroup) discover answers to situations that are inherently unstructured, to improve dependent decision-making. The study's main shortcoming is that it used a simple multiple regression model to determine the relationship between decision-making strategies and operational performance, despite the fact that there are other factors that can influence this relationship and should be considered in the research. Future studies should strengthen this model by integrating environmental dynamism or complexity as a moderating or intervening variable to offer it greater explanatory power