Waveless Picking in Supply Chain Performance of Food and Beverages Processing Firms in Kenya

The purpose of this study was to analyze the role of waveless picking in supply chain performance of food and beverages manufacturing firms in Kenya. The research concentrated on the 134 food and beverage manufacturers that are operating in Nairobi City County besides being registered with Kenya Association of Manufacturers. The study adopted a mixed research design with both qualitative and quantitative approaches. The target population of the study was the 134 food and beverages manufacturing firms in Nairobi County. A sampling frame of this study included a list of the 134 manufacturing companies in Nairobi County that are members of the Kenya Association of Manufacturers. The study utilized simple random sampling. A sample size of 100 was selected with the aid of Yamane 1967 formula. Both primary and secondary data was collected using a questionnaire. The questionnaire was tested pilot at 10 food and beverages manufacturing companies in Kiambu county. These pilot study questionnaires were filled out by warehouse managers. The statistical package for social sciences (SPSS) version 25 was used to analyze the data. Using content analysis, the qualitative data was analyzed. Quantitative data was analyzed using statistical methods involving descriptive and inferential data. A multiple linear regression model was applied to analyze the relationship between the variables. Analysis was also performed on the correlation. In this study, the findings were presented using tables and graphs. Data presentation made use of percentages, frequencies, means and other means of central tendencies. The study on revealed several ways to improve operational efficiency and productivity. Most enterprises have not reduced warehouse travel time, indicating potential for improvement. The study recommended reducing warehouse travel time, implementing batch picking, designating picking zones, increasing product forecasting accuracy, improving cash flow management and supply chain scheduling, accepting technology and automation, and promoting continuous learning and development.