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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/5170
Title: RESEARCH TITLE: PRODUCTIVITY IMPROVEMENT TECHNIQUES USING STATISTICAL PROCESS CONTROL IN DAIRY INDUSTRY CASE OF SEBETA AGRO-INDUSTRY
Authors: TESFAYE, DEMEKE
Keywords: SPC tools, Pareto chart, cause-effect diagrams
control charts, histograms, process performance, Continuous improvement.
Issue Date: Jun-2019
Publisher: st.mary's University
Abstract: This thesis addresses the concepts of productivity improvement techniques and application of SPC tools in dairy industry.SPC is one the most widely applied technique to control and improve processes in dairy industry, but very few studies have reported on the successful application of SPC in dairy industry. This thesis aims to critically assess the status of SPC in the SAI dairy processing and suggests for future researches. Now a day to survive in competitive market, improving productivity and reducing quality problems products is a must for any company. We need to have better understanding of quality. By improving the quality, the method of optimization reduces process operational cost and product variation. This study is to apply quality control tools in production process to reduce souring, returns, breakages, and damages by identifying where highest souring occur at and to go give suggestions for improvement. The approaches used in this study are Histogram, Check sheet, Pareto diagram, Fish bone diagram, control chart, which have been applied to improve productivity and quality of products and minimization of quality defects. It has been found that the company has many quality problems especially there is highly souring in the production process line. There is a various process parameters such as acidity, temperature, fat, volume, which have influence of the quality of the final products have been controlled in order to reduce defects and there have been observed a need of improvement by using SPC tools.
URI: .
http://hdl.handle.net/123456789/5170
Appears in Collections:Quality And Productivity Management

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