2016•International Journal of Reliability Quality and Safety EngineeringRequires access

Productivity Improvement in the Chassis Preparation Stage of the Amplifier Production Process: A DMAIC Six Sigma Methodology

Anshu Gupta, Pallavi Sharma, Suresh Chander Malik, Neha Agarwal, P. C. Jha

Open publisher page 15 citations

Abstract

Manufacturing in India is witnessing a wave of growth, which is required to be supported by measures to increase productivity. Improved technology and infrastructure, design and process innovation, skill development, quality improvement and waste minimization are some of the ways that can be adopted to achieve this goal. At the same time demand for higher value at reduced price is also increasing on the consumer front. As a result, manufacturers are increasingly adopting quality improvement techniques to improve productivity and quality, reduce waste and thereby providing higher value at moderate cost. The data driven Six Sigma quality improvement methodology provides a framework to identify, eliminate and control the causes of variation in an operational process. In this paper, we study the chassis preparation process of an amplifier production process and implement the Six Sigma DMAIC model to identify the causes of variation, suggest and implement measures for improvement, and establish control measure to control the process performance post implementation stage. The implementation of the DMAIC methodology improved the process performance and provided measure to maintain dependable quality in the process.

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Manufacturing in India is witnessing a wave of growth, which is required to be supported by measures to increase productivity. Improved technology and infrastructure, design and process innovation, skill development, quality improvement and waste minimization are some of the ways that can be adopted to achieve this goal. At the same time demand for higher value at reduced price is also increasing on the consumer front. As a result, manufacturers are increasingly adopting quality improvement techniques to improve productivity and quality, reduce waste and thereby providing higher value at moderate cost. The data driven Six Sigma quality improvement methodology provides a framework to identify, eliminate and control the causes of variation in an operational process. In this paper, we study the chassis preparation process of an amplifier production process and implement the Six Sigma DMAIC model to identify the causes of variation, suggest and implement measures for improvement, and establish control measure to control the process performance post implementation stage. The implementation of the DMAIC methodology improved the process performance and provided measure to maintain dependable quality in the process.

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Available abstract

Manufacturing in India is witnessing a wave of growth, which is required to be supported by measures to increase productivity. Improved technology and infrastructure, design and process innovation, skill development, quality improvement and waste minimization are some of the ways that can be adopted to achieve this goal. At the same time demand for higher value at reduced price is also increasing on the consumer front. As a result, manufacturers are increasingly adopting quality improvement techniques to improve productivity and quality, reduce waste and thereby providing higher value at moderate cost. The data driven Six Sigma quality improvement methodology provides a framework to identify, eliminate and control the causes of variation in an operational process. In this paper, we study the chassis preparation process of an amplifier production process and implement the Six Sigma DMAIC model to identify the causes of variation, suggest and implement measures for improvement, and establish control measure to control the process performance post implementation stage. The implementation of the DMAIC methodology improved the process performance and provided measure to maintain dependable quality in the process.

Key concepts: DMAIC, Six Sigma, Design for Six Sigma, Manufacturing engineering, Productivity, Production (economics), Quality (philosophy), Process (computing)

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