Minimizing the Dry Content Variation in the Pulp Drying Process Using Six Sigma Methodology

Boby John () and K. K. Chowdhury
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Boby John: Indian Statistical Institute
K. K. Chowdhury: Indian Statistical Institute

SN Operations Research Forum, 2021, vol. 2, issue 4, 1-21

Abstract: Abstract Industries need to continuously improve their processes to survive and grow. Six Sigma has become a widely popular methodology for continuously improving process performances. This paper is a case study on reducing the dry content variation in the pulp drying process using the Six Sigma methodology. The process was not able to meet the specification on dry content. Through brainstorming, the various potential factors are identified. The important factors are then shortlisted through gemba investigation and using statistical tools. The analysis showed that the dry content is autocorrelated and also depends on dryer temperature. Hence, the integrated EPC-SPC methodology is suggested as the solution. The solution methodology consists of a dynamic regression model to forecast the dry content and a control chart to monitor the residuals. The suggested solution is to forecast the dry content for the upcoming period and adjust the temperature if the forecasted value is not on or close to the target. At the end of every period, the difference between actual and forecasted dry content is plotted on the residual control chart, and actions are taken whenever necessary. The implementation of the solution resulted in increasing the process capability indices Cp from 0.34 to 1.21 and Cpk from 0.24 to 1.15. This study demonstrates the usefulness of the Six Sigma methodology for improving processes with autocorrelated performance characteristics and integration of EPC-SPC methodology within the Six Sigma framework for problem-solving. The approach can be generalised to solve problems of chemical industry processes with autocorrelated performance characteristics.

Keywords: Dry content; Pulp drying process; Integrated EPC-SPC methodology; Dynamic regression; Economically weighted moving average control chart (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s43069-021-00110-y

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