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

Author

Listed:
  • Boby John

    (Indian Statistical Institute)

  • K. K. Chowdhury

    (Indian Statistical Institute)

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.

Suggested Citation

  • Boby John & K. K. Chowdhury, 2021. "Minimizing the Dry Content Variation in the Pulp Drying Process Using Six Sigma Methodology," SN Operations Research Forum, Springer, vol. 2(4), pages 1-21, December.
  • Handle: RePEc:spr:snopef:v:2:y:2021:i:4:d:10.1007_s43069-021-00110-y
    DOI: 10.1007/s43069-021-00110-y
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    References listed on IDEAS

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    1. Muneeb A. Akram & Abdul-Wahid A. Saif & M. Abdur Rahim, 2012. "Quality monitoring and process adjustment by integrating SPC and APC: a review," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 11(4), pages 375-405.
    2. Boby John & S.M. Subhani, 2020. "A modified control chart for monitoring non-normal characteristics," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 29(3), pages 309-328.
    3. Lawrence, Akvile & Karlsson, Magnus & Thollander, Patrik, 2018. "Effects of firm characteristics and energy management for improving energy efficiency in the pulp and paper industry," Energy, Elsevier, vol. 153(C), pages 825-835.
    4. Nien Fan Zhang, 1998. "Estimating process capability indexes for autocorrelated data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 559-574.
    5. Douglas C. Montgomery & William H. Woodall, 2008. "An Overview of Six Sigma," International Statistical Review, International Statistical Institute, vol. 76(3), pages 329-346, December.
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