Diagnosis and economic impact of operational variability: A case from the chemical forest industry

Jukka Rantamäki, Olli Saarela

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    Purpose: This paper deals with the identification and diagnosis of operational variability in chemical processes, which is a common problem in mills but little explored in literature. The CRoss-Industry Standard Process for Data Mining (CRISP-DM) is a widely used approach in problem solving. The first purpose of this paper is to contribute to the body of knowledge on applying CRISP-DM in a pulp mill production process and the special issues that need to be considered in this context. Exact amounts of a cost increase due to variation in pulp production have not been reported previously. The second purpose of this paper is to quantify the cost of variation. Design/methodology/approach: In the case studied, the variation in a pulp mill batch cooking process had increased. In order to identify the causes of variation, CRISP-DM was applied. Findings: The cycle of variation was identified and found to be related to the batch cooking process cycle time. By using information from this analysis it was possible to detect otherwise unobserved defective steam nozzles. The defective equipment was repaired and improved. Further improvement was achieved when the fouling of a heat exchanger was found by analysis to be the root cause of long-term variability parameters. By applying CRISP-DM, equipment defects and fouling were identified as the root causes of the higher manufacturing costs due to increased variation were detected and estimated. The Taguchi loss function is a possible tool for estimating the cost of variation in pulp manufacturing. Originality/value: This article provides new knowledge in the context of implementing CRISP-DM and the Taguchi loss function in the pulp and paper manufacturing process.
    Original languageEnglish
    Pages (from-to)294-309
    JournalJournal of Quality in Maintenance Engineering
    Volume21
    Issue number3
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Keywords

    • operational variability
    • diagnosis
    • data analysis
    • economic impact

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