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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 purpose of this paper is to: first, 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. Second, to quantify the cost of variation.

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.

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.

This paper provides new knowledge in the context of implementing CRISP-DM and the Taguchi loss function in the pulp and paper manufacturing process.

A control chart is a widely used Six Sigma DMAIC process measure and control phase tool. The purpose of this paper is to contribute to the body of knowledge on applying statistical process control (SPC) methods in a pulp mill production organization and the special issues that need to be considered in this context.

The method for obtaining the results was action research, where the researcher actively participated in implementing changes in organization. Procedures to detect and further handle the deviations in a pulp mill organization were created and implemented. A cause and effect diagram used in finding causes and storing the accumulated knowledge was modified to make it applicable to this environment.

Factors for successful SPC implementation were found to be in line with earlier findings in other industries. SPC can act as a means of organizational learning in the pulp and paper industry. Specific problems in the pulp and paper industry concerning the use of SPC were the autocorrelation of data, excessive measurement variation, and limited process knowledge. The effectiveness of SPC in a pulp mill was shown both in the decreasing amount of deviations and in the positive opinions of the employees.

Findings are generated from a single case, so general applicability is limited.

This case study can be used as a benchmark by other practitioners in the industry.

This article provides new knowledge in the context of implementing SPC in a pulp and paper manufacturing organization.