Enhancing container terminal performance through an integrated SPC-DMAIC methodology: an empirical case study

Purpose This paper examines how Statistical Process Control (SPC) can support the monitoring and control of container ship loading and unloading operations by embedding multivariate process monitoring within the Define Measure Analyse Improve Control (DMAIC) improvement framework. This paper aims to demonstrate how established SPC principles can be coherently operationalised to address unproductive time in complex port service environments. Design/methodology/approach The study adopts a single-case research design based on operational data collected from a major Spanish container terminal. A multivariate Hotelling’s T² control chart is applied during Phase I analysis to jointly monitor vessel turnaround time, number of containers handled and crane movements. These SPC tools are systematically integrated within the DMAIC cycle to structure problem definition, measurement, analysis and improvement planning in a real operational setting. Findings The results show that multivariate SPC, when integrated within DMAIC, offers enhanced diagnostic insight compared with univariate monitoring by capturing the joint behaviour of interdependent operational variables. The Phase I analysis reveals statistically identifiable sources of variability that align with documented operational disruptions and planning deficiencies, thereby supporting more informed root cause analysis and prioritisation of improvement actions. The findings illustrate the feasibility and practical value of the proposed approach, while remaining confined to exploratory, single-case evidence. Practical implications The framework provides actionable guidance for port managers seeking to enhance process visibility, detect operational instability and support data-driven continuous improvement. By linking multivariate SPC signals to DMAIC-based decision processes, the study offers a structured approach for managing variability in vessel service operations under real-world constraints. Originality/value This study contributes to the Lean Six Sigma (LSS) literature by integratively adapting and operationalising established SPC and DMAIC principles within a complex maritime service context. The paper demonstrates how multivariate SPC can be coherently embedded within DMAIC to support process monitoring in container terminal operations.