Robustness analysis of pull strategies in multi-product systems

Abstract

This paper examines the behaviour of shared and dedicated Kanban allocation policies of Hybrid Kanban-CONWIP and Basestock-Kanban-CONWIP control strategies in multi-product systems; with considerations to robustness of optimal solutions to environmental and system variabilities.</p> <p><strong>Design/methodology/approach: </strong>Discrete event simulation and evolutionary multi-objective optimisation approach were utilised to develop Pareto-frontier or sets of non-dominated optimal solutions and for selection of an appropriate decision set for the control parameters in the shared Kanban allocation policy (S-KAP) and dedicated Kanban allocation policy (D-KAP). Simulation experiments were carried out via ExtendSim simulation application software. The outcomes of PCS+KAP performances were compared via all pairwise comparison and Nelson’s screening and selection procedure for superior PCS+KAP under negligible environmental and system stability. To determine superior PCS+KAP under systems’ and environmental variability, the optimal solutions were tested for robustness using Latin hypercube sampling technique and stochastic dominance test.</p> <p><strong>Findings:</strong> The outcome of this study shows that under uncontrollable environmental variability, dedicated Kanban allocation policy outperformed shared Kanban allocation policy in serial manufacturing system with negligible and in complex assembly line with setup times. Moreover, the BK-CONWIP is shown as superior strategy to HK-CONWIP.</p> <p><strong>Research limitations/implications:</strong> Future research should be conducted to verify the level of flexibility of BK-CONWIP with respect to product mix and product demand volume variations in a complex multi-product system</p> <p><strong>Practical implications:</strong> The outcomes of this work are applicable to multi-product manufacturing industries with significant setup times and systems with negligible setup times. The multi-objective optimisation provides decision support for selection of control-parameters such that operations personnel could easily change parameter settings to achieve a new service level without additional optimisations of the system parameters.</p> <p><strong>Originality/value:</strong> The examination of the behaviour of the two Kanban allocation policies in HK-CONWIP and BK-CONWIP in a complex multi-product assembly line with setup-times and environmental variabilities, under erratic demand profiles.