International Journal of Industrial and Manufacturing Systems Engineering
Kanban, Discrete-Event Simulation, Optimization, Production Performance
Kanban is credited as a major means to controlling the inventory within a manufacturing system. Determining the optimum number of Kanban is of great interest for manufacturing industries. To fulfill this aim, an integrated modelling approach using discrete-event simulation technique and Kanban Lean tool is developed for a pull system ensuring an optimum Kanban number. This research has developed a base-case simulation model which was statistically validated using ANOVA. Initial Kanban number obtained from the mathematical model of Toyota motor company is used to obtain initial results. A Kanban integrated simulation model is developed that employed the idea of pull system that required the arrival of a customer for a product and Kanban pair to proceed through the production steps. The Kanban-Simulation integrated model is further used to test the effect of different Kanban numbers to obtain the best value of Kanban which is selected as 275. This approach has been applied on a case company involved in the manufacturing of agricultural and construction metal hand tools. The optimum Kanban number is selected by simulating the model about three performance indicators: customer waiting time, weekly throughput, and Work-in-progress. The analysis of the results obtained from the proposed integrated Kanban-simulation model showed a 76.7% reduction in the inventory level. The integrated Kanban-simulation model has also given a minimum customer waiting time of 0.84 Hrs. and a maximum throughput value of 737 Pcs of shovels. The integrated Kanban-simulation model is useful for manufacturing industries working to avoid overproduction waste and greatly reduce inventory costs.
Mullisa, Angassu Girma and Abdul-Kader, Walid. (2022). Optimal Kanban Number: An Integrated Lean and Simulation Modelling Approach. International Journal of Industrial and Manufacturing Systems Engineering, 7 (1), 17-24.