A simulation analysis of the impact of production lot size and its interaction with operator competence on manufacturing system performance

2014 ◽  
Vol 49 ◽  
pp. 203-214 ◽  
Author(s):  
Long Che Mak ◽  
Wai Keung Wong ◽  
Yung Sun Leung
2013 ◽  
Vol 315 ◽  
pp. 78-82 ◽  
Author(s):  
Mohd Norzaimi Bin Che Ani ◽  
Aimuni Binti Ismail ◽  
Shaliza Azreen Mustafa ◽  
Chin Jeng Feng

Cellular manufacturing system facilitates lean manufacturing in terms of production flexibility and control simplification. The paper presents a case study on a newly constructed cellular manufacturing system adopted by an electronic assembly factory as the back end process. The original rabbit chase is infeasible in this case because products handled are multi-types and multi-paths. Further, the cycle times are largely imbalance. The application of two proposed rabbit chase models was investigated through computer simulations enhanced with ANOVA and surface response methodology. The allocation of operators and the impact of changing lot size to the performances of the cell are investigated. For the findings, there are clear indications of the effects of the number of operators and the lot size for the performances of the system, regardless which rabbit chase model used.


Kybernetes ◽  
2019 ◽  
Vol 49 (5) ◽  
pp. 1533-1560
Author(s):  
Xinfeng Lai ◽  
Zhixiang Chen ◽  
Bhaba R. Sarker

Purpose The purpose of this paper is to study a production lot sizing problem with consideration of imperfect manufacturing and emergency maintenance policy, providing managerial implication for practitioners. Design/methodology/approach In this study, the authors introduce two models, where in Model I, shortages are not allowed and repair times are negligible. In Model II, shortages are allowed and are partially backlogged, and repair times are assumed to be exponentially distributed, algorithm is developed to solve the models, numerical examples were demonstrated the applications. Findings Results show that in the Model I, demand rate is the most significant parameter affecting the average expected cost, whereas the time needed to breakdown after machine shift is the most significant factor affecting the production lot size. Therefore, reduction in the time needed to breakdown after machine shift would be helpful for determining an appropriate production lot size in Model I. In Model II, repair time parameter is the most significant factor affecting the average expected cost. Reducing the value of machine shift parameter would be helpful for determining an adequate production lot size and reducing decision risk. Practical implications This paper can provide important reference value for practitioners with managerial implication of how to effectively maintain equipment, i.e. how to make product lot size considering the influence of the maintenance policy. Originality/value From the aspect of academia, this paper provides a solution to the optimal production lot sizing decision for an imperfect manufacturing system with consideration of machine breakdown and emergency maintenance, which is a supplement to imperfect EMQ model.


2021 ◽  
Vol 13 ◽  
pp. 184797902199927
Author(s):  
Firas M Tuffaha ◽  
Mohammad M AlDurgam

It is common in the integrated targeting inventory literature to assume 100% inspection. Yet, sampling inspection is still a valid alternative in numerous situations. Inspection time has been assumed negligible in the literature of integrated inventory and sampling inspection. Neglecting inspection time is unrealistic, especially when rejected lots are sent for 100% inspection. This research work integrates process targeting, production lot-sizing and inspection. Given a scenario of a producer and distributer, the objective is to determine the optimal mean setting at the producer, the production lot size to be produced and shipped to the distributor and the reorder point at the distributor under a given sampling inspection plan. To the best of the authors’ knowledge, sampling inspection and its associated costs are rarely addressed in integrated supply chain models, and have never been addressed in integrated models with controllable production rates. Numerical illustrations using an efficient solution technique are presented to highlight the impact of various model parameters. The results indicated that inspection time has a significant impact on the total cost of the developed model, especially, when tightened inspection plans are used.


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