A comparison of Kanban-Like control strategies in a multi-product manufacturing system under erratic demand

Onyeocha, Chukwunonyelum Emmanuel; Khoury, Joseph D.; Geraghty, John

doi:10.1109/wsc.2013.6721644

Cited by 7 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings of this research agree with the works of Onyeocha and Geraghty (2012); Onyeocha et al (2013aOnyeocha et al ( , 2013b, by demonstrating that BK-CONWIP combined with S-KAP has a lower WIP inventory than BK-CONWIP D-KAP. BK-CONWIP is effective in managing the conflicting objectives of maintaining high service levels while minimising total WIP in both the simple and complex multi-product serial assembly lines examined.…”

Section: Resultssupporting

confidence: 92%

“…The models captured accurately the systems' entities and linkages. The models were validated owing to the contents of the systems and by direct comparison with the output of models found in the works of Olaitan and Geraghty (2013) and Onyeocha et al (2013aOnyeocha et al ( , 2013b. Furthermore, the models were operated using a simple push control strategy for which the systems are being operated and the output data collected were affirmed by the company's production team as valid.…”

Section: Simulation Modellingmentioning

confidence: 93%

“…The control mechanisms for BK-CONWIP in both S-KAP and D-KAP mode are provided in Onyeocha et al (2013a;2013b). The control mechanism for HK-CONWIP in D-KAP mode is provided in Onyeocha and Geraghty (2012).…”

Section: Control Mechanismsmentioning

confidence: 99%

See 2 more Smart Citations

Robustness analysis of pull strategies in multi-product systems

Onyeocha¹,

Khoury²,

Geraghty³

2015

JIEM

Self Cite

View full text Add to dashboard Cite

Abstract:Purpose: 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.Design/methodology/approach: 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. Findings: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.-1125-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1407Research limitations/implications: 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 Practical implications: 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.Originality/value: 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.

show abstract

Section: Resultssupporting

confidence: 92%

Section: Simulation Modellingmentioning

confidence: 93%

See 1 more Smart Citation

Robustness analysis of pull strategies in multi-product systems

Onyeocha¹,

Khoury²,

Geraghty³

2015

JIEM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, a number of studies [26,27] developed mathematical or simulation models for optimisation of production authorisation card in order to minimise the inventory, production and shortage costs in a multi-product system, while certain studies [28][29][30][31] evaluated the effect of the WIP cap of CONWIP in multi-product manufacturing systems. To address the issue of selection of an appropriate pull control strategy, researchers compare and rank the performance of various pull control strategies [32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A comparison of HK-CONWIP and BK-CONWIP control strategies in a multi-product manufacturing system

Onyeocha

Wang

Khoury³

et al. 2015

Operations Research Perspectives

Self Cite

View full text Add to dashboard Cite

show abstract

“…These studies assumed that research results on single-product lean manufacturing environments are scalable to multi-product lean manufacturing environments. This assumption is not reliable because a pure implementation of a PPCS in a multi-product environment requires maintaining semi-finished parts of each of the products distributed throughout the system, which proliferates WIP inventory (Olaitan & Geraghty, 2013;Onyeocha, 2012Onyeocha, , 2014Onyeocha, Khoury, & Geraghty, 2013, 2015Satyam & Krishnamurthy, 2008).…”

Section: Introductionmentioning

confidence: 99%