A framework model for lean tools selection for improving material flow using fuzzy TOPSIS

Baskaran, S. Muthu; Lakshmanan, A.R.

doi:10.1504/ijpqm.2019.100144

Cited by 6 publications

(5 citation statements)

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“…The general notation of representing fuzzy triangulation number is (aij, bij, cij), where a , b , c denotes minimum, intermediate and maximum value, respectively, and i , j represents the row and column of the fuzzy TOPSIS matrix. These steps can be referred from base papers for more details (Kumar and Barman, 2021; Baskaran and Lakshmanan, 2019; Kumar et al. , 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Huge amount of waste can be reduced along with reduction in lead time, integrated with a prioritization technique using fuzzy TOPSIS, which helps the practitioners and the industry to tackle the waste systematically. It improves the overall efficacy of the organization coupled with improved customer satisfaction, cost reduction and employee empowerment (Baskaran and Lakshmanan, 2019). VSM and AHP Improve quality, cost and targets of the manufactured components using lean tools mainly VSM.…”

Section: Research Gapmentioning

confidence: 99%

“…Continuous Improvement Abdulmalek and Rajgopal (2007), Chan et al (2005), Shah and Ward (2003), Smith and Hawkins (2004), Vinodh and Chintha (2011), Baskaran and Lakshmanan (2019) Visual Controls Abdulmalek and Rajgopal (2007), Shah and Ward (2003), Smith and Hawkins (2004), Vinodh and Chintha (2011), Baskaran and Lakshmanan (2019) WIP Reduction…”

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confidence: 99%

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Deployment of fuzzy TOPSIS-integrated value stream mapping for a fabrication industry: a case study

Aadithya

Asokan

Vinodh

2022

TQM

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PurposeThis research aims to identify lean tools and techniques that are needed to be implemented to improve the performance in the fabrication industry. The objective is to find the wastes in manufacturing processes using value stream mapping (VSM) and prioritize the lean tools suitable to enable the attainment of leanness and streamline the processes.Design/methodology/approachVSM tool is applied in the industry to construct the current state map, identify improvement proposals and implement in future state. Fuzzy technique for order performance by similarity to ideal solution (TOPSIS), a multi-criteria decision-making technique (MCDM), is used to prioritize the identified improvement proposals. This study observed that mistake-proof processing and layout organization are the two techniques with the top priority that needs further improvement to enhance the leanness of the organization.FindingsUpon successful implementation, the cycle time is reduced by 14.97%, and total inventory is reduced by 45.67% which leads to the improvement of value addition from 5.88 to 9.21%. Although lean has been adopted for many years, implementation of lean in the fabrication industry has been limited.Practical implicationsThis study addresses the challenges in terms of implementing lean in fabrication industries and practical implications of lean tools and techniques and the prioritization of lean concepts against various lean criteria to enable leanness.Originality/valueThe deployment of improvement prioritization tool integrated with VSM in the context of a fabrication industry is the original contribution of the authors.

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Section: Methodsmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

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Deployment of fuzzy TOPSIS-integrated value stream mapping for a fabrication industry: a case study

Aadithya

Asokan

Vinodh

2022

TQM

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“…Many academic articles have shown integrated frameworks to prioritise lean technologies in the field of MCDM. Fuzzy AHP, Fuzzy TOPSIS, and the fuzzy decision-making trial and evaluation laboratory (FDEMATEL) are MCDM techniques that are used in the framework model for lean tool selection [44,45]. Triangular fuzzy numbers (TFNs) are used in research because of their straightforwardness [46].…”

Section: Figure 1: Mcdm Modelsmentioning

confidence: 99%

Implementation of Hybrid Artificial Neural Network and Multi-Criteria Decision Model for the Ranking of Criteria That Affect Productivity – A Case Study.

Janarthanam,

Rao

2024

SAJIE

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This study delves into the often-overlooked factors influencing industrial productivity, focusing on labour and machine maintenance as key drivers. Extensive research was undertaken in a core shop producing automotive components to identify and assess these factors. Using multi-criteria decision models (MCDM) such as analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), viekriterijumsko kompromisno rangiranje (VIKOR) method, enterprise distributed application service (EDAS), and Entropy TOPSIS, the study ranked various productivity criteria. Artificial neural networks were then employed to validate these rankings. The research emphasised the significance of manufacturing equipment and raw materials, following the prioritisation of the workforce. Implementing material handling systems aimed at reducing errors and enhancing productivity proved pivotal. As a result of these strategies, non-value-added activities (NVA) decreased by 65.56%, process time improved by 61.03%, waiting time reduced significantly by 66.66%, manpower decreased by 35%, and costs decreased by 45%. These outcomes translated into a notable 23% increase in production levels in the core shop. The study underscores the efficacy of innovative work methods and standardised operating procedures in maximising productivity.

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“…( (Abid et al 2020, Abu et al 2019, Alhuraish et al 2017, Ali et al 2020, Al Manei et al 2017, Baskaran and Lakshmanan 2019, Caldera et al 2019, Costa et al 2020, Davis and Fifolt 2018, Dubey et al 2016, Gandhi et al 2018, Hudnurkar et al 2019, Kaswan and Rathi 2019, Knol et al 2018, Kuvvetli et al 2016, Lande et al 2016, Lodgaard et al 2016, Madhani 2020, Marzagao and Carvalho 2016, Mohammad and Oduoza 2019, Mundra and Mishra 2020, Netland 2016, Parmar and Desai 2020, Pereira et al 2017, Pereira and Tortorella 2018, Secchi and Camuffo 2019, Sieckmann et al 2018, Singh et al 2021, Stankalla et al 2018, Zala et al 2020, Zhuo 2016…”

Section: Literature Reviewmentioning

confidence: 99%

Critical Success Factors of Lean Six Sigma Implementations in Philippine Food Manufacturing Companies

Paulmino,

Marquez

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Lean Six Sigma (LSS) is a data-driven philosophy that drives performance improvement by eliminating waste and reducing variability. Food Manufacturing (FM) companies introduce programs like LSS to improve competitiveness. However, LSS implementations are complicated and complex. Companies must carefully manage the implementation of LSS to achieve the desired outcomes. In line with this, proper management of Critical Success Factors (CSFs) can enhance the initiative's chances of success. This research aims to identify, rank, and understand the CSFs, and recommend strategies to manage them within Philippine FM companies. A combined approach of structured literature review, structured survey, and structured interview were used to gather the necessary data. The thematic analysis and Relative Importance Index method revealed that there are 15 CSFs. These CSFs were synthesized into 4 clusters: Synergy, Expertise in LSS, Effective Management, and Dedicated Leadership. Thus, to be successful, all stakeholders within the company must have the essential hard and soft skills to execute the DMAIC framework and manage the initiative as a whole. More importantly, they must have the proper working environment, be aligned to the intended goal, and fully cooperate and contribute to the activities that must be accomplished to achieve the target future state.

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A framework model for lean tools selection for improving material flow using fuzzy TOPSIS

Cited by 6 publications

References 0 publications

Deployment of fuzzy TOPSIS-integrated value stream mapping for a fabrication industry: a case study

Deployment of fuzzy TOPSIS-integrated value stream mapping for a fabrication industry: a case study

Implementation of Hybrid Artificial Neural Network and Multi-Criteria Decision Model for the Ranking of Criteria That Affect Productivity – A Case Study.

Critical Success Factors of Lean Six Sigma Implementations in Philippine Food Manufacturing Companies

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