Barriers to lean six sigma implementation in the supply chain: An ISM model

Ali, Syed Mithun; Hossen, Md. Anwar; Mahtab, Zuhayer; Kabir, Golam; Paul, Sanjoy Kumar; Adnan, Ziaul Haq

doi:10.1016/j.cie.2020.106843

Cited by 68 publications

(58 citation statements)

References 47 publications

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“…ISM has been broadly utilized in the early literature on policymaking to its wide use in recent research [ 81 ]. It provides a clear picture of the interrelationships, and presents cooperative identifications of these associations [ 82 ].…”

Section: Methodsmentioning

confidence: 99%

Developing an interplay among the psychological barriers for the adoption of industry 4.0 phenomenon

et al. 2021

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This research aims to identify, rank, and create an interplay among the psychological barriers to adopting Industry 4.0 technologies in the manufacturing sector. A comprehensive literature review tracked by a discussion with industry and academic experts recognized 20 barriers. Based on three widely acclaimed statistical techniques, hybrid AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Performance by Similarity to Ideal Solution) and ISM (Interpretative Structural Modeling), critical psychological barriers have been investigated. A group of 8 experts from industry and academia with at least 10 years of experience was consulted for AHP and ISM techniques. Whereas TOPSIS was conducted by 443 operational-level users, including managers and supervisors of different functional areas of the manufacturing industry located in Pakistan. The findings reveal that ‘Fear of job losses’, ‘Fear of data loss/Risk of security breaches, ‘Lack of advanced & continued education of employees’ and ‘Lack of standards and reference architecture’, with highest importance weights, emerged as the most prominent psychological barriers in developing economies. Then the interrelations among these barriers resulted in a four-layered structural model. The driver barriers identified in the final model advocate that development in ‘advanced & continued education of employees’, ‘standards & reference architecture’ and ‘minimization of fear of job & data loss’ can expedite the adoption of industry 4.0 (i4.0) technologies. The study uniquely develops hierarchical relationships among the psychological barriers for adopting i4.0 in the manufacturing context using AHP-TOPSIS and ISM techniques. The study would be valuable for practitioners, decision-makers and companies that wish to focus their efforts and resources on removing the most critical barriers and challenges for the seamless implementation of Industry 4.0.

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Section: Methodsmentioning

confidence: 99%

Developing an interplay among the psychological barriers for the adoption of industry 4.0 phenomenon

et al. 2021

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“…Very few studies mention usage of some software for transitivity calculations such as [56,57]. However, one of the most frequent reasons for incorrect ISM calculations have been wrong transitivity calculations, such as in studies [9,13,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Therefore, this study proposes the use of an established Warshall algorithm [67] for transitivity calculations.…”

Section: Structural Self-interaction Matrix (Ssim)mentioning

confidence: 99%

“…There are two approaches followed for decision making, namely, discourse and survey. For discourse many techniques have adopted such as idea engineering workshops [40,60], telephonic enquiries [43,44], group decision making [13,52], personal interview [70,78], brainstorming [16,33,34,66], laddering interview [39], direct meetings [44], semi-structured interview [21,58,93] and structured interview [69], Delphi technique [37,61], and focus group discussion [11,27]. Similarly, for surveys different techniques are as follows: individual and consensus questionnaire [43], Likert scale questionnaire [78,90], email questionnaire [44], library survey method [9], and self-administered questionnaire [46].…”

Section: Decision Making and Data Collection Techniquesmentioning

confidence: 99%

“…Implementation of this technique and conduction of brainstorming sessions with experts in previous studies [17,18] led to identification of some key challenges such as variables' identification, selection of decision makers and method of decision making, and unavailability of end-to-end software for ISM and MICMAC. Furthermore, the literature shows erroneous applications of steps of ISM such as wrong reachability matrix [19][20][21][22], wrong transitivity calculations [9,13,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], incorrect level partitioning and wrong structure of the model [31,[38][39][40][41][42], and incorrect addition [11,14,[43][44][45][46][47][48] or reduction [49][50][51][52] of edges affecting the reachability of variables. An error in an earlier step generally leads to an error in subsequent steps.…”

Section: Introductionmentioning

confidence: 99%

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SmartISM: Implementation and Assessment of Interpretive Structural Modeling

Ahmad

Qahmash

2021

Sustainability

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Interpretive Structural Modeling (ISM) is a technique to establish the interrelationships between elements of interest in a specific domain through experts’ knowledge of the context of the elements. This technique has been applied in numerous domains and the list continues to grow due to its simplistic concept, while sustainability has taken the lead. The partially automated or manual application of this technique has been prone to errors as witnessed in the literature due to a series of mathematical steps of higher-order computing complexity. Therefore, this work proposes to develop an end-to-end graphical software, SmartISM, to implement ISM technique and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)), generally applied along with ISM to classify variables. Further, a scoping review has been conducted to study the applications of ISM in the previous studies using Denyer and Tranfield’s (2009) framework and newly developed SmartISM. For the development of SmartISM, Microsoft Excel software has been used, and relevant algorithms and VBA (Visual Basic for Applications) functions have been illustrated. For the transitivity calculation the Warshall algorithm has been used and a new algorithm reduced conical matrix has been introduced to remove edges while retaining the reachability of variables and structure of digraph in the final model. The scoping review results demonstrate 21 different domains such as sustainability, supply chain and logistics, information technology, energy, human resource, marketing, and operations among others; numerous types of constructs such as enablers, barriers, critical success factors, strategies, practices, among others, and their numbers varied from 5 to 32; number of decision makers ranged between 2 to 120 with a median value of 11, and belong to academia, industry, and/or government; and usage of multiple techniques of discourse and survey for decision making and data collection. Furthermore, the SmartISM reproduced results show that only 29 out of 77 studies selected have a correct application of ISM after discounting the generalized transitivity incorporation. The outcome of this work will help in more informed applications of this technique in newer domains and utilization of SmartISM to efficiently model the interrelationships among variables.

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“…It is also important that employees be involved in the approach as much as possible, which is what continuous improvement and most enterprise modelling methods propose. [38] mentions the lack of training and planning as barriers to Lean projects implementation. These aspects have to be systematically taken into account in the projects portfolio.…”

Section: Competitive Aspectsmentioning

confidence: 99%

Model-Based Enterprise Continuous Improvement

Vallespir¹,

Zouggar-Amrani²

2021

Lean Manufacturing

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The enterprise reengineering based on enterprise modelling is usually carried out within the framework of conventional projects. This leads to relatively long projects that are not compatible with a highly variable economic environment. The objective of the evolution management presented here is to use enterprise modelling and all the benefits it brings in a framework that allows for more continuous improvement than is generally observed. The proposed architecture is made up of three levels: a strategic level based on performance measurement, a tactical level that manages system migration and is based on enterprise models, and an operational level consisting of managing a portfolio of evolution projects. Together, these allow a shorter set of projects to be carried out, while remaining coherent and aligned with the company’s strategy. This approach puts enterprise modelling methods and continuous improvement/Lean management approaches into perspective, allowing complementarities and opening up interesting perspectives concerning enterprise re-engineering methods.

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Barriers to lean six sigma implementation in the supply chain: An ISM model

Cited by 68 publications

References 47 publications

Developing an interplay among the psychological barriers for the adoption of industry 4.0 phenomenon

Developing an interplay among the psychological barriers for the adoption of industry 4.0 phenomenon

SmartISM: Implementation and Assessment of Interpretive Structural Modeling

Model-Based Enterprise Continuous Improvement

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