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Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

Around the world, protecting environment and purchasing green products by the manufacturing firms progressively becomes a popular and important issue. Manufacturers are realizing the importance of producing green products under green practices. This study aims to propose an appraisement platform to evaluate the overall performance index of a firm under green practices. Furthermore, the study also helps in identifying ill-performing areas, which necessarily require future attention to augment green supply chain (GSC) of a firm. A case research is conducted to assess the real-life application by the proposed approach.

The authors used fuzzy performance index to measure the overall performance index of a firm. Beside this, they proposed a degree of similarity approach amalgamated with fuzzy performance importance index to classify the ills and strong indices in GSC extent.

The intermittent assessment of green practices and their metrics in the organizational supply chain management (SCM) is indeed necessary. The present study provides an appraisement module to assess overall GSC fuzzy performance index and also helps in identifying the ill-performing areas which require future augmentation toward successful green implementation.

The exposed research work dealt with chains of subjective indices (measure and their interrelated metrics), which are induced into hierarchical appraisement module. To tackle the uncertainty of indices, the subjective indices are transposed into interval-valued fuzzy number set (IVFNS), as IVFNs are preferred to undertake the uncertainty of GSC indices. The proposed approach is demonstrated with a case research to justify its validity and originality.

Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue.

This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review.

To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE.

The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process.

The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment.

The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.

The purpose of this paper is to develop a multi-level hierarchical framework (evaluation index system) toward evaluating an “appraisement index” from the prospectus of measuring and monitoring resilient performance of the candidate industry.

In this reporting, vagueness, imprecision, as well as inconsistency associated with subjective evaluation information (aligned with ill-defined assessment indices of SC resilience performance), has been tackled by the application of fuzzy theory.

Subjective evaluation information (expressed in linguistic term) acquired from the committee of decision makers (called expert group), against different resilience indices/metrics, has been fruitfully explored through the proposed fuzzy-based resilience performance appraisement module. Finally, a case study from an Indian automobile company has been conducted from the perspective of checking effectiveness of the proposed methodology for evaluation of appraisement index indicating SC resilience extent.

This methodology might be successfully applied to help other decision-making problems from the perspective of performance appraisal and benchmarking of candidate alternatives/choices under predefined criteria and subjective evaluation circumstances.

The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.

The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.

The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.

The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.

The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

In recent years, stimulated environmental awareness (green consciousness) has favored the emergence of the green supply chain paradigm. Therefore, apart from traditional supplier selection criterions, green criteria are necessarily to be incorporated in the supplier selection problem. In this context, the present study aims to highlight an efficient supplier appraisement platform by considering green performance criteria, in fuzzy environment.

The present work exhibits an efficient fuzzy-based supplier performance assessment system using generalized trapezoidal fuzzy numbers set. A fuzzy overall evaluation index has been estimated towards assessing suppliers' green performance extent, thus facilitating supplier appraisement cum selection decision-making.

The proposed method has been found efficient for solving the group decision-making problem under uncertain environment due to vagueness, ambiguity associated with decision-makers' subjective judgment. The proposed appraisement platform has been explored by an Indian automobile part manufacturing company at eastern part of India. Suppliers have been evaluated individually to check their performance level with respect to green attributes. Apart from estimating overall performance metric, the model presented here can identify ill-performing areas that necessitate future attention.

The major contributions of this work have been summarized as follows: Development and implementation of an efficient decision-making procedural hierarchy to support suppliers' green performance extent evaluation. An overall performance metric has been introduced. Concept of generalized trapezoidal fuzzy numbers has been efficiently explored to facilitate such an appraisement cum selection decision-making. The appraisement index system has been extended with the capability to search ill-performing areas that require future progress.

In today’s’ highly competitive market, outsourcing logistic activities have become a global trend as it offers wide range of services including transportation, distribution, packaging, labeling, warehousing, freight forwarding and order fulfillment. The demand of third-party logistics (3PL) provider becomes an increasingly important issue for corporate seeking improved customer service, operational efficiency, logistics costs as well as capital expenditure reduction. However, choosing a proper 3PL provider is a kind of multi-criteria decision making problem under consideration of complicated criteria hierarchy. Therefore, it seems necessary to develop an efficient appraisement module towards performance evaluation as well as selecting the best 3PL provider. The paper aims to discuss these issues.

The present paper proposes a fuzzy based appraisement platform for evaluation and selection of 3PL providers. The theory behind interval-valued fuzzy numbers (IVFNs) has been utilized to aid the said decision-modeling. Based on two appraisement modules for 3PL evaluation; empirical data have been analyzed to validate case application.

The proposed method has been found efficient for solving the group decision-making problem under uncertain environment due to vagueness, ambiguity associated with decision-makers’ subjective judgment. The proposed appraisement platform has been explored by an Indian automobile part manufacturing company at eastern part of India. 3PL providers have been evaluated individually to check their performance level with respect to various evaluation attributes. Apart from estimating overall performance metric, the model presented here can identify ill-performing areas which necessitate future attention.

The major contributions of this work have been summarized as follows: First, development and implementation of an efficient decision-making procedural hierarchy to support 3PL evaluation and selection. Second, an overall performance metric has been introduced. Third, concept of IVFNs has been efficiently explored to facilitate such a appraisement cum selection decision making. Final, the appraisement index system has been extended with the capability to search ill-performing areas which require future progress.

Lean manufacturing is an operational strategy oriented toward achieving the shortest possible cycle time by eliminating waste. It is derived from the Toyota Production System and its key thrust is to increase the value-added work by eliminating waste and reducing incidental work. In today's competitive global marketplace, the concept of lean manufacturing has gained vital consciousness to all manufacturing sectors, their supply chains, and hence a logical measurement index system is indeed required in implementing leanness in practice. Such leanness estimation can help the enterprises to assess their existing leanness level and can compare different industries who are adapting this lean concept. The paper aims to discuss these issues.

The present work exhibits an efficient fuzzy-based leanness assessment system using generalized interval-valued (IV) trapezoidal fuzzy numbers set. The concept of “degree of similarity” between two IV fuzzy numbers has been explored here to identify ill-performing areas towards lean achievement.

The methodology described here has been found fruitful while applying for a particular industry, in India, as a case study. Apart from estimating overall lean performance metric, the model presented here can identify ill-performing areas towards lean achievement.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision-making procedural hierarchy to support leanness extent evaluation. An overall lean performance index evaluation platform has been introduced. Concept of generalized IV trapezoidal fuzzy numbers has been efficiently explored to facilitate this decision-making. The appraisement index system has been extended with the capability to search ill-performing areas which require future progress.

The purpose of this study is to develop a CMMI model for the manufacturing industry and to find an appropriate method to assess the CMMI level. The CMMI level indicates how well the processes are planned, performed, measured and controlled, thus revealing the performance level of an organization.

Among the various process areas of CMMI, “organizational process focus” (OPF) is selected for the study. The CMMI model for the process area OPF is designed based on the CMMI enablers, criteria and attributes. Based on this multilevel model, a case study approach is adopted and fuzzy logic is used to measure the CMMI level of an organization. The fuzzy performance importance index (FPII) and the ranking score are used to further analyze the attributes.

The proposed model has been successfully used to measure the CMMI level of the manufacturing industry in south India. The triangular fuzzy number of the fuzzy CMMI measure index (FCMI) is obtained as (2.077, 3.534, 5.000). The transformation of FCMI back into linguistic terms discloses the current CMMI level of the industry as “Capability Maturity Level 2” (CML 2).

The authors tested the suitability of an inter-disciplinary approach known as CMMI for the process appraisal in the manufacturing sector. The investigation sets forth a unique framework to quantify the performance of practices followed in a manufacturing organization and thereby help the industry to realize the present strength and weakness in terms of process assets.

Lean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting time by combing the principles of lean thinking with Six Sigma methodologies. To implement LSS successfully in healthcare organizations it is necessary to know the readiness level before starting the change process. Thus, the purpose of this paper is to assess the readiness level for the implementation of LSS in healthcare using a fuzzy logic approach.

The current study uses a fuzzy logic approach to develop an assessment model for readiness to implement LSS. The conceptual model for readiness is developed with 5 enablers, 16 criteria and 48 attributes identified from the literature review. The current study does the study in a medium-size hospital from India.

The fuzzy readiness for implementation of LSS index (FRLSSI) and fuzzy performance importance index (FPII) are calculated to identify the readiness level for the implementation of LSS in the case hospital. The FRLSSI is computed as average ready with (3.30, 5.06 and 6.83) and the FPII computed helps to identify 15 weaker attributes from 48 attributes.

The current study uses only one hospital for study. In the future, the model can be tested in many hospitals.

The current study would be used by the managers of a healthcare organization to identify the readiness level of their organization to implement LSS. The proposed model is based on the identification of enablers, criteria and attributes to assess the readiness level of a healthcare organization and it helps to improve the readiness level to implement LSS effectively.

The present study contributes to the knowledge of readiness for the implementation of LSS in a healthcare organization. The conceptual model is developed for assessing the readiness level of a healthcare organization and it helps to improve the readiness level for successful implementation of LSS. Weaker attributes are identified and necessary corrective actions should be taken by the management to improve the readiness. The continuation of the assessment readiness model over a period of time would help to improve the readiness level of healthcare for the implementation of LSS.