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Industrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.

In this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.

This research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.

The proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.

The proposed model is generic and can be applied for large-scale GSC environments with little modifications.

No prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

Heightened public concerns for the global environment due to human overexploitation have given rise to many green product initiatives by businesses. Green cosmetics (GC) are products developed and marketed based on ecological sustainability and have shown increasing consumer appeal worldwide. The current research investigates the antecedents of green cosmetics consumption among Indian GC users, hitherto un-investigated comprehensively.

The study is a cross-sectional pairwise comparison of green evaluation criteria for cosmetics using an expert panel of thirty Indian cosmetic users using a fuzzy analytic hierarchy process (F-AHP) and investigate the impact of a comprehensive list of antecedents on the multi-criteria category consumption decision.

The study results indicate that perceived consumer effectiveness is the most critical factor for green cosmetics consumption in India, followed by brand trust, behavioural control, and environmental effectiveness. The least important factors are price and social norms.

As the GC category is at nascent stage in India the investigation is limited to the GC category innovators – a set of people high in intellectual and financial resources. The study is also limited to women users as the male cosmetic market in the country is still very small.

The study results can help marketers in designing and implementing GC related marketing strategies to deliver higher customer value to the target segment. Academicians can use the study results for better prediction of category related behaviour of consumers.

The study results will help promote GC category adoption and usage which can benefit the environment and consumer health.

The study contributes to literature and practice by assessing a comprehensive set of critical antecedents to GC adoption using a novel approach of F-AHP and an expert user panel. The study results offer insights to marketers that can be used to develop suitable strategies to convert non-GC users into GCs in India and similar markets, improving category penetration and benefitting marketers, retailers, users, and the environment.

The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).

To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.

The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).

This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.

This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

Explains that a common problem of cost control at design stage is the firm’s (manager’s) desire for the lowest cost compatible with supporting innovation and the designer’s preference for the optimal design, which may be unnecessarily sophisticated. Develops a mathematical model to represent this situation, pointing out that the manager is usually unaware of the design alternatives unless they are revealed by the designer, but can use budgetary limits and “load” costs onto certain cost drivers (e.g. number of parts) to influence the designer’s choice and align his/her interests with those of the firm. Suggests that the difference between actual and “loaded” costs is a function of the non‐cost benefits from design choice (e.g. competitive edge) and the degree of information asymmetry between manager and designer. Considers the implications for costing activities and the limitations of the model.

The primary aim in this paper is to develop and demonstrate a theory of constraints (TOC) model in which constraint resource prevents the throughput of the organization.

In this paper, the authors propose an integrated model by combining Laplace criterion and TOC into a single evaluation model in a multiproduct constraint resource environment. A case study is illustrated to demonstrate the effectiveness of this model. The outsourcing decision model compares three alternatives: standard cost accounting, standard theory‐of‐constraints, and our own solution.

The numerical results show that the new approach is superior to Standard cost accounting and Theory of Constraints and presents a more realistic state of optimum allocation of resources and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Laplace‐TOC model has been used to maximize the product throughput. Instead of calculating $return per constraint minute, this method decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing Organization where one can compare the financial performance of the Organization by selecting the right decision model.

The purpose of this paper is to develop and demonstrate an outsourcing model in which constraint resource prevents the throughput of the organization.

The paper proposes an integrated model by combining the Hurwicz criterion, the theory‐of‐constraints (TOC) and linear programming (LP) into a single evaluation model in a multi‐product constraint resource environment. A case study is presented to demonstrate the effectiveness of this model. The decision model compares four alternatives: standard cost accounting, standard TOC, LP analysis, and our own solution, which is an approach that combines TOC, LP, and the Hurwicz criterion.

The numerical results show that this model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Hurwicz‐TOC‐LP model has been used to maximize the product throughput. Instead of calculating $ return per constraint minute, this method decides the priority of product and resource center that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

The aim of this paper is to develop and demonstrate an outsourcing decision model in which constraint resource prevents the throughput of the organization.

An integrated model is proposed by incorporating the weighted product model (WPM) of AHP in estimating the priority of each product in a multi‐product constraint resource environment. A numerical example is presented to demonstrate the effectiveness of this model. The outsourcing decision model compares four alternatives: Standard cost accounting, standard theory‐of‐constraints (TOC), LP analysis and an original solution.

The numerical results show that the proposed model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This research is applicable to the companies which produce multi‐products in a situation in which market demand exceed the company's production capacity.

This is the first time that the WPM of AHP/TOC has been used to maximize the product throughput. Instead of calculating $return per constraint minute, it decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.

This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.

Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.

This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

The role of personal values in consumer behavior is well documented; however, in the context of fashion consumption, the role of personal values’ influence on consumers’ ethical behavior has not been studied. Consequently, the purpose of this paper is to seek to explore whether consumers’ personal values predict consumers’ behavioral intentions to engage in ethical fashion consumption.

The present study employed the Fritzsche model, which states that an individual's personal values are related to his/her intentions to engage in ethical behavior. The present study examined the causal relationship between the personal values and behavioral intentions to engage in ethical fashion consumption. Data collected from the US national sample were subjected to structural equation modeling.

The proposed model explained 42 percent of variance in consumer's behavioral intentions toward ethical fashion consumption. Furthermore, a significant negative relationship between self-enhancement personal values and behavioral intention toward ethical fashion consumption was found. Several theoretical and practical implications related to the present study were discussed.

To the authors’ knowledge, the study is first of its kind in several aspects: first, ethical fashion consumption has been conceptualized in the broadest definition possible, as oppose to focussing on a particular facet of fashion consumption (e.g. organic products or counterfeit fashion); second, linking consumer personal values as a predictor of his/her ethical fashion consumption behavioral intentions; and third, employing the Fritzsche model in fashion behavior context.