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Agility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.

In present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.

It was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.

The decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.

A novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

The purpose of this paper is to report a case study carried out to assess the agility and identify obstacles to agility in a supply chain. A human perception-based framework is used for the calculation of agility. The case study was carried out in a North India-based manufacturing organization.

In this study, the concept of a multi-grade fuzzy logic approach is used. Using this concept, the overall agility index has been determined. The fuzzy logic approach has been used to overcome the disadvantages such as impreciseness and vagueness using a scoring method.

From the analysis, it is observed that the organization on which the study was performed is “very agile.” After evaluating the agility level, the fuzzy performance importance index is calculated, which helps to identify the barriers of agility in the supply chain. These barriers help decision makers to implement appropriate improvement measures for improving agility level. Overall, 11 barriers were identified in the study.

Managers of the contemporary manufacturing organization have to measure the agility level of the organization and identify barriers to agility in order to survive in a competitive environment. The obstacles identified in this study are used to improve the performance of the organization. The enterprise should improve on the weak areas in order to achieve the highest agility level.

The agile supply chain (ASC) enablers proposed by previous researchers are not sufficient for the evaluation of agility of a supply chain. There are a few more ASC enablers such as customer satisfaction, flexibility and adaptability that also play a vital role in making a supply chain agile. Adding these three ASC enablers, a total of seven ASC enablers along with their attributes are being considered for the development of a conceptual model.

Infrastructural revolution, intense competition and customer attraction towards organised apparel retailing in India are potentially affecting traditional retailing. The authors seek to identify the factors that customers perceive during shopping in organised apparel retail store. This study also investigates the indirect effects of identified factors on behavioural outcomes such as loyalty.

The study randomly selected the customers immediately after shopping to minimise the experience's carryover effects. A sample of 648 customers was collected. Data were analysed using multivariate analysis of covariance.

The study has found that in-store logistics is the second order factor with ease of return, on-shelf availability, product accessibility, shopping convenience, and product information as the variables. The result shows that in-store logistics, store environment, store communication, merchandise assortment, perceived price and employee attribute influence customer satisfaction. As expected, these factors indirectly influence the loyalty.

This study focused on organised multi-brand apparel retailing, and the survey was conducted in a tier-II city of India only. Hence, any attempt to generalise the findings must be undertaken with caution.

In the context of multi-brand retailing, competition is fierce. New entrants and traditional apparel retailers hesitate to adopt organised apparel retailing. The findings of this study can be helpful for new entrants and traditional apparel retailers to adopt organised apparel retailing.

Previous studies in the field of multi-brand retailing have mainly focused on the marketing aspect of retail stores. This study contributes to the operations aspect and tests the impact of operational function (in-store logistics) on customer satisfaction.

This paper aims to study the concept, characteristics and factors of the resilient supply chain (RSC) and develop a hierarchical structural model and classify the factors based on their interrelationships.

This paper has used a mixed-approach of literature review and expert opinion to identify the factors of RSC. For the development of the structural model and clustering of the factors, this paper has used the total interpretive structural modeling approach with Matrice d’Impacts Croises-Multiplication Applique and Classment and decision-making trial and evaluation laboratory analysis.

In total, this study has identified 17 factors that enable the 3R capability of the RSC. The result shows that the factors have a close dependence relationship with supply chain (SC) risk management culture as the most influencing factor. Further, this study classifies the factors into enablers and strategies.

This research work is the theoretical contribution to the RSC concept and helps the experts to develop and improve the resilient ability in the SC. This research is based entirely on subjective expert feedback; thus, the results are sensitive to the expert’s judgment.

This research will help the decision-makers in allocating the resources and policies to develop or improve the SC capabilities.

This research work is the first kind of research in the field of the RSC that considers the 3R concept to identify and model the resilient factors of the SC. Along with the theoretical concept, this research provides empirical evidence for the importance ranking of the factors.

The purpose of this paper is to provide a traffic route selection strategy based on minimum carbon dioxide (CO₂) emission by vehicles over different route choices.

The study used queuing theory for Markovian M/M/1 model over the road junctions to assess total time spent over each of the junctions for a route with junctions in tandem. With parameters of distance, mean service rate at the junction, the number of junctions and fuel consumption rate, which is a function of variable average speed, the CO₂ emission is estimated over each of the junction in tandem and collectively over each of the routes.

The outcome of the study is a mathematical formulation, using queuing theory to estimate CO₂ emissions over different route choices. Research finding estimated total time spent and subsequent CO₂ emission for mean arrival rates of vehicles at junctions in tandem. The model is validated with a pilot study, and the result shows the best vehicular route choice with minimum CO₂ emissions.

Proposed study is limited to M/M/1 model at each of the junction, with no defection of vehicles. The study is also limited to a constant mean arrival rate at each of the junction.

The work can be used to define strategies to route vehicles on different route choices to reduce minimum vehicular CO₂ emissions.

Proposed work gives a solution for minimising carbon emission over routes with unsignalised junctions in the tandem network.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

Due to the coronavirus disease 2019 (COVID-19) pandemic, the world faces different issues, and proper healthcare waste (HCW) treatment is one of them. If appropriate disposal of HCW is not performed, it will have hazardous effects on humanity. This paper has identified the significant barriers hindering the proper treatment of healthcare waste management (HCWM) with the strategies to overcome these barriers.

This paper has identified the significant barriers hindering the proper treatment of HCWM with the strategies to overcome these barriers, and different barriers are identified and categorized into organizational, waste handling, human resource and technical barriers. The analytical hierarchy process (AHP) process is used to rank the barriers and sub-barriers. Then, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method evaluates the strategies for proper implementation of HCWM.

The results show that organizational barriers are the most significant barrier, with a lack of coordination of hospitals with other authorities and no priority given to waste management issues as highly ranked barriers. The results of the Fuzzy TOPSIS method indicate that “Increase govt support and policies” and “Enhance training and awareness of employees” are the most feasible strategies to overcome these barriers for the successful implementation of HCWM.

This study will be helpful in policy formulations for the proper treatment of HCW in an efficient manner. This paper helps to complete the research gap by providing the different characteristics of barriers.

This paper fills the research gap by expanding the limited knowledge in this field and providing further evidence on this phenomenon. The study also enables the distinctive characteristics of barriers to be understood within a particular context.

The purpose of this paper is to provide a solution for greening the supply chain of small and medium enterprises (SMEs) by minimising the vehicular pollutant emission in the logistics network.

The paper proposes an optimisation model to reduce the pollution emission in the logistics of supply chain network in SMEs. The work considers vehicle routing and selection of suppliers, manufacturers and assemblers according to the availability of various Bharat Stage Emission Standards type vehicles. Introsort sorting based selection algorithm is used to solve the problem. The proposed solution is implemented using C++ on an experimental data set for analysing the model.

The outcome of the study is a pollution optimisation model for logistics of SMEs. The finding shows an approach to reduce total vehicular pollution emission in the logistics network in meeting the demand. The model is tested over an experimental study, and the result findings show which supply chain entities, type of environmental standard vehicles and vehicle routes are selected for the specific demand.

The proposed model is confined to pollution optimisation with limited parameters only and does not consider cost and other factors that can be included in future work.

The work can be used for limiting pollution in logistics system as the corporate social responsibility of enterprises.

Proposed work presents a sustainable and green solution for pollution control in logistics activities of the SMEs.

Diabetes is one of the major healthcare challenges in India. The chronic nature of the disease makes the lifetime cost of the treatment exorbitantly high. The medicine cost contributes a major size of expense in diabetes management. To make healthcare available to poorest of the poor, it is imperative to control the rising cost of diabetes treatment. The earlier research works done in this area focuses more on inventory management techniques to control the cost of healthcare. Less interest is shown in the role of better supply chain partnership (SCP) in reducing the cost of procurement of medicine. The purpose of this paper is to develop and use the SCP assessment framework for a diabetes clinic. The approach is generalized enough to be adopted for other similar organization.

This paper adopts self-assessment criteria of the European Foundation for Quality Management (EFQM) business excellence model for analysis of SCP in the supply chain of a private diabetes clinic in Varanasi. The paper uses analytic hierarchy process (AHP) method for calculation of weights of criteria.

The EFQM-based framework can be adopted as easy-to-use tool to make an objective assessment of the SCP. The proposed model in the study is a balanced model between enablers and results, which includes multiple assessment dimensions. The supply chain performance score of the diabetes clinic under study was found as the Tool Pusher, which means the effort in direction of SCP is not too good. The organization needs to clearly define the SCP goal and analyze the results to identify the gap areas.

The study is first of its kind and contributes to the literature by providing non-prescriptive and easy-to-use SCP assessment framework, for chronic disease care. The case study approach provides a procedure for the healthcare organization willing to adopt this approach.

The aim of this paper is, first, the desire to present the issue of retail sector competitiveness with the simultaneous determination of factors having an impact on competitiveness and their development. The main aim is to identify the factors and relationships among those factors to strengthen the competitive positioning of apparel retail stores.

The literature review and experts’ opinion helped to identify the key factors. The relationships among the factors were obtained by using interpretive structural modelling (ISM). Experts’ opinions were collected again for the fuzzy direct relationship matrix. Factors were further classified by driver and dependence power using the fuzzy matrix of cross-impact multiplications applied to classification (FMICMAC) analysis.

Total nine strengthening factors (SFs) identified here, and developed an integrated model using ISM and classified it into four clusters with the help of driver and dependence power. The model hierarchy shows the interrelationships among these SFs. The retail environment, Information and Communication Technology, technology adoption and human resource management were found to be the most significant factors needing some spotlight by the top-level authority.

The study will help managers to understand the variables and their relationships and to select the right factors to achieve a potential competitive position. Relationships among the factors were obtained through the opinions of experts and academicians. Expert opinion is a subjective judgement, and biasing in judgement might affect the result.

The research presents the first kind of an integrated model using ISM and FMICMAC to identify nine factors and classify them by their driving and dependence power. The developed model helps in the identification, classification and selection of factors as per requirement. This study will assist managers to understand the variables and their relationships and to select right factors to achieve a potential competitive position.