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As a result of growing environmental issues and stringent carbon emission (CEM) regulations imposed throughout the globe, low CEM has become one of the essential requirements of manufacturing industries. Low-carbon manufacturing, which aims to reduce carbon intensity and improve process efficiency, has evolved as emerging issue that has encouraged a lot of research into quantifying the CEM of different manufacturing processes. To comply with increasingly stringent CEM regulations and achieve low carbon manufacturing, manufacturing industries require accurate CEM data for their products. In this work, an empirical model is developed to quantify carbon emissions for machining of cylindrical parts. The CEM associated with a cylindrical part machining is decomposed into CEM from electrical energy consumption, material consumption, cutting tool wear, and coolant consumption and from the disposal of machining waste materials. Electrical energy consumption of a machine tool is further decomposed into different energy modules: startup, standby, spindle acceleration, idle, rapid positioning, air-cutting, and cutting for accurate quantification of CEM. Energy consumption models are developed for each module, and are integrated to quantify the total energy consumption of the machine tool. Finally, the developed model is applied on a cylindrical part with three different process plans to validate the developed model for practical implementation in industry. The proposed model can be utilized in the manufacturing industry to quantify carbon emissions based on different process parameters before machining a cylindrical part to achieve low carbon manufacturing process planning and scheduling.

Food loss and waste have become an issue of global significance, considering their concurrent effects on the socioeconomic and environmental facet of society. Despite this domain gaining prolific attention recently, issues hampering the effective utilization of residues from fish processing usually go unidentified in developing economies such as India. This occurs mainly owing to fragmented supply chains, inappropriate handling, discontinuous cold chains, inadequate temperature monitoring and so on, affecting quality and causing underuse. Any researcher trying to understand the prospects of utilizing these fish processing co-streams in a developing economy with the vision of improving consumption, economic sustainability, reducing discards and promoting circularity faces a lacuna. The authors address this demand in research by identifying the validity of this domain both in the global and native research community by conducting a detailed review using bibliometric analysis and content analysis. Data from Scopus with 717 documents, comprising 612 research articles from 78 countries, 1597 organizations and 2587 authors, are analysed. Results signify (i) developing a focus on hydroxyapatite production, bio-methane generation, transesterification processes, biomass and the rest raw material generated from fish processing, and (ii) reduced research on supply chain-related aspects despite their considerable importance. To comprehend this deficiency, especially in the Indian stance, barriers hindering the utilization of generated by-products are identified, and recommendations for improvements are proposed. The results will provide the struts for a circular and sustainable supply chain for processed seafood in developing economies.

PurposeCo-creation and co-invention are two diverse spheres in modern-day supply chains. Despite literature suggesting the existence of similar coherence between these concepts, the availability of published theory favoring these ideal lacks justification. This research aims to investigate the correlation and convergence of these well-known concepts to support a combined impact on research.Design/methodology/approachComprehensive review of published literature using mathematical and statistical tools to measure inherent interrelationships and publication impacts in literature handling co-creation and co-invention.FindingsAn exploratory quantitative and qualitative analysis reveals the conundrums existing in distribution, keyword and adoption of research in the global and scientific community. The research favors a positive correlation existing between concepts such as co-ordination, collaboration, open-innovation, value creation with supply chain management and its development with rising importance of big data and block-chain technologies. Analysis reveals knowledge development with increased user-based interaction, better utilization of resources and enhanced productivity to support the mutual adaptation of co-creation and co-invention.Research limitations/implicationsOutcomes will be a beacon for researchers to develop models and frameworks. Results derived will aid in improving customer participation, enhance decision making in product development, augment value creation and knowledge and resource sharing leading toward innovation.Originality/valueResults will provide a detailed outline of the development and implementation of concepts in both developed and developing countries. Outcomes will also serve as a framework for marketing heads, graphic designers, website designers, supplier management and customer management in the service industry, production supervisors and customer management personnel in manufacturing industries.

Purpose A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC). Design/methodology/approach The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation. Findings The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC. Research limitations/implications The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system. Practical implications The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions. Originality/value Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.

Today’s global and complex world increased the vulnerability to risks exponentially and organizations are compelled to develop effective risk management strategies for its mitigation. The prime focus of research is to design a supply risk model using Bayesian Belief Network bear in mind the tie-in of risk factors (i.e. objective and subjective) those are critical to a supply chain network. The proposed model can be re-engineered as per new information available at disclosure, so risk analysis will be current and relevant along the timeline as so situation is strained. The top three factors which influenced profitability were transportation risk and price risks. Netica is the platform used for designing and running simultaneous simulations on the Bayesian Network. The proposed methodology is demonstrated through a case study conducted in an Indian manufacturing supply chain taking inputs from supply chain/risk management experts. .

Machining processes contribute significantly to the energy consumption of manufacturing industries, and reducing their energy consumption is a major challenge to achieve sustainable and cleaner manufacturing. The accurate and practical energy consumption prediction models for a machine tool are the foundation for sustainable and cleaner manufacturing. The machining of a workpiece mainly involves constant-power consumption machining processes e.g. turning and variable-power consumption machining processes e.g. end facing. The cutting power characteristics of the variable-power consumption machining processes are more complex and dynamic, due to change in one of the process parameters (e.g. cutting speed during end facing) than the constant-power consumption machining processes, and have received limited attention in the literature. In the present work, an empirical model is developed to predict the cutting energy consumption of the variable-power consumption machining process i.e. end facing. The end facing experiments were performed on a Computer Numerical Control Lathe in the dry and wet environment to obtain the fitting constants of the developed model. Four validation experiments were performed to confirm the prediction capability of the developed model. The validation experiments confirm that the accuracy of the developed model is more than 96%. Further, the predicted power profiles were in good agreement with the measured power profiles, which shows that the developed model satisfactorily encompasses the influences of the process parameters on the cutting power consumption.

Purpose The purpose of this paper is to establish the structural relationships between the environmental sustainability enablers (ESEs) for the steel supply chain in India and also to classify them on the basis of driving and dependence power with an objective of achieving enhanced environmental sustainability performance. Design/methodology/approach The ESEs were identified through extensive literature review and discussion held with experts from the Indian steel industry. The interpretative structural modeling (ISM) approach was applied to an Indian steel supply chain to arrange these 12 ESEs in different hierarchies of the structural framework on the basis of their driver dependence power and also diagraph was developed to classify them. Findings The water pollution control system, air pollution control system and soil pollution control system were found to be in the bottom hierarchy of the ISM framework and these enablers were also observed in the driving quadrant of the diagraph. These driving enablers should be given priority for its full-scale development, management and monitoring to sustain and enhance the environmental sustainability in the Indian steel supply chain, whereas top management commitment, environment compliance certification and government regulation and incentives are the antecedents for carrying out the sustainability program. Research limitations/implications The outcomes of the current study are limited to the Indian steel manufacturing company in specific but the results will not change significantly for the steel sectors in India. However, an empirical analysis can be used to explore and validate the structural framework for its acceptability. Practical implications The outcomes from the methodology provide the basis for an Indian steel manufacturing industry to develop the right strategy in their quest for environmental sustainability. Originality/value Although the study on ESEs of various industries was reported in the literature, the comprehensive study to identify the significant ESEs related to the Indian steel supply chain for environmental sustainability in specific have been hardly carried out. The current study will definitely be a valuable addition to environmental sustainability literature in general and steel supply chain environmental sustainability in specific.

PurposeThe requirement of high-quality government-supported healthcare services has necessitated the significance of recognizing new management practices to enhance patient satisfaction. Hence, the purpose of this study is to address the patient's enhanced custom needs through the implementation of supply chain value stream mapping (SCVSM) in government-supported drug distribution system (DDS) for enhanced patient's satisfaction.Design/methodology/approachThis study elucidates the role of one popular emerging management technique (i.e. SCVSM) in the healthcare sector by an investigative case study. The DDS in Rajasthan (India) was selected for this study. The data for this analysis were gathered in three ways (i.e. direct observation, documentary analysis and semi-structured interviews).FindingsThe outcome of this current study reveals that it is possible to apply the tool (SCVSM) to investigate the wastes in DDS to deliver the medicines at right time, right quantity and right quality. The application of SCVSM concluded that the various Kaizens (areas needed to improve) in lead time; transportation and routing should be adopted. The study further implemented kaizen on the current SCVSM and developed future SCVSM.Research limitations/implicationsAlthough various stages and functions exist in the healthcare supply chain, the current study is focused on the distribution system of drugs. The proposed approach provides a platform for both researchers and academicians to understand the existing DDS and to implement the SCVSM approach in the healthcare environment. The results show that the proposed SCVSM model is able to identify some operational bottlenecks and wastes which interfere in DDS.Originality/valueIt was observed that limited literature related to lean implementation on DDS and implementation of SCVSM on the healthcare environment in general and government-supported or public in specific are available. The current study on the application of SCVSM in DDS is unique in nature and will definitely add value to the existing literature of the application of value stream mapping (VSM) on the healthcare supply chain management field.

Purpose The purpose of this study is to analyze the post-harvest supply chain enablers (PHSCEs) for vertical integration to enhance rural employability, farmer profitability and rural produce marketability (i.e. market prospects) in the post-harvest supply chain (PHSC). The impact of vertical integration is also explored for various commercial produces. Design/methodology/approach A structural equation modeling (SEM) of PHSCEs for vertical integration was developed to enhance market prospects, rural employability and farmer profitability. The impact of business-to-business (B2B) and business-to-customer market prospects are explored in various dimensions for stakeholders such as farmers, manufacturers (processors), distributors and retailers. The fuzzy technique for order of preference by similarity to ideal solution (F-TOPSIS) was used to prioritize these PHSCEs to improve market prospects and rural employability. Findings The PHSCEs are clustered into three groups, namely, initiatives at the strategic frontier, initiatives at the tactical frontier and concerns for rural employability via vertical integration using exploratory factor analysis, confirmatory factor analysis and SEM to prove the null hypothesis. With F-TOPSIS results, the availability of warehousing was found to be the most crucial enabler when observing the PHSCEs from the initiatives’ perspective. The technology adaptability and availability, institute for training and research and information infrastructure and information visibility were found to be the key PHSCEs when observed from PHSC stakeholders’ perspectives. Research limitations/implications The implementation of this study will improve the rural produce marketability, rural employability, B2B marketing (i.e. effective distribution) and subsequent value chains with the practice of vertical integration for fresh produce at the rural level. Practical implications The outcomes of this study have a key role in developing the rural regions and improving rural livelihoods via value addition. The awareness of commercial cultivation and value addition in rural areas needs to be improved. This will help farmers to earn better revenues with improved market prospects in comparison to the revenues obtained from the cultivation of staple/conventional crops. Originality/value In an era of cold chains and food processing, this study aims to disseminate awareness about value addition for commercial and fresh produces at the rural level. The implication of this study will improve rural produce marketability, rural employability and farmer profitability at the rural level with the level of vertical integration.

PurposeThe purpose of this study is to develop a methodology for the identification, categorization and prioritization of operational government-supported healthcare supply chain barriers (GHSCBs).Design/methodology/approachThis study develops a theoretical background for identifying and segregating relevant GHSCBs and proposes a 5W2H (a Toyota production system) with fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) embedded approach to quantify the causal–effect relationships among the identified operational GHSCBs.FindingsSeven GHSCBs (i.e. uncertainty of demand management, lack of continuous improvement and learning, lack of deadline management, lack of social audit, warehousing equipment unavailability, human resource shortage and inadequate top level monitoring) were identified as significant cause group where the government, top management and decision-makers of government-supported healthcare supply chain (GHSC) have to put efforts.Research limitations/implicationsThe results obtained are specific to the GHSC of Indian perspective, which could be extended to global context. However, the proposed approach can be a base and provide a platform to understand and analyze the interactions among GHSCBs.Practical implicationsThe proposed methodology will show the appropriate areas for allocating efforts and resources to mitigate the impact of GHSCBs for successful implementation of healthcare supply chain.Originality/valueAccording to best of the authors' knowledge, this is the first study of operational barrier for GHSC in India in specific. The use of 5W2H embedded fuzzy DEMATEL approach for the development and analysis of the theoretical framework of Indian GHSCBs is unique in barrier literature.

PurposeThe purpose of this paper is to identify, analyze and orient the enablers of Six Sigma to enhance supplier capability for an Indian manufacturing supply chain (SC).Design/methodology/approachIn total, nine enablers of Six Sigma were identified through an extensive literature review and discussion held with managers/senior managers in different Indian manufacturing companies. The interpretative structural modeling (ISM) approach is applied to the Indian auto ancillary company for developing and analyzing the structural framework of enablers to enhance the supplier capability.FindingsThe enablers such as top management commitment and leadership, supply chain management, standardization, training and education, human resource management and project selection and execution methodology of Six Sigma related to supplier capability have emerged as the prominent enablers, which are driving force in the system for the Indian manufacturing SC.Research limitations/implicationsThis study is restricted to only one Indian manufacturing company. Therefore, the outcomes of the study should not be generalized. Further studies may be carried out for several Indian manufacturing industries to get a more comprehensive implementation approach, their validity and their variation across the different industries.Practical implicationsThe simplicity and clarity of the proposed structural framework of Six Sigma helps in the identification and orientation of enablers for the successful implementation of Six Sigma in the SC. The proposed structural framework can be applied to different manufacturing SCs by allowing managers to structure the enablers considering their unique implementation constraints, which can reflect their priority considerations.Originality/valueThe study goes beyond the conceptual discussion of supplier capability issues. The supplier capability cannot be seen as a standalone approach irrespective of the constraints from the supplier domain as it is in synchronization with the entire SC performance. The enablers and their orientation with respect to the SC are providing a unique contribution toward supplier management planning. The outcomes from the proposed structural framework are used for developing action plans for organization “A” or other organizations to build suitable supplier capability in the SC.

Machining is a prevalent process in manufacturing industries and consumes a considerable amount of energy which causes adverse environmental effects. Establishing an accurate energy evaluation model is essential for a sustainable machining process. An extensive amount of research work is conducted to model energy consumption for the constant material-removal rate machining processes such as turning and milling. However, no significant attempt is made to model energy consumed during variable material-removal rate machining processes like end face turning, grooving etc., which results in a substantial amount of error for the prediction of the total energy required for machining a product. In this work, experiments are performed on a computerized numerical control machine tool to acquire the material-removal energy consumption of end face turning process. The fluke 435 power analyzer is used to measure energy consumption. An empirical model is established between cutting parameters and energy consumed during the end face turning process. The coefficient of determination is used to evaluate the fitness of the model. The results indicate that the model can predict the end face turning energy consumption data accurately. The developed model can be further used to estimate the total energy consumption for machining of a product beforehand in early design stages and to identify the most suitable sustainable machining options.

Machine tools are one of the vital equipment in the manufacturing sector and consume a significant amount of energy during the steady state and transient state. An extensive amount of research work had conducted to model energy consumption in a steady state of machine tools. The researchers generally neglect the transient state because the profile of transient state energy consumption is stochastic, and time duration is concise, which results in a significant error during the prediction of total energy consumption of machine tools. This work presents an experimental study to acquire the transient state energy consumption of a machine tool. The Matlab software is used to process the captured data, and an empirical model is developed for the prediction of energy consumed by a machine tool in a transient state. The coefficient of determination is determined to evaluate the fitness of the empirical model. The results indicate that the model can predict the transient state energy consumption accurately. This approach can be conveniently applied to predict the total energy consumption of machine tools accurately. The accurate energy consumption model of machine tools is the foundation for selection of optimal process parameters of a machining process that leads to sustainable manufacturing

This paper provides an overview of standard literature publications during 2008–2020 on e-waste linked circular economy concept using bibliometric analysis. It presents key topic of waste to wealth and reduction of an environmental hazard. As applied in e-waste management, the concept of circular economy has received considerable attention among the research community over the past few decades. The Scopus database is utilized to retrieve the peer-reviewed articles from 2008 to 2020. A total of 326 publications were identified to focus on e-waste and circular economy. The parameters examined are document type, year wise and journal wise distribution, top authors, top citation and keywords. After that, VOSviewer software is used to visualize the current trends as well as the recent hotspots. It is observed that the topic of a circular economy is encircled by various issues such as e-waste, reuse, recycle, and sustainability in a density map of frequency of appearance of keywords. A few factors are also identified for improved e-waste management and for achieving the sustainable development goals of 2030 though minimized utilization of natural resources.

PurposeThe paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.Design/methodology/approachThe proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.FindingsThe proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.Practical implicationsThe obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.Originality/valueMany researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

PurposeDrug warehouses (DWs) play a crucial role in drug distribution of government-supported healthcare supply chain as it controls both the cost and responsiveness of the logistics activities. The current study proposes a methodology using data envelopment analysis (DEA) to estimate the performance along different dimensions and was applied to 30 government-supported DWs.Design/methodology/approachThis study employs DEA to evaluate the performance and relative technical efficiency of DWs. In this research, four inputs and six outputs are identified based on intensive literature review and discussion with all stakeholders of DWs. The inputs are warehouse storage capacity, temperature-controlled storage capacity, number of skilled employees and operational cost, while the outputs are fill rate, number of generic drugs, volume of drugs, consumption points, inventory turns ratio and time efficiency.FindingsResults show that 30% DWs operate at the most productive scale size with 100% efficiency level while 47% DWs have a significant possibility for further enhancement in productive efficiency and 23% DWs should diminish their operational size to increase their productivity level. It was also found that achieving 100% operational productivity along warehouse space capacity needs significant effort, whereas other three inputs, namely temperature-controlled capacity, number of skilled employees and operational cost, require comparatively less effort. Similarly, it was observed that the performance along the fill rate and time efficiency is satisfactory, whereas the performance along other fours output variables (i.e. number of generic drugs, volume of drugs, consumption points and inventory turns ratio) needs to be improved.Practical implicationsThe findings offer insights on the inputs and outputs that significantly contribute to efficiencies so that inefficient DWs can focus on these factors.Originality/valueAlthough many issues related to DEA have been widely researched and reported, but no literature has been found for analysis of DWs in general and government-supported DWs specifically to find out efficiencies for supply chain performance improvement.

PurposeThe objective of this paper is to compare and evaluate the environmental performance of steel supply chains considering relevant environmental loss factors using Taguchi loss function (TLF) and design of experiments (DOE).Design/methodology/approachThe different environmental loss factors in steel manufacturing supply chain were studied and the significant factors were identified. Their combined contributions along the significant factors were estimated using TLF and DOEs comparing environment losses at different scenarios.FindingsThe proposed methodology using TLF and DOEs was applied to three Indian steel manufacturing companies (Company A, Company B and Company C). The Company A with minimal average environmental loss score is found to be operating its supply chain with higher efficiency and has better environmental performance compared to the other two companies (B and C).Research limitations/implicationsThe results obtained are based on the study carried out in three Indian steel manufacturing companies. Therefore, the results cannot be generalized.Practical implicationsThis paper will definitely show the direction for comparative environmental performance assessment of manufacturing industries in general and steel industries in specific considering environmental loss factors and environmental conditions. It determines individual performance across each environmental loss factor and their combined impact.Originality/valueAlthough there is a need to have comparative performance analysis with respect to environmental losses among steel companies in developing countries such as India, but hardly any study has been reported in this direction. This work will definitely add the value to the supply chain literature in general and environment losses in steel manufacturing supply chain in specific.

The establishment of standard, universal and quality healthcare service is essential for developing countries such as India to enhance the quality of life. Various healthcare schemes such as Jan Aushadhi Scheme, Mukhyamantri Nishulk Dava Yojna etc are operating in India which provides free healthcare services including free distribution of medicines. The medicines provided in these schemes are mostly generic drugs and the supply chain has already been established for it. The variety of generic drugs varies in quantity across different storage points and consumption points, and also having different self-lives, costs and logistics requirements. It was observed that the performance of these supply chain needs to be improved in various dimensions. Therefore, generic drug distribution in Rajasthan is studied and analyzed in the current study. The different aspects of generic drug distribution considered in the study are process view of the existing supply chain, sourcing, ordering and procurement, logistics and warehousing, inventory management and information system. The use of various techniques such as value stream mapping, inbuilt analytical tool for ordering, 5S practice and performance measurement system is explored to develop a strategy for improved resource utilization.

The increasing problem of industrial wastes' (IWs) accumulation and shortage of conventional construction materials such as aggregates can be simultaneously addressed by a careful selection and utilization of IWs in concrete. This paper targets an explicit assessment of the current status of utilization of various IWs in a particular type of concrete, namely, ‘Radiation Shielding Concrete (RSC)’. RSC is a high-density concrete which prevents any potential leakage of the X-rays, gamma rays, and fast-moving neutrons in various establishments such as nuclear power structures, X-ray or radiology department rooms in the hospitals. Traditionally, RSC has been made using high density natural aggregates like haematite, magnetite, or ilmenite, but this practice faces several challenges such as the NA being a limited resource, expensive, and non-available in several parts of the world. This paper reviews the research being carried out for the gainful utilization of various categories of IWs such as slags, supplementary cementitious materials, mines wastes, and a few miscellaneous IWs like polymers and glass, for a partial or complete replacement of conventional constituents of RSC for a greener construction.

Electronic waste is one of the most challenging waste streams to manage. It has become a significant concern in developing countries due to the ever-increasing volume of generation coupled with deficient growth in collection and processing infrastructure. For the various stakeholders, it is of paramount importance to adopt a robust and sustainable collection method for hazard mitigation. The prevalent e-waste collection methods are categorized under four major heads, namely take-back, retail store, door-to-door and curbside collection. The e-waste collection problems are analysed from various perspective, based on literature that cited developing country-specific survey and data that includes India. Economic sustainability and potential risk are included as attributes in the evaluation scheme. We attempt to establish a decision-making model. Discussion with the field experts and decision-makers (DMs) provided the weights for various attributes and sub-attributes. A fuzzy linguistic scale is used to take care of ambiguity in DMs’ opinion. Fuzzy- Analytical Hierarchy Process (FAHP) is used to determine the importance of various attributes and sub-attributes, while Fuzzy-VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) is used to determine the rank of the alternatives. Based on the analysis, ‘take-back collection’ and ‘retail store based collection’ are found the most suitable options for urban and rural regions respectively. The attributes, social awareness and economical sustainability are found to have the highest significance in both cases. Implementation of a collection method is an expensive activity, and the proposed Fuzzy-Multi Attribute Decision Making attempts to capture various attributes and their complex interplay to arrive at a decision on optimum e-waste collection option(s) in a specific locality.

The high rate of population growth in the 21st century is causing mass migration from rural to urban settlements. Rapid urbanization in developing countries generally follows unregulated g...

Purpose This paper aims to review the healthcare supply chain (HSC) literature along various areas and to find out the gap in it. Design/methodology/approach In total, 143 research papers were reviewed during 1996-2017. A critical review was carried out in various dimensions such as research methodologies/data collection method (empirical, case study and literature review) and inquiry mode of research methodology (qualitative, quantitative and mixed), country-specific, targeted area, research aim and year of publication. Findings Supply chain (SC) operations, performance measurement, inventory management, lean and agile operation, and use of information technology were well studied and analyzed, however, employee and customer training, tracking and visibility of medicines, cold chain management, human resource practices, risk management and waste management are felt to be important areas but not much attention were made in this direction. Research limitations/implications Mainly drug and vaccine SC were considered in current study of HSC while SC along healthcare equipment and machine, hospitality and drug manufacturing related papers were excluded in this study. Practical implications This literature review has recognized and analyzed various issues relevant to HSC and shows the direction for future research to develop an efficient and effective HSC. Originality/value The insight of various aspects of HSC was explored in general for better and deeper understanding of it for designing of an efficient and competent HSC. The outcomes of the study may form a basis to decide direction of future research.

Purpose Dealers positioned at the downstream of the supply chains are those who can directly influence demand scenario for the manufacturer. However, manufacturer has to ensure that the influence is favorable by competitively fulfilling the anticipations of the dealers. In this regard, the purpose of this paper is focused on developing sustainable strategic relationships between manufacturer and dealer. Design/methodology/approach In this paper, the basic requirements of the dealers expected to be fulfilled by a manufacturer are analyzed by applying fuzzy Analytic Hierarchy Process and fuzzy Quality Function Deployment. The said approach is implemented in a case situation of a paint manufacturing company in India, and the results are analyzed to provide directions for the company to strategically align manufacturer–dealer relationships. Findings It was found that the case company has to focus mainly on promotions and advertisements, painters meet, and recognition offered to the dealers. Although pricing was also considered as one of the prominent aspects yet through competitive assessment the case company was found to be better off compared to its competitors. Research limitations/implications As the results obtained in the current study are specific to a manufacturing environment, they cannot be generalized for all the companies. However, similar approach can be adopted for analyzing the customer requirements in order to achieve dealers’ attractiveness and satisfaction. Originality/value The application of proposed approach assists a manufacturer to pursue right strategy to practice for achieving dealer attractiveness and satisfaction. This will definitely help the supply chain managers to have the right requirements of the dealers’ are fulfilled on priority.

Purpose The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis. Design/methodology/approach Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables. Findings From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models. Research limitations/implications The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment. Practical implications The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue. Originality/value The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

Purpose The purpose of this paper is to identify, analyze and classify (i.e. driving and dependence power) the government-supported health-care supply chain enablers (GHSCEs) in rural areas of India for enhancing availability and minimizing wastage of generic medicines. Design/methodology/approach A methodology is proposed using interpretive structural modeling (ISM) – fuzzy matriced impacts croises multiplication appliqueeaun classement (Fuzzy MICMAC) analysis to analyze the GHSCEs on the basis of inputs collected from various stakeholders about their driving and dependence power. Findings The performance measurement system, employee recognition and reward, technology adoption, training cell and inbuilt analytical tool for IT system were found to be the appropriate GHSCEs where efforts and resources should be put for enhancing availability and minimizing wastage. Research limitations/implications The proposed approach provides a platform for the both researchers and academicians to understand the GHSCEs and their relationships. It also provides the direction to the government for optimally allocating the efforts and resources to enhance the current performance level of generic drug distribution. Originality/value Although many issues related to health-care supply chain have been widely researched and reported, no literature has been found for analysis of GHSCEs to choose the appropriate set of GHSCEs for supply chain performance improvement in general and developing country like India in specific.

Purpose The purpose of the paper is to identify, analyze and select the enablers for vertical integration of Aloe vera supply chain (AVSC) so that rural employability will be enhanced in the context of Rajasthan, India. Design/methodology/approach Interpretive structural modeling (ISM) was proposed to develop a structural model to identify the right enablers for enhancing the rural employability and business prospects. Also, fuzzy-matrix cross-reference multiplication applied to classification (F-MICMAC) was applied to segregate the enablers into four clusters on the basis of their driving and dependence power. Finally, the significant enablers were selected. Findings Out of identified 13 enablers, three enablers (i.e. institute for training and research, transportation infrastructure and government incentives for value addition) were appearing at the bottom of the ISM structural model and also in the driving quadrant of driver-dependent diagram. Therefore, they are the significant enablers for vertical integration of AVSC to enhance the rural employability in the context of Rajasthan, India. Research limitations/implications The interactions among enablers are not statistically validated. However, the empirical analysis and total interpretive structural modeling may be used for this purpose. Practical implications The outcomes of the study will provide the guidelines for implementation of vertical integration at the village level to enhance rural employability in the context of Rajasthan, India in specific. Originality/value Although a few studies have been reported in the literature related to value-addition process (vertical integration), but the modeling of enablers to segregate and identify the appropriate enablers for vertical integration of AVSC for enhancing employability at the rural areas is unique.

PurposeThe purpose of this paper is to analyze the Environmental Sustainability Enablers (ESEs) for an Indian steel manufacturing company to select the appropriate set of ESEs for implementing and enhancing environmental sustainability.Design/methodology/approachA methodology using fuzzy Decision-Making Trail and Evaluation Laboratory (DEMATEL) is proposed to analyze ESEs capturing multiple experts’ qualitative judgments on their mutual impacts. It is applied to an Indian steel manufacturing company to understand the salient features of the concept.FindingsThe relevant 18 ESEs were classified into cause and effect group and also interactions (i.e. influencing and influenced) of each ESE with other ESEs were evaluated. It was observed that the five ESEs (i.e. competitors’ environmental sustainability strategy, environmental compliance certification, government regulations and incentives, influence of external factors and air pollution controlling system) are the most prominent in the cause group.Research limitations/implicationsThe results obtained are specific to an Indian steel manufacturing company and it cannot be generalized for steel manufacturing sector. However, the current paper can show direction to carry out such work in other manufacturing companies.Practical implicationsThe proposed methodology will be helpful for allocating efforts and resources to enhance the impact of ESEs for successful achievement of environmental sustainability.Originality/valueNo such paper has been reported in the literature with an objective to develop structural framework for Indian steel manufacturing company to improve environmental sustainability.

Energy saving in the industrial sector is mandatory for emerging countries to reduce negative environmental impact. Manufacturing consumes a significant amount of energy and releases a large amount of waste (solid, liquid and gas), resulting in the substantial stress on the environment. Negative environmental impact is due to a large amount of energy consumption by the machine tools in discrete manufacturing processes like turning, milling and drilling etc. This paper presents a multi-objective optimization model to optimize the machining parameters in turning process. Two objectives, surface roughness and power consumption are simultaneously optimized. The machining parameters are cutting speed, feed rate and depth of cut. Technique for order preference by similarity to ideal solution (TOPSIS) is used to identify the optimal turning parameters and the obtained results indicate that depth of cut is the most significant factor followed by the feed rate and cutting speed. The results obtained by the TOPSIS approach are compared with the existing grey relational analysis approach results. It is found that both optimization techniques show different optimal values. The confirmations experiments are necessary to select the best optimization approach.

In the current scenario, the manufacturers' perspectives and their corresponding business practices in dealing with the suppliers have been vastly transformed and are evolving at a faster rate. Even supplier specific investments are committed for strategically developing and aligning the suppliers' capabilities so as to competitively meet the growing needs of the end customers. In this regard, Supplier Development (SD) has been proven to be one of the effective sourcing strategies that manufacturers can practice to befittingly mold their supply bases. However, operating on a global platform and having variegated interests among the stakeholders, the SD Programs (SDPs) have to deal with different challenges to ensure definite return on SD investments. Due to the ever increasing complexity of the supply chains, different circumstances are prescribing the course of SDPs. Thus in the current study, a pragmatic survey was conducted in Indian manufacturing industry to explore the interactions among the barriers to SDPs. The study has comprehensively incorporated the prominent barriers originate from different sides (i.e. supplier, manufacturer, manufacturer-supplier and external environment) and provided a basis for a manufacturer to effectively conduct the SDPs.

The consumption of surimi made from fish has begun to see a continuous rise in demand owing to vast availability, low pricing and copious protein content. Despite visible improvements in processing and distribution trends of Indian surimi, the downstream operations of the surimi supply chain in specific lacks performance due to prolonged delays, extended waiting times and diminishing fish quality. The rising fossil fuel prices, associated emissions and increased focus on energy utilization for organizational sustainability have made this inefficiency a supply chain concern. This has indeed created a room to adjust the existing supply chain design. This work integrates the principles of lean and carries out an environmental analysis observing the utilization of water, energy and raw materials along the supply chain followed by the identification and elimination of wastes. This article aims in eliciting sustainability in downstream operations in the Indian surimi supply chain by identifying and optimizing supply chain operations using Value Stream Mapping (VSM). VSM is used as a tracking tool to capture the existing downstream operations of the surimi supply chain and propose different alternatives. Performance analysis was carried out along sustainability for different supply chain integration scenarios namely No vertical integration, Partial vertical integration and Complete vertical integration using value stream mapping and discrete event simulation model developed. It was observed that the supply chain with complete vertical integration would provide better performance in comparison to other supply chain design alternatives considered. The supply chain with complete vertical integration will reduce 74.52% of lead time, 79.68% of emissions and 81.66% of energy consumption compared to the current state. Therefore, creating a surimi supply chain with cleaner downstream operations that will lead to sustainability enhancement.