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Titanium is highly susceptible to biofouling due to the poor toxicity and excellent biocompatibility. Microbiologically influenced corrosion of cp-Ti induced by Bacillus vietnamensis and the inhibition effect of Ti-5Cu alloy were evaluated using various electrochemical techniques and surface analyses. Electrochemical results revealed that, cp-Ti and Ti-5Cu alloy exhibited excellent passivation in the sterile medium. In biotic medium, however, attachment of B. vietnamensis biofilm to surface of cp-Ti accelerated the active dissolution and poisons repassivation. In contrast, cells adhered preferentially as a form of discrete colonies on Ti

Third-party reverse logistics provider (3PRLP) selection plays an important role in the operation and implemention of reverse logistics or reverse supply chains. Economic and cost based partner relationships are no longer acceptable for 3PRLPs or for organizations that seek to introduce sustainable supply chain management. Recent emphasis on sustainability has made 3PRLP evaluation and selection more complex. In order to advance this area of research and to help further incorporate sustainability into 3PRLPs selection modeling, a novel multi-stage, multi-method, multi-criteria approach is developed. Methodologically, this is the first time that neighborhood rough set (NRS) theory is integrated with TOPSIS and VIKOR techniques. Neighborhood rough set, as a data management and soft computation tool, can help reduce the number of 3PRLP to be evaluated and ranked using TOPSIS combined with VIKOR's compromise conception decision tools. A conceptual application is developed using business, environmental, and social factors within the context of reverse logistics management decisions. A sensitivity analysis evaluating various neighborhood rough set parameters is also introduced to investigate robustness of solutions using the multi-stage methodology. Methodological implications and future research and application directions conclude the paper.

The Ti6Al4V-Cu alloy was reported to show good antibacterial properties, which was promising to reduce the hazard of the bacterial infection problem. For the purpose of preparing Ti6Al4V-Cu alloy with satisfied comprehensive properties, it’s important to study the heat treatment and the appropriate Cu content of the alloy. In this study, high Cu content Ti6Al4V-xCu (x = 4.5, 6, 7.5 wt%) alloys were prepared, and firstly the annealing heat treatments were optimized in the α+β+Ti2Cu triple phase region to obtain satisfied tensile mechanical properties. Then the effect of Cu content on the tribological property, corrosion resistance, antibacterial activity and cytotoxicity of the Ti6Al4V-xCu alloys were systematically studied to obtain the appropriate Cu content. The results showed that the optimal annealing temperatures for Ti6Al4V-xCu (x = 4.5, 6, 7.5 wt%) alloys were 720, 740 and 760 °C, respectively, which was resulted from the proper volume fractions of α, β and Ti2Cu phases in the microstructure. The additions of 4.5 wt% and 6 wt% Cu into the medical Ti6Al4V alloy could enhance the wear resistance and corrosion resistance of the alloy, but the addition of 7.5 wt% Cu showed an opposite effect. With the increase of the Cu content, the antibacterial property was enhanced due to the increased volume fraction of Ti2Cu phase in the microstructure, but when the Cu content was increased to 7.5 wt%, cytotoxicity was presented. A medium Cu content of 6 wt%, with annealing temperature of 740 °C make the alloy possesses the best comprehensive properties of tensile properties, wear resistance, corrosion resistance, antibacterial property and biocompatibility, which is promising for future medical applications.

The circular economy (CE) is an evolving economic and sustainable development model. In this new environment, companies face a more dynamic, uncertain, and complex market environment. These challen...

In developing economies agriculture and farming play crucial roles for economic sustainable development. Farmer credit risk evaluation is an important issue when determining financial support to farmers, improving agricultural supply chain performance, and ensuring profitability of financial institutions. Credit risk evaluation, or creditworthiness, is not a trivial exercise due to various complexities. Honoring complexity is necessary to effectively evaluate and predict farmer creditworthiness. A methodology using fuzzy rough-set theory and fuzzy C-means clustering is used to evaluate and investigate the complex relationships between farmer characteristics, competitive environmental factors, and farmer credit level. The methodology is detailed using actual bank data from 2044 farmers within China. This empirical methodology generates decision rules that provide insight to more complex relationships than can be found through standard econometric multivariate approaches. A rule-based methodological outcome can be used to predict the creditworthiness of farmers and to aid in agricultural loan decision making. Prediction accuracy of the rule-base was 81.16%. A central finding is that education and skills related characteristics are important for determining farmer credit-worthiness. Other implications are presented along with study limitations and future research directions.

Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model.

Sustainable supply chain management (SSCM) faces greater complexity because it considers additional stakeholder requirements, broader sustainable performance objectives, increased sustainable busin...

The concept of circular economy (CE) significantly lessen the waste and environment pollution. A growing number of articles support the need to consider implementing CE within supply chains. Unfortunately, most corporations have not been successful in pursuing this goal, greatly due to existence of several challenges. Up to now, limited articles have analyzed the challenges to CE practices in the leather sector context. To address this issue, this paper introduces a decision support framework for investigating the interdependencies among challenges to CE practices in the leather industry context using Rough-based Decision-Making Trail and Evaluation Laboratory (Rough DEMATEL) technique. According to the results of the study “lack of financial support from authorities” is the most pressing challenge that impede CE implementation. Findings can assist industrial decision-makers to focus on the challenges to CE practices and employ effective strategies and solutions for moving the leather industry towards sustainable development.

Titanium is not fully immune to the microbiologically-influenced corrosion, yet highly susceptible to biofouling. Biocorrosion inhibition effect of Ti-5Cu alloy against Pseudomonas aeruginosa was studied using antibacterial test, various electrochemical techniques and surface analysis. Ti-5Cu alloy was verified to effectively reduce the MIC rate. After 14 days of incubation in P. aeruginosa, the icorr of cp-Ti (89.20 ± 12 nA cm-2) was significantly greater than that of Ti-5Cu alloy (35.42 ± 5.3 nA cm-2). Release of Cu2+ ions which was confirmed by shake-up feature controlled respiration of P. aeruginosa, resulting in strong pitting resistance ability. The outermost layer of Ti-5Cu alloy was predominantly composed of TiO2, but a spot of Ti° was also detected. In contrast, the content of Ti2O3 at sputtering thickness of 0, 2, and 6 nm was higher on the surface of cp-Ti compared with Ti-5Cu alloy. Ti2Cu intermetallic compounds promoted the rapid self-healing property of passive film in the presence of P. aeruginosa.

Controlling the emission of urban passenger transport modes has become one of the most important tasks of governing urban air pollution. Most strategies only focused on carbon emission, whereas neglecting the influences of other pollutants (CO, HC, NOx, PM2.5), especially for upstream emissions from electricity generation caused by the electricity consumed during the operation of electrified transport modes. Based on the multinomial logit model (MNL), this study firstly calculated and evaluated the emission reduction effects brought about by the implementation of targeted emission taxes on different transport modes from the perspective of whole fuel cycle. Taking Jiangning District as an example, our research found that the policy implementing targeted emission tax for different transport modes can not only bring reduce 13.104 tons of CO, 0.327 tons of HC, 0.568 tons of NOx, and 0.140 tons of PM2.5, but also 26,726.82 (euro) of eco-environmental benefits for the treatment of air pollution. Our study can provide useful insights for shifting the structure of urban passenger transport modes, especially promoting the transfer of private cars to the urban green transport systems, to alleviate urban air pollution by formulating effective emission reduction strategies.

Lean manufacturing practices (LMPs) and corporate environmental sustainability are becoming inextricably linked. Throughout the lean and green debate, many organisations have recognised that LMPs h...

Flow behavior and processing map play important roles in the hot deformation process of titanium alloys. In this research, compression Gleeble tests have been carried out to investigate the stress–strain relationship at temperatures ranging from 700 to 1000 °C and strain rates ranging from 0.001 to 1 s−1 for ATI 425 titanium alloy. Arrhenius type constitutive equation was obtained to describe the compressive flow behavior with modification of additional deformation dead zone, friction model, temperature model and strain rate. The introduction of novel calculation method for α value in Arrhenius equation gives more accurate fitting than traditional one. Processing maps were drawn based on the distribution of dissipator co-content, and optimized deformation temperature and strain rate range obtained. It is proven to be accurate and effective through the experimental results. The microstructure analysis shows that more dynamic recrystallization can be achieved in the area with larger η value on the processing map.

PurposeThe purpose of this paper is to identify how organizations can evaluate the green product deletion decision within an environmentally sustainable consumption and production environment through a hybrid multistage multiple criteria evaluation approach.Design/methodology/approachThis paper proposes a decision-making model by integrating “soft computation” using neighborhood rough set theory, fuzzy cluster means, and cumulative prospect theory. Literature is used to identify various factors for the decision environment. An illustrative problem provides insights into the methodology and application.FindingsThe results indicate that green products can be evaluated from both their relative environmental burdens and benefits. Sustainable consumption and production factors that play a role in this multifactor decision are identified. The results show that a comprehensive evaluation can capture an effective overall picture on which green product(s) to delete.Research limitations/implicationsThe opaqueness of the proposed methodology may cause less acceptance by management. The methodology made a number of assumptions related to the data. An actual application of the tool rather than just an illustrative example is needed.Originality/valueThe main contribution of this study is the novel integration of supply chain perspectives, both upstream (supply and production) and downstream (demand/usage), with green product deletion decision making. The hybrid multistage technique has advantages of being able to incorporate many factors that have a variety of quantitative and qualitative characteristics to help managers address green product deletion issues as well as its impact on greening of supply chains and organizational environmental sustainability. This paper adds value to product deletion, supply chain management, and sustainable production and consumption literatures.

This paper compares perishable retailers’ quantity commitment (QC) to rational expectations equilibrium (REE) when they sell at a markdown strategy over two periods, where strategic consumers are disappointment averse and elation seeking, respectively. Strategic consumers weigh the expected utility of buying the product at high and low price to determine purchase timing. In low-price period, due to the risk of shortage, strategic consumers will feel a sense of elation (disappointment) which increases (decreases) their utility. In addition, when disappointment has a higher impact on utility than elation, strategic consumers are disappointment averse, otherwise, they are elation seeking. The results show that, QC is always not inferior to REE. Particularly, when the disappointment aversion (DA) level is below the threshold or in elation seeking (ES) case, QC is strictly superior to REE when the strategic consumers’ proportion is above the medium critical value. Moreover, in this case, the value of QC increases in the strategic consumers’ proportion and the DA level (or decreases in the ES level) when the proportion is medium, while it is independent of the proportion and decreases in the DA level (or increases in the ES level) when the proportion is high.

Outsourcing is recognized as a strategic instrument for companies to move towards diversified operation advantages and efficient global market. However, outsourcing agreements may fail due to insufficient risk consideration and evaluation. This requires an effective risk evaluation approach that necessitates an entire comprehension of the system, its requirements and dimensions. This paper proposes a novel fuzzy group multiple-criteria decision-making approach through integrating triangular fuzzy hesitant sets (TFHS), Failure Mode and Effect Analysis (FMEA) and combined compromise solution (CoCoSo) algorithm. This approach (F-FMEA-CoCoSo, henceforth) analyzes and evaluates the possible risks level of alternative outsourcing providers. It hereby provides measurable information for managers according to the linguistic opinions of industrial experts. Utilization of hesitant fuzzy variables allows decision making participants to state their opinion more precisely. A case study of an Iranian chemical company is used to exemplify the applicability and suitability of the proposed F-FMEA-CoCoSo approach. The model reflects an analytical approach while experts are experiencing considerable uncertainty in risky conditions of outsourcing operations. CoCoSo contains an integrated approach of compromise solutions and its utilization enables investigators to assure results reliability which is proven via the sensitivity analysis. Through this study, we found that risk evaluation of outsourcing providers must consider four key-factors: multi-experts, multi-criteria, multi-uncertainties and measurability.

This work focuses on firms that sell perishable products to risk-averse strategic consumers over two periods: the full-price period and the clearance period. The risk-aversion level of strategic consumers affects their expected utility in both periods, which will determine their purchasing behaviour. Typically, firms implement either quantity commitment (QC), price commitment (PC) or rational-expectation equilibrium (REE) policies to plan for risk-averse strategic consumers’ purchasing behaviour. In this study, we develop and analyze a novel model on the sales policy, full price, and the initial inventory decisions in the above setting. Results show that the risk-aversion level of strategic consumers has structural implications on optimal policies. First, firms should implement QC when the risk-aversion level is higher than a critical threshold; otherwise, PC is better. Second, the critical threshold of risk aversion increases as the unit cost of a product and clearance price decrease and as the consumer valuation increases. This can affect the optimal policy (QC, PC or REE). Finally, PC is always optimal when strategic consumers are risk seeking. We also conclude that the optimal policy is significantly affected by the proportion of strategic consumers when we consider different kinds of consumers (e.g. myopic consumers).

Blockchain technology has been widely touted for building and supporting supply chain management capabilities. The technology has substantial potential to enhance operational effectiveness and profitability. Blockchain platform evaluation and selection still requires investigation. Blockchain platform selection and adoption decision making in a multi-organizational supply chain context is complex. Effective blockchain adoption and operations requires consideration of multiple vendors—service providers—and platforms while satisfying multiple users and stakeholders. This study introduces a managerial decision support method to assist organizations evaluate and select joint blockchain service vendor and platforms for multiple organizational functions and organizations. Using literature and practice, we introduce blockchain service vendor and platform attributes from user, vendor, and platform perspectives. A social network theory lens sets the foundation for an innovative group decision-making method—a DEMATEL-based hierarchical best-worst method—integrating characteristics from this decision environment. The technological and distributed network nature of blockchain technology requires evaluation by decision makers from various levels of a supply chain network. These decision makers will likely have varying blockchain technology knowledge and subjective preferences that need integration. The proposed method helps to operationalize social network relationships to comprehend partial and idiosyncratic expert opinions about blockchain technology. An illustrative example and various scenarios are presented to identify managerial and research implications. Methodological limitations and future research are presented for this emergent managerial and technological concern across supply chains.

Supplier segmentation is an important strategic activity for companies. The main purpose of segmenting suppliers is to more easily manage a large number of suppliers by formulating relationship management strategies for subsets of suppliers, which is more efficient than separate unique strategies for each supplier. Existing supplier segmentation approaches have paid limited attention to environmentally related criteria. Given the increased importance of sustainable and green supply chains, this points a large gap in the literature. Thus, a green supplier segmentation model is proposed in this study. A supplier potential matrix is used to evaluate suppliers with respect to two dimensions, capabilities and willingness, with respect to environmental issues. Given the multicriteria nature of this problem, a novel hybrid multicriteria methodology is used to evaluate the problem. Rough set theory is used to calculate the weight of each criterion for suppliers’ capabilities and suppliers’ willingness. VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is then used to determine an overall score for each supplier. Finally, fuzzy C-means is used to segment the suppliers while considering the overall score for each supplier. An application of the proposed model for suppliers of a large chemical company is used to evaluate the feasibility of this technique.

The mining and extractive industry’s operations have significant harmful environmental consequences. Mining companies have started adopting green supply chain management (GSCM) practices which include green information technology systems (GITS) to help provide economic benefits while seeking minimal environmental damage. These mining organizations face significant hurdles related to introducing and implementing various GSCM practices which can address some of the environmental burdens. This study addresses this issue by adopting a GSCM practices framework and applying a novel decision support method that integrates grey numbers with DEMATEL and the NK model for evaluating and developing an implementation path model. Using a multiple case field study with input from managers of the Ghanaian gold mining industry, the adopted GSCM practices framework and methodology is applied. The results provide an evaluation and development path model to guide these organizations and managers for GSCM planning and investment decisions. The path results show that these organizations should first develop SSP (Strategic Supplier Partnership) with their suppliers for implementing GITS (Green Information Technology and Systems) and other GSCM practices. These results provide some exploratory insight and guidelines for managers and policy-makers who seek to integrate green initiatives. This study also sets the stage for further investigation of organizational greening in developing countries and the mining industry.

Globalization and greening are two major trends manufacturing. Each trend has increased organizational and supply chain risk and uncertainty. Advanced manufacturing technology (AMT) are resources that can competitively aid modern industry in this volatile and complex environment. Thus, the evaluation, selection and implementation of more environmentally conscious AMT is important for meeting global requirements, especially with respect to environmental sustainability. Although a wide variety of methods to support AMT selection and evaluation processes exist, important aspects including green flexibility performance and psychological characteristics of decision makers under risk and uncertainty are missing. This paper presents a novel method for general investment appraisal of AMT, but especially introducing the context of green flexibility within manufacturing organizations. This paper aims to (1) develop effective green flexibility measures for manufacturing firms, incorporating various economic and environmental flexibility types, (2) introduce a hybrid possibility multiple criteria decision model for AMT evaluation and ranking integrating neighborhood rough set theory and cumulative prospect theory based on the three-parameter interval grey number, and (3) investigate the application of the proposed method in an illustrative case example to help manufacturing practitioner and researchers understand how to investigate various AMTs in this decision environment. Various advantages and disadvantages of the methodology are introduced. The results are evaluated with theoretical, methodological and managerial implications identified. This paper sets the foundation for significant future research in green manufacturing flexibility in an AMT environment.

Purpose This paper aims to introduce a joint DEMATEL and NK methodology to develop a process model for introducing and implementing relational supply chain practices for low-carbon supply chains. Using this process model as a guide, insights into specific practices and how to implement these relational practices to achieve competitive advantage across organizations are introduced. Design/methodology/approach Low-carbon cooperation practices framework based on the relational view is developed. A methodology based on DEMATEL and the NK model is used to construct a sequential process model for introducing and implementing these relational practices. Empirical data from three manufacturing organizations in China are utilized to validate the model. Findings Initial results provide a sequence of relational practices for guiding those organizations and their suppliers for healthy and low-carbon development. Interdependencies between relational practices are analyzed and evaluated from four aspects. Insights into the broader application of the methodology and initial results from both a research and managerial perspective are presented, especially with consideration of the China, an emerging economy, context. Research limitations/implications The methodology remains relatively abstract in nature, yet the tool can provide very useful interpretations and information for both researchers and practitioners. Practical implications This paper stipulates that in addition to internal operational practices, the relational practices between buyer and supplier may be equally important to achieve a low-carbon outcome, especially in supply chain setting. This paper also shows that not only the relational practice itself but also the implementation sequence of the relational practices can relate to performance. According to the authors’ initial results, organizations in this study should first develop product development cooperation, then exchange carbon knowledge and implement effective governance and last build a trust relationship with its suppliers for low-carbon cooperation. Originality/value This is one of the few approaches that directly evaluates and identifies the interdependencies among relational practices and to construct a process model for introducing and implementing low-carbon supply chain cooperation. It is also the first time that the NK model has been integrated with DEMATEL. Focusing on Chinese supply chain carbon emissions concerns is also a unique perspective.

Green supplier development (GSD) is an important operational strategy that significantly contributes to environmental performance of the supply chain. However, green supplier evaluation for development, the first step in green supplier development, has not been widely reported in the literature. This article aims to develop a green supplier evaluation methodology for supplier development. Poor suppliers set and weak areas of each supplier are identified so that green supplier development practices can be implemented. This study proposes an environmental evaluation methodology based on environmental performance and environmental practices and use of development priority number to effectively assess suppliers’ green capability. A novel interval-valued intuitionistic fuzzy numbers based reference-neighborhood rough set approach is then used to identify the poor supplier set and suppliers’ weak areas. The feasibility of this methodology is illustrated through a case study in a large chemical company. The methodology proposed can guide organizations when developing more specific GSD plans through identifying the poor suppliers and the weak areas.

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses., This article belongs to the Special Issue Artificial Intelligence in Health Care, The research was funded by the National Natural Science Foundation of China under Grant 71771156, 71971145 and the APC was funded by the National Natural Science Foundation of China under Grant 71971145.

Green supplier development focuses on helping organizations integrate activities to improve the natural environmental performance of their supply chains. These green-supplier-development programs require substantial resources and investments by a buyer company. Investigation into investment management in this context has only begun. This paper introduces a methodology to help manage investment in green-supplier-development and business-supplier-development practices. Managing these practices and their outcomes requires managing of a large sets of data. We propose a combination of rough set theoretic and fuzzy clustering means (FCM) approaches; first to simplify, and then sharpen the focus on the complex environment of evaluation of the investment decisions. The combined methodology, based on performance measures of supplier practices and agreed-upon investment objectives, identifies a set of guidelines that can help make decisions about sound investments in the supplier practices more effectively and judiciously. Various steps involved in the methodology are illustrated through using an example developed to highlight the salient steps and issues of the methodology. We show how the results may be interpreted to obtain many insights useful from both practical and research perspectives. Although the impetus to developing this methodology came from sustainability considerations, the methodology is general enough to be applicable in other areas where management and evaluation of investments is based on large data sets.

The fourth industrial revolution, also labelled Industry 4.0, was beget with emergent and disruptive intelligence and information technologies. These new technologies are enabling ever-higher levels of production efficiencies. They also have the potential to dramatically influence social and environmental sustainable development. Organizations need to consider Industry 4.0 technologies contribution to sustainability. Sufficient guidance, in this respect, is lacking in the scholarly or practitioner literature. In this study, we further examine Industry 4.0 technologies in terms of application and sustainability implications. We introduce a measures framework for sustainability based on the United Nations Sustainable Development Goals; incorporating various economic, environmental and social attributes. We also develop a hybrid multi-situation decision method integrating hesitant fuzzy set, cumulative prospect theory and VIKOR. This method can effectively evaluate Industry 4.0 technologies based on their sustainable performance and application. We apply the method using secondary case information from a report of the World Economic Forum. The results show that mobile technology has the greatest impact on sustainability in all industries, and nanotechnology, mobile technology, simulation and drones have the highest impact on sustainability in the automotive, electronics, food and beverage, and textile, apparel and footwear industries, respectively. Our recommendation is to take advantage of Industry 4.0 technology adoption to improve sustainability impact but each technology needs to be carefully evaluated as specific technology will variably influence industry and sustainability dimensions. Investment in such technologies should consider appropriate priority investment and championing.

Planning and synchronizing operations are fundamentally essential to ensure sustainability in Supply Chains (SCs) (Bag et al., 2020). Traditionally operations management is primarily focused on efficiency, effectiveness, and economy of SCs. However, growing pressures from governments and SC stakeholders are forcing organizations to recalibrate their operations strategies to include environmental and social sustainability perspectives. There is greater impetus since proposed Sustainable Development Goals (SDGs) for 2030 by the United Nations (United Nations Development Programme, 2015). Research in operations management has also started embracing sustainability from an operational excellence perspective in SCs (Mani and Gunasekaran, 2018; Sehnem et al., 2019). The Sustainable Supply Chain (SSC) is a concept that concurrently integrates ecological, economic, and societal measures of operations in an SC. The Triple Bottom Line (TBL) concept clubs all these three metrics of sustainability. Organizations need to evaluate not just their own operations, but also operations across the supply chains, considering all the three metrics of sustainability. A localized and short term approach to sustainability is not appropriate (Jabbour et al., 2019). The operational performance of the SCs needs to be evaluated based on their trade-offs with TBL model of sustainability. Sustainability in operations is a significant emerging challenge for organizations (Bhandari et al., 2019; Ghadimi et al., 2019). SC operations such as quality management, lean manufacturing, six sigma, Information Technology (IT) implementation, material sourcing, inventory management, and reversed logistics operations are frequently studied in SSCs (Li, 2013; Gaustad et al., 2018; Farias et al., 2019). Most existing works on SSCs discuss strategies such as corporate social responsibility, green sourcing and supplier selection (Luthra et al., 2017), the various R's of sustainability- reduce, recycle, reuse, remanufacture, redesign (Scur and Barbosa, 2017). However, in recent years, a number of operational excellence based paradigms have been integrated in the SSC literature, such as big data (Bag et al., 2020), blockchain, Circular Economy (CE) (Gaustad et al., 2018), Inter-organizational Information Technologies, Internet of Things (Zhao et al., 2019), Industry 4.0, Theory of Constraints (Koh et al., 2017), Business Process Reengineering (Kumar and Rahman, 2014), etc. These research paradigms explores different dimensions of SSCs and contribute further by providing decisions support such as routing decisions, production scheduling, integration mechanisms in SSCs, risk mitigation, sustainable performance indicators, lean strategies, policy enablers, and barriers, thus, enhancing performance and excellence across SSC operations (Jabbour et al., 2019). There is a great focus given towards the impact assessment and management of SC operations, using impact assessment techniques such as Life cycle assessment, environmental management system, and carbon footprint assessment (Farias et al., 2019). There is also a growing literature that contributes towards sustainable performance measurement in SCs, especially using the TBL concept. Research in these dimensions of operations management can help managers implement sustainable operational strategies and better understand the trade-offs by applying a holistic sustainable business perspective. The theoretical contributions from these researches help managers adopt innovative approaches towards sustainability in SC operations. Still, research in SSCs has a long way to go. The up-scaling of supply chain sustainability will require managers and practitioners to tackle complexities and potential challenges with holistic and systemic focus. Expounding the role of operational excellence activities is crucial for SC sustainability. The journey towards sustainability in SCs will require managers to tap on operational excellence approaches to influence various sustainability performance dimensions such as SSC flexibility, business competitiveness, coordination and collaboration, dynamic and relational capabilities, SC transparency, technology management and innovation (Mangla et al., 2019). However, there is a growing need to understand how operational excellence can be leveraged to improve and transform SSC structures significantly, to further enhance these SC sustainability performance dimensions. The present Virtual Special Issue (VSI) seeks to contribute in the same direction by encouraging researchers to develop an understanding of how initiatives and strategies for operational excellence will advance sustainability in SCs.

Green supplier development (GSD) has emerged as an important green supply chain management activity since the supplier environmental performance is the overall greatest contributor to environmental issues in manufacturing industry. In order to successfully implement GSD, manufacturing firms should seek to identify barriers and remove them in the initial stages of green supplier development programs (GSDPs). These barriers do not only affect the GSDPs but also influence each other. Their relationship and their role in the implementation of GSDPs require additional investigation. In this article, GSD barriers are identified for the manufacturing industry. A hybrid methodology is developed involving Grey-DEMATEL and Grey-ISM to identify their relationships and roles in the implementation of GSDPs under uncertainty. Empirical data from the equipment manufacturing industry in China are utilized to gain insights into the GSD barriers phenomena. Findings using Grey-MICMAC analysis imply that four barriers have been identified as weak linkage variables. The results obtained from Grey-ISM analysis show that manufacturing firms first and foremost need the support of government regulations externally and the top management commitment internally to effectively implement GSDPs in the manufacturing industry in China. Implications and directions for further studies are identified based on the exploratory findings.

Supply chain flexibility is an important operations strategy dimension for organizations to achieve and maintain competitive advantage. With rising greener customer expectations and increasingly stringent environmental regulations, green supply chains are now viewed as another competitive weapon. Green supply chains are characterized by higher complexity and turbulence. Green supply chain flexibility can aid organizations function in this complex and uncertain environment, yet investigation into this area is very limited. This paper aims contribute to this field by investigating green supply chain flexibility achievement through information systems. This paper introduces a green supply chain flexibility matrix framework. Given the large data needs, as described in the matrix, a novel probability evaluation methodology that can help predict rankings of projects and programs is introduced. The methodology extends a TOPSIS based three-parameter interval grey number (TpGN) approach by incorporating neighborhood rough set theory (RST) to evaluate IS programs’ green flexibility support capability. The results of this methodology are more objective and effective for two reasons. (1) The results are predictive rankings based on probability degree instead of the fixed deterministic ranks. (2) Neighborhood rough set theory used in this study can limit loss of information when compared to rough set theory, yet still simplify extensive data sets. This paper also identifies study limitations and future research directions for green supply chain flexibility.

One of the most critical operational practices influencing the environmental sustainability of organizations and their supply chains is the transport of materials, products and people. The carbon footprints, materials depletion, and general pollution emissions from transport vehicles makes their environmental burdens significant. Thus, identifying, selecting and implementing more environmentally conscious transportation vehicles can be of paramount importance for the development and management of greener supply chains. Given the relative importance of this issue, it is surprising that research on transport fleet evaluation, especially from an environmental sustainability perspective, has been rather limited. A primary challenge in this context is the broad range of influencing factors that need to be considered, many of which are not fully and easily measurable. This paper aims to (1) develop a holistic framework for sustainable transport fleet appraisal incorporating various vehicle performance, economic and environmental criteria, (2) introduce a novel hybrid approach for sustainable transportation vehicle evaluation and selection by combining a three-parameter interval grey number with a rough set theory and VIKOR method, (3) investigate the application of the proposed approach in a case example where empirical data is collected from industry experts, (4) evaluate the robustness of the methodology through sensitivity analysis experiments, and (5) provide practical insights and directions for future research in this area.

Purpose – The purpose of this paper is to threefold. The first purpose is to review and critically analyze corporate sustainability development (CSD) research in China. Second, the paper extracts a unified theoretical framework among CSD drivers, CSD practices, and corporate performance in China. Finally, it seeks to identify links between CSD and industrial management and data systems (IMDS) topics. Design/methodology/approach – A comprehensive and structured review of the research literature investigating CSD in China was completed. Categorizations and classifications of the literature were summarized. A critical analysis of the literature resulted in a generic theoretical framework that can be used for evaluation of the literature and further investigation. Findings – The literature review found over 189 papers on CSD in China published from 1997 to 2013. The framework developed focussed on relationships among drivers, practices, and performance within a CSD in China context. The framework provides useful insights into the implementation of CSD practices. The integration of the three dimensions of sustainability and decision-making methodology are still rare. Specific features of CSD are also reviewed with a linkage to IMDS research around information technology, business process modeling, and supply chain management. Originality/value – This is one of the first works to provide a comprehensive focus on CSD in China. The theoretical framework was developed for CSD in China to clarify the relationships between the drivers, the corporation’s characteristics, CSD practices, and corporation performance and will prove useful for future research development and investigation. The linkage to IMDS topics is novel and will help further research related to CSD in China for this journal.

The effects of temperature and pressure on density, microstructure and mechanical properties of powder compacts during hot isostatic pressing (HIPping) were investigated. Optimized HIPping parameters of temperature range from 900 to 940 °C, pressure over 100 MPa and holding time of 3 h, were obtained. Tensile properties after different heat treatments show that both the geometry of samples and cooling rate have a significant influence on mechanical properties. Finite element method was used to predict the temperature field distribution during HIPped sample cooling, and the experimental results are in agreement with simulation prediction. The interaction of HIPping parameters was analyzed based on the response surface methodology (RSM) in this study.

Supplier development is a critical competitive endeavor for organizations and their supply chains. Investigation into this area has increased over the past few years, yet further understanding of suppler development is needed. Specifically, recent supplier development investment practices have shown a shift from standalone actions by organization or supplier to joint actions between organization and suppliers. Faced with this phenomenon, the goal of this paper is to develop a theoretic model based on the Cobb–Douglas production function. The study focuses on determining optimal supplier development investment strategies with respect to joint actions that increase supplier production capability for the benefit of both the focal organization and its suppliers. The supplier development investment strategies mainly refer to joint actions between an organization and multiple suppliers through capital resources (tangible) investments, knowledge (intangible) investments, and sharing cost of capital resources (tangible) investments. Using various game theoretic models, we reveal how supplier development investment strategies and profits of all the members are affected by various buyer-supplier relationships and investment returns to scale reasons. Whether the focal organization (buyer) has any incentives to share cost of capital resources (tangible) investments is also investigated. Our first finding is that supplier development investment activities motivation is derived from increases in supply volume for the “increasing returns to scale” situation, and derives from increasing the organization and suppliers’ marginal profit in the “decreasing returns to scale” situation. Secondly, the cooperative relationship is more economically beneficial to the supply chain, but it also requires more capital resources and knowledge expenditures (investments) than a non-cooperative relationship. Thirdly, through numerical analysis it is found that the cooperative relationship can not obtain Pareto efficiency for all the members of the supply chain when using the Nash bargaining model. Additional gaming insights and implications are also provided from parametric analysis. Opportunities for further research are also presented.

Purpose – The purpose of this paper is to introduce a methodology to identify sustainable supply chain key performance indicators (KPI) that can then be used for sustainability performance evaluation for suppliers. Design/methodology/approach – Initially the complexity of sustainable supply chain performance measurement is discussed. Then, a two-stage method utilizing neighborhood rough set theory to identify KPI and data envelopment analysis (DEA) to benchmark and evaluate relative performance using the KPI is completed. Additional analysis is performed to determine the sensitivity of the KPI set formation and performance results. Findings – The results show that KPI can be determined using neighborhood rough set, and DEA performance results provide insight into relative performance of suppliers. The supply chain sustainability performance results from both the neighborhood rough set and DEA can be quite sensitive parameters selected and sustainability KPI sets that were determined. Research limitations/implications – The data utilized in this study are illustrative and simulated. Only one model for the neighborhood rough set and DEA was utilized. Additional investigations using a variation of rough set and DEA models can be completed. Practical implications – This tool set is valuable for managers to help identify sustainable supply chain KPI (from among hundreds of potential measures) and evaluate sustainability performance of various units within supply chains, including supply chain partners, departments, projects and programs. Social implications – Sustainability incorporates many business, economic and social implications. The methods introduced in this paper can help organizations and their supply chains become more strategically and operationally sustainable. Originality/value – Few tools and techniques exist in the sustainable supply chain literature to help develop KPIs and evaluate sustainability performance of suppliers and the supply chain. This paper is one of the first that integrates neighborhood rough set and DEA to address this important sustainable supply chain performance measurement issue.

Wind-blown sand is now one of the most serious environmental issues around the world. The properties of wind and the distance from sand source to the affected locations are two most important factors for assessing the effect of the sand source on its neighborhood. To acquire better assessment result, the methods of wind vector interpolation and the measurement of the distance from sand source to the affected location have been researched. The adopted interpolation procedure for wind vector includes three steps: 1) resolve the existing wind vectors, 2) interpolate based on the resolved wind vectors using inverse distance weighted interpolation, and 3) compose the interpolated wind vectors. This paper also presents a new measuring method and its theoretical basis to acquire the distance from sand source to the affected location. This paper claims that the distance along the wind direction, based on which the range of the damage can be acquired, is more valuable for the damage evaluation. The presented method has been applied to three regions of Southern Xinjiang, China.

Although business process management (BPM) is an important organizational practice for improving operational competitiveness of organizations, research has shown that as many as 60–80% of BPM initiatives are unsuccessful. This study provides a methodology to evaluate BPM implementation critical success factors (CSFs) that can aid project managers make proper BPM investment strategies. Through a review of the literature, eight CSFs for the successful implementation of BPM are identified. To help advance research on the implementation of BPM, this paper uses multi-site field study data with a novel grey-based DEMATEL (the decision making trial and evaluation laboratory) approach to visualize the structure of complicated causal relationships between these CSFs and obtain the influence level of these factors. The field study data uses three Chinese manufacturers as the setting. The four most important factors found in the field study, from amongst the identified CSFs, include Strategic alignment, Top management support, Project management and a Collaborative environment. We also found a number of direct and indirect relationships amongst the CSF factors. Insights into the application of the technique and results from both a research and managerial perspective are presented. Aggregate analysis for the methodology and future research directions are also introduced in the final section.

Green and sustainable information technology (GSIT) can be an important and strategic decision for many organizations. Strategic GSIT decisions can influence a variety of internal and external organizational stakeholders. GSIT justification and evaluation is critical to organizations, especially those seeking the ecological modernization `win-win' of introducing green technologies into organizations. The literature has seen very few tools and approaches to aid in the explicit justification of strategic GSIT. Although numerous models exist for regular IT, there are certain characteristics including a greater number of factors, especially intangible and socially focused factors, requiring greater investigation of tools for the evaluation and justification process. The identification of appropriate metrics and categorizations to use in advanced strategic appraisal techniques for GSIT is in itself a non-trivial exercise. Thus, in this paper issues facing the strategic justification of GSIT set the stage for introduction of a novel, flexible and comprehensive evaluation approach utilizing a grey systems, fuzzy, and TOPSIS multiple criteria foundation. An illustrative application for evaluating and selecting alternative green data center designs sets the stage for an illustrative example and sensitivity analysis. The results are evaluated including discussion of practical and research implications. Directions for future research are also identified.

It is a challenge in the research field of GIS-T and ITS to design a road network model that can well express the full range of transportation information. The existing Multi-hierarchy road network models often have some defaults, such as repeated storage of data, high degree of redundancy, difficult to analyze network, etc. Based on the analysis of image pyramid model and road feature hierarchy, a road network pyramid model has been proposed in this paper. This model has the adaptive ability to express multi-scale information and can descript road network the most detailedly with comprehensive road characteristic of all hierarchies. Low degree of redundancy of spatial and topologic data is another advance of the model. So this model is more suitable for multi-hierarchy analysis of road network.

Public and private organizations have started to respond to various stakeholder and market pressures to improve their environmental and social sustainability performance. Government agencies represent one of the most pertinent stakeholders. Government stakeholder pressures to encourage greater organizational sustainability include coercive measures such as penalties, fines, and removal of license to operate if organizations are unable to meet specific regulatory requirements. Yet, noncoercive approaches are also available to government agencies and regulators for encouraging the greening of organizations and markets.

Purpose – The purpose of this paper is to propose a framework integrating the Hart and Milstein (2003) strategies for organizational sustainable development (SD) with the ideas of Kleindorfer et al. (2005) on sustainable operations management (SOM), which requires guidance of green supply chain management (GSCM). Design/methodology/approach – The construction of the framework was based on previous studies that discussed synergies between operations management principles with environmental bias and studies on adoption of GSCM practices. Findings – The proposed framework guides managers to reconcile operations management practices/principles that are already being implemented in organizations with an environmental perspective because these practices sustain organizations to simultaneously reach SOM and SD. Originality/value – The paper presents a framework that provides guidance on how organizations can seek sustainability in their operations, considering that articles on the topic of sustainability have not been developed with this specific focus.

Performance measurements are central to improving organisational competitiveness. Organisations and researchers have developed and investigated various performance-measurement systems to manage and improve internal and external operations for logistics and supply chains. Yet, formal modelling tools and approaches for organisations to help evaluate the relationships between the performance measures and the desired competitive outcomes are limited, especially in logistics and supply-chain management functions. To help address this gap, we introduce a novel application of neighbourhood rough-set theory for the identification and selection of performance measures related to externally derived desired outcomes on the sourcing function. Our contribution includes introducing an innovative neighbourhood rough-set approach using elements of the Supply-Chain Operations Reference model. The model may aid in determining a core set of external logistics and supply-chain performance measures to internal performance exp...