Modeling big data enablers for operations and supply chain management

Analyzing the enablers of circular supply chain using Neutrosophic-ISM method: lessons from the Indian apparel industry

Purpose

This research identifies the enablers for implementing circular supply chains (CSCEs) and analyzes interrelationships between them to quantify their driving and dependence power to understand the critical CSCEs.

Design/methodology/approach

Initially, 10 CSCEs are identified for the Indian apparel industries through an extant literature review and validated using the Delphi method by seeking experts' opinions. The identified CSCEs are subjected to a novel neutrosophic interpretive structural modeling (N-ISM) method to capture the interrelationships between CSCEs and compute the driving and dependence power of CSCEs.

Findings

The findings of the present research work revealed that “supportive legislative framework, awareness of circular economy's potential for revenue gain and availability of trained research and development (R&D) team” are the critical CSCEs that need to be considered while implementing a circular supply chain in apparel industries.

Research limitations/implications

This study offers insightful implications to guide practitioners in implementing the circular economy in apparel supply chains.

Originality/value

This research work is one of the earlier studies to analyze the enablers for implementing circular supply chains. This study has explored CSCEs in the context of apparel industries. From a methodological perspective, the novel N-ISM method is worth highlighting as the originality of the work.

Fuzzy ISM–DEMATEL modeling for the sustainable development hindrances in the renewable energy supply chain

Purpose

By replacing traditional fossil fuels, renewable energy (RE) has the potential to become an outstanding sustainable energy supply. However, owing to technological, economic, social and legal constraints, RE is still in its early stages of development. Hence, this paper aims to analyze the sustainable development hindrances in the RE supply chain (RESC).

Design/methodology/approach

Twenty-three hindrances to the sustainable development of the RE industry were investigated in this research, which included a review of the expert opinion and literature. Then, a mutual relationship between the hindrances by integrating interpretive structural modeling and decision-making trial and evaluation laboratory in fuzzy environment was established. Furthermore, using the cross-impact matrix multiplication applied to a classification analysis, these hindrances were grouped.

Findings

The findings show that the important hindrances are “lack of standards for the RESC (H19), lack of entrepreneurship support (H21), lack of incentives/subsidies to encourage RE producers to compete (H30) and lack of governmental support for sustainable supply chain solutions (H31).

Originality/value

This research provides unique insights into the area of sustainability in RESC. To the best of the authors’ knowledge, this is the first paper to analyze the sustainability hindrances in the RESC.

Modelling the factors affecting Quality of Life among Indian police officers: a novel ISM and DEMATEL approach

Background

This study examines quality of life (QOL) of a sample of police officers in India. The concept of QOL includes well-being, social obligations, relationships of a person, and his association with environment. The study examines the factors on which QOL of police officers depend and their relationships with each other. The issues linked with QOL are important because they directly affect the individual's ability to maintain a healthy lifestyle and affect organizational performance in the long run. This study explores relevant factors that have an impact on the QOL of the employees of police department in India.

Method

In this paper, literatures review, ISM, MICMAC, and DEMATEL methodology have established eleven factors that impact the QOL of police officers in India. Mutual relations between factors have been established using the ISM approach to develop a model to represent these relationships. DEMATEL methodologies were used to analyze these factors.

Results

Results indicate that "fair compensation, work overload, workplace safety, and job stress" are the top-level factors that affect QOL of police officers.

Conclusion

The identification of factors and their mutual relationships that affect QOL are important for police officers and have to be dealt with according to their order of importance. The research model developed in this study shows how the factors of police officers' QOL are interrelated and presents the interrelationships among these factors. A comprehensive model depicting the relationships among these factors has been established, so that the QOL of police officers can be improved.

S, M. N. Modelling the drivers of responsiveness of automotive supply chain using an integrated fuzzy DEMATEL-ISM approach

Purpose

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Design/methodology/approach

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

Findings

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

Research limitations/implications

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

Originality/value

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

Examining the barriers to operationalization of humanitarian supply chains: lessons learned from COVID-19 crisis

Humanitarian supply chains (HSC) have vital significance in mitigating different disruptive supply chain risks caused due to natural or man-made activities such as tsunami, earthquakes, flooding, warfare, or the recent COVID-19 pandemic. Each kind of disaster poses a unique set of challenges to the operationalization of HSC. This study attempts to determine the critical barriers to the operationalization of HSC in India during the COVID-19 pandemic. Initially, we determined and validated 10 critical barriers to HSC operationalization through a Delphi method. Further, we analyzed the barriers by computing the driving and dependence power of each barrier to determine the most critical ones. To do so, we coined a distinct form of interpretive structural modeling (ISM) by amalgamating it with the neutrosophic approach, i.e. Neutrosophic ISM. The findings indicate, "lack of Government subsidies and support, lack of skilled and experienced rescuers, and lack of technology usage" are the most critical barriers that influence the streamline operations of HSC during the COVID-19 outbreak, unlike other disruptions. This is the first-of-its-kind research work that has identified and analyzed the critical barriers to HSC operationalization during COVID-19 in the Indian context. The results and recommendations of the study can aid policymakers and HSC professionals in formulating suitable strategies for successful HSC operations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10479-022-04752-x.

Parallel Bookkeeping Path of Accounting in Government Accounting System Based on Deep Neural Network

“Parallel bookkeeping” is a key technical arrangement to achieve the goal of moderately separating and connecting the financial accounting system and budget accounting system established by the government accounting system. It is still a new thing for the majority of financial personnel in the government accounting subject. A deep neural network is the basis of deep learning. Up to now, the neural network has been applied in many fields, and its application in the financial field is more in-depth. The neural network is of great help to financial accounting. Integrating it into parallel bookkeeping in accounting can improve the work efficiency and accuracy of financial personnel. Through experimental analysis, it is found that its efficiency and accuracy are improved by 45% and 21.34% compared with the previous parallel bookkeeping path. The accounting parallel bookkeeping path based on the deep neural network studied in this paper not only has great practical significance for the work of financial personnel but also has far-reaching significance for the research of accounting paths in the future.

Sustainable supply chain visibility assessment and proposals for improvements using fuzzy logic

Purpose

Visibility management is essential to sustainable supply chains (SSCs), allowing the ability to see the chain end-to-end, with opportunities to derive benefits, including competitive advantage. Central to visibility management is visibility assessment and identification of areas for improvement. This paper aims to propose a method of assessing visibility in SSCs and the generation of proposals for improvement.

Design/methodology/approach

A hierarchically structured assessment template is developed that comprises of dimensions, factors and attributes of visibility in SSCs. The template permits the use of linguistic variables. A fuzzy logic approach is adopted to calculate visibility levels and generate improvement areas based on linguistic data captured through the template. An industry-based case study is used to illustrate the process.

Findings

This study reveals that visibility can be measured straightforwardly using the method developed in this paper. It is found that automation and contextual factors can significantly impact visibility levels, so also is sustainability awareness and practices adopted.

Originality/value

This paper describes a visibility assessment model that incorporates linguistic variables, fuzzy logic and the use of an adaptable visibility assessment template. The assessment model can identify potential inhibitors of visibility for SSC under study.

Big data analytics in supply chain management: a systematic literature review

Purpose

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

Design/methodology/approach

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

Findings

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

Research limitations/implications

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

Originality/value

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

Multi-criteria decision analysis of significant enablers for a competitive supply chain

Purpose

The purpose of this research paper is to study the significant enablers for a competitive supply chain and analyze the relationships among them by using multi-criteria decision-making (MCDM) techniques. The supply chain (SC) managers will get better insights from the models of this study to design their SCs that are more competitive for competitive advantage.

Design/methodology/approach

After an extensive review of literature followed by experts' opinions, 21 significant enablers for a competitive SC (CSC) were selected for structural modeling using MCDM techniques of total interpretive structural modeling (TISM), Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC), followed by decision-making trial and evaluation laboratory (DEMATEL) approach.

Findings

Top management commitment is the most prominent causing enabler of a CSC; customer satisfaction is the topmost effect enabler; the operational performance of individual firms in the supply chain is a crucial enabler of a CSC.

Practical implications

The results and findings of this study would provide better insights to SC professionals and practitioners to comprehend the significant enablers of a CSC for designing and executing SC operations more competitively to achieve better customer satisfaction and sustainable business performance.

Originality/value

To the best of the authors’ knowledge, this is a foremost study focusing on the significant enablers of a CSC by utilizing the TISM along with MICMAC and DEMATEL methods. It is expected that this research will offer useful guidance for assessing and considering the SC enablers for achieving a CSC and facilitate new research in this area with more thrust.

Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic

Purpose

This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.

Design/methodology/approach

The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.

Findings

The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.

Research limitations/implications

The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.

Originality/value

The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.

A structural equation model for big data adoption in the healthcare supply chain

Purpose

The purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).

Design/methodology/approach

First, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.

Findings

The segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”

Research limitations/implications

In literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.

Originality/value

This paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries

Purpose

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

Design/methodology/approach

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Findings

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

Research limitations/implications

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

Originality/value

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Analysis of barriers intensity for investment in big data analytics for sustainable manufacturing operations in post-COVID-19 pandemic era

Purpose

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

Design/methodology/approach

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

Findings

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

Practical implications

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

Originality/value

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

Modelling the Enablers for Branded Content as a Strategic Marketing Tool in the COVID-19 Era

This study aims towards identifying and modelling the significant factors which act as enablers for the branded content to be used strategically by marketers as a marketing tool in the COVID-19 era. A qualitative approach was adopted for this study, and significant factors associated with branded content were identified from the literature review and primary survey. The factors were then verified by the experts in the area of branding and digital marketing. Total interpretive structural modelling (TISM) and Decision-making Trial and Evaluation Laboratory (DEMATEL) techniques were used to model the factors as per their contextual relationships. As per the model outcomes from TISM and DEMATEL approaches, branded content is an efficient marketing tool that promises value delivery to stakeholders. This, in turn, depends on the authenticity and transparency in content development and distribution. The most significant driving enablers for the system suggest efficient measurement and evaluation strategies and the customer as co-creator for the branded content.

On relating big data analytics to supply chain planning: towards a research agenda

Purpose

This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.

Design/methodology/approach

Based on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.

Findings

This study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.

Research limitations/implications

This paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.

Originality/value

This study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

Purpose

The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.

Design/methodology/approach

The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.

Findings

The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.

Practical implications

The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.

Originality/value

This research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

Big data analytics based enablers of supply chain capabilities and firm competitiveness: a fuzzy-TISM approach

Purpose

This paper aims to identify the big data analytics (BDAs) based enablers of supply chain capabilities (SCCs) and competitiveness of firms. This paper also models the interaction among identified enablers and thus projects the relationship strength of these enablers with SCC and a firm's competitiveness.

Design/methodology/approach

In order to achieve the research objectives of this paper, we employed fuzzy total interpretive structural modeling (TISM), an integrated approach of an interpretive structural model and TISM.

Findings

Results suggest that BDA-based enablers namely, IT infrastructure for BDA; leadership commitment; people skills for use of BDA and financial support for BDA significantly enable SCC and enhance firm competitiveness.

Practical implications

Results of the present study have implications for researchers and practitioners; the results will enable them to design policies around identified enablers of BDA initiatives.

Originality/value

The present paper is one of a few early efforts that address the role of BDA in augmenting SCC and subsequently a firm's competitiveness from a resource-dynamic capability perspective.

Factors influencing organizational agility in higher education

Purpose

The purpose of this paper is to explore the factors that can facilitate agility in higher education and to analyze the interrelationship between the factors.

Design/methodology/approach

A structured model of factors facilitating agility in higher education was developed using total interpretive structural modeling (TISM). Cross-impact matrix multiplication (MICMAC) analysis helped in classifying the factors on the basis of their driving and dependency power.

Findings

An extensive literature review and expert opinion helped in identifying eight enablers that can promote agility in higher education. The ability to sense the environment, organizational structure, adoption of ICT, organizational learning, human resource strategies, leadership, readiness to change and collaboration with the stakeholders were the eight factors identified. The structural model revealed leadership as the most crucial enabler followed by human resource strategies and organizational structure.

Research limitations/implications

The model has incorporated and prioritized all the crucial drivers of agility that can help universities and colleges design, adopt and implement policies and practices that would facilitate agility.

Originality/value

So far, the research on agility in higher education has looked into each factor in isolation. This research provides a comprehensive list of the factors and establishes the interplay between the factors making this study new and original.

Supply chain management in the era of circular economy: the moderating effect of big data

Purpose

This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.

Design/methodology/approach

This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.

Findings

The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.

Originality/value

This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.

Mediating effect of big data analytics on project performance of small and medium enterprises

Purpose

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.

Design/methodology/approach

A sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.

Findings

The result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.

Practical implications

This study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.

Originality/value

For the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

Identification and analysis of enablers of SCM by using MCDM approach

Purpose

The purpose of this paper is to differentiate the empowering influences of critical enablers of supply chain management (SCM) along with their interrelationships. These empowering enablers are significant, as they encourage productive execution to improve organizational performance and stakeholder's satisfaction.

Design/methodology/approach

From the literature review, incidence of a number of SCM enablers were found and they were subjected to critical scrutiny by a considerable number of experts engaged in SCM research and application to identify significant and applicable empowering enablers by grounded interactions. By using Impact Matrix Cross-Reference Multiplication Applied to a Classification analysis, the driving and dependence power were analyzed and the empowering enablers were ordered. This was pursued by building up a structural model of the empowering enablers using interpretive structure modeling, followed with measuring cause–effect relationship using decision-making trial and evaluation laboratory (DEMATEL).

Findings

Among these identified enablers of SCM, operational performance, green SCM, employee empowerment and motivation and strategic association came out to be strategic enablers.

Research limitations/implications

The findings may help the practicing professionals to develop clarity in understanding of these essential enablers and their contextual as well as cause–effect relationship in SCM. The practicing professionals need to focus on all these enablers during implementation of SCM for enhancing the organizational performance and stake holders' satisfaction.

Originality/value

This study is of practical utility in real-life implementation of SCM. The algorithm used in applying the multi-criteria decision-making approach is very user-friendly, and the application of DEMATEL is an innovation compared to previous research. Further, the findings can be used as a benchmark for improving the performance of SCM by considering the cause–effect relationship.

An integrated fuzzy-ANP and fuzzy-ISM approach using blockchain for sustainable supply chain

Purpose

The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic network process (fuzzy-ANP) application. Over the past two decades, the overall product cost is affected by the SC at a global level. Organizations are working on their existing SC for improving their performance. BC technology is a newly emerging technology and magnetizes the attention of researchers and industrialists. This technology is still at the initial stage, and only little investigation is available in the literature and it has not been much investigated by researchers.

Design/methodology/approach

Literature and expert opinion interpretation in BC characteristics are further analyzed and modeled using fuzzy–interpretive structural modeling (fuzzy-ISM), fuzzy-MICMAC and fuzzy-ANP. The combined approach of both fuzzy-ISM and fuzzy-MICMAC is applied to identify the common drivers to integrate the BC technology in the light of efficient supply chain management (SCM).

Findings

Comparative analysis between traditional and BC-based supply chain (BCSC) using fuzzy-ANP is carried out, considering the common driving characteristics. The proposed integrated (combined) approach of fuzzy-ISM, fuzzy-MICMAC and Fuzzy-ANP found that integration of BC with SCM is better prioritized than traditional supply chain management (TSCM). The findings in the article endorse that the TSCM can be made efficient by integrating the BC technology considering five most driving characteristics, namely, data safety and decentralization, accessibility, documentation, data management and quality.

Originality/value

The current proposed research work identifies 12 characteristics after studying numerous literature reviews and having a discussion with SC experts with knowledge of BC. The integrated approach of fuzzy-ISM and fuzzy-MICMAC is implemented here. After that, fuzzy-ANP is used to give ranking among BCSCM and TSCM. The study carried out in this article motivates industries to implement BC in their SC system. It will reduce the transaction cost, documentation work, save time and eliminate human error at the national and international levels. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of BC to ensure that the system becomes more transparent, easily traceable and finally improve the performance.

Building supply chain risk resilience

Purpose

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

Design/methodology/approach

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

Findings

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

Originality/value

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

Modelling internet of things driven sustainable food security system

Purpose

The purpose of this paper is to model the sustainable food security system using various technologies driving internet of things (IoT). The right to food is a fundamental right of humans. With increasing population and urbanization, less land is being used for agricultural purposes. In addition, the climate change due to global warming often leads to frequent disasters such as droughts and floods, adversely affecting the food production. This leads to increased levels of poverty and hunger. Ensuring food security has become the prime agenda for all the policymakers and government bodies across the world. With changing global dynamics, traditional ways of ensuring food security will not be sufficient alone.

Design/methodology/approach

There is a need to develop a sustainable food security system that not only focusses on food production but also equally emphasizes on the efficacy of food distribution and reducing food wastage. In this digital age, the emerging disruptive technologies like Block chain, robotics, big data analytics, and cloud computations, etc., are increasingly changing the functioning of various sectors, giving rise to IoT-based working environment. The policymakers are also exploring these technologies to maximize their outreach so as to benefit the larger set of population and to gain visualization and control over policy implementation using IoT. This paper attempts to model the sustainable food security system using various technologies driving IoT. It also studies the interrelationship among various technologies and their application in various levels of policy implementation. The methodology used in the paper is fuzzy-TISM, which not only provides the causal relationship among two technologies but also provides the magnitude of the cause–effect relationship and the hierarchical framework for the complex problem.

Findings

The paper is addressed to the design of sustainable food security system in the Indian context wherein government ensures food security for all, using public distribution system (PDS).

Social implications

The paper is addressed to the design of sustainable food security system in Indian context wherein government ensures food security for all, using PDS.

Originality/value

This study is a novel attempt to integrate the IoT into the design of the PDS to ensure food security. The enabling factors for IOT are modelled using Fuzzy-TISM.