Operational excellence for improving sustainable supply chain performance

Mapping sustainable supply chain innovation: A comprehensive bibliometric analysis

This comprehensive bibliometric study investigates Sustainable Supply Chain Innovation (SSCI) research, examining its evolution, identifying key contributors, and unveiling emerging trends. Analyzing 1158 English-language SSCI articles using the robust Scopus dataset exposes noteworthy journals, authors, institutions, and global contributions. The findings suggest a consistent increase in research output since 1999, with a notable surge in the past decade. Network analysis and density-based spatial clustering identified six SSCI research clusters: Sustainability and Responsibility in Business, Navigating Innovation and Disruption, Sustainable Business Strategies, Environmental Sustainability and Innovation, Sustainable Food Systems and Environmental Impact, and Sustainable Business Dynamics. These clusters highlight the diverse nature of the evolving Sustainability and Supply Chain Management (SCM) field, contributing to a thorough understanding of the SSCI research landscape and emphasizing interconnections between sustainability and SCM themes, potentially leading to more comprehensive theoretical models. Furthermore, this understanding aids businesses in anticipating emerging trends and implementing optimal practices in SSCI. Moreover, recognizing active institutions and global contributors provides practical insights for fostering strategic collaborations.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

Influence of Artificial intelligent in Industrial Economic sustainability development problems and Countermeasures

Economic Sustainability Development (ESD) helps improve the sustainable values needed to conserve resources via optimum use, recovery, and recycling. There should be a direct relationship between countermeasures and the cause of economic losses due to improper design of ESD. Therefore, combining big data and cutting-edge technology may facilitate real-time monitoring, encourage consumers to engage in more sustainable practices and foster the development of industry sustainability. However, countermeasures have unforeseen consequences and tradeoffs that are difficult to predict in ESD. In this research, ESD uses big data to enhance their operations and customer service, develop targeted marketing strategies, and boost sales and profitability. In ESD, Data analytics is being used by human resources to improve decision-making throughout the recruiting process and in evaluating employee performance. In the long run, Artificial Intelligence (AI) adoption may boost productivity and produce new goods, creating jobs and boosting the economy. AI may have a net beneficial impact on ESD. Therefore, ESD-AI helps to overcome the problems by minimizing costs and boosting the economy. AI-integrated ESD helps analyze vast amounts of data, which may increase the speed at which things are done and substantially enhance decision-making. Hence, a balanced approach is essential to guarantee that AI systems can tackle sustainability challenges without adversely compromising other aims to boost the economy.

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers' mental workload in assembly tasks

Introduction: As a result of Industry 5.0's technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers' trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view. Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers' perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants' perceived workload. Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels. Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience

This article provides a theoretical framework for comprehending the connections between dynamic data analytics capability (DDAC), innovation capabilities (IC), supply chain resilience (RES), and sustainable supply chain performance (SSCP). Since this is the first empirical investigation of the sequential mediation effect between DDAC and SSCP through IC and RES, it fills a critical need in the supply chain literature. A quantitative methodology was used, involving a survey questionnaire distributed to 259 large Pakistani manufacturing firms. We used PLS-SEM to test for the expected associations. Findings show that using DDAC has a beneficial effect on both innovative and resilient capabilities, which in turn leads to better SSCP. The research illuminates the sequential mediating roles of product, process, and resilience, underlining the need of combining data-driven innovation with resilience in order to achieve sustainable supply chain performance. These results provide useful guidance for businesses that want to boost their sustainability results by taking a more all-encompassing approach to data-driven innovation and resilience.

Big data analytics and innovation in e-commerce: current insights and future directions

Big data analytics (BDA), as a new innovation tool, played an important role in helping businesses to survive and thrive during great crises and mega disruptions like COVID-19 by transitioning to and scaling e-commerce. Accordingly, the main purpose of the current research was to have a meaningful comprehensive overview of BDA and innovation in e-commerce research published in journals indexed by the Scopus database. In order to describe, explore, and analyze the evolution of publication (co-citation, co-authorship, bibliographical coupling, etc.), the bibliometric method has been utilized to analyze 541 documents from the international Scopus database by using different programs such as VOSviewer and Rstudio. The results of this paper show that many researchers in the e-commerce area focused on and applied data analytical solutions to fight the COVID-19 disease and establish preventive actions against it in various innovative manners. In addition, BDA and innovation in e-commerce is an interdisciplinary research field that could be explored from different perspectives and approaches, such as technology, business, commerce, finance, sociology, and economics. Moreover, the research findings are considered an invitation to those data analysts and innovators to contribute more to the body of the literature through high-impact industry-oriented research which can improve the adoption process of big data analytics and innovation in organizations. Finally, this study proposes future research agenda and guidelines suggested to be explored further.

Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

Purpose

The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

Design/methodology/approach

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

Findings

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

Research limitations/implications

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

Practical implications

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

Originality/value

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

Big data analytics capabilities for reinforcing green production and sustainable firm performance: the moderating role of corporate reputation and supply chain innovativeness

The literature review lacks empirical studies on the role of big data analytics (BDA) and green technological innovation capabilities (GTICs) in promoting the sustainable performance of the manufacturing industry. The primary objective of this study is to examine the role of BDA-technology capability, GTIC, and environmental orientation toward green production and sustainable firm performance. Moreover, this research paper investigates the mediating role of green production and green competitive advantage and the moderating role of corporate reputation and supply chain innovativeness. Primary data was collected from Pakistani manufacturing firms through the survey method. Structural equation modeling was applied to measure and verify the relationship of proposed hypotheses. Empirical findings show BDA technology capability, GTIC, and environmental orientation positively contribute to green production. Moreover, green production helps achieve a green competitive advantage, and green competitive advantage positively influences sustainable firm performance. Furthermore, mediating role of green production and green competitive advantage and moderating role of corporate reputation and supply chain innovativeness was also confirmed. This study contributes by developing a comprehensive model showing the relationship between organizational capabilities, BDA technology capability, GTIC, and sustainable firm performance by considering potential mediators and moderators. Thus, this research suggests enhancing green production and sustainable firm performance through adopting BDA technology capability and GTIC by Pakistani manufacturing firms.

Big data analytics capability in healthcare operations and supply chain management: the role of green process innovation

Green approaches remain little disseminated in the healthcare sector despite growing interest in recent years from practitioners and researchers. Big Data Analytics Capability (BDAC) can play a critical role in the integration of environmental concerns into operations and supply chain management (OSCM) and further strengthen the environmental performance of healthcare facilities. According to the literature, the integration of the environment into operations process remains insufficient to achieve high levels of performance and requires efforts in green process innovation. However, this relationship between BDAC and green process innovation remains poorly justified empirically. To address this theoretical gap, we investigated the relationship between BDAC, environmental process integration, green process innovation in OSCM and environmental performance. The main contribution of this study is the valuable knowledge on how BDAC influences environmental process integration and green process innovation to enhance environmental performance. Moreover, the study highlights the mediating role of green process innovation on environmental performance, a finding that has not been mentioned in the extant literature. The paper provides valuable insight for managers and stakeholders that can assist them in supporting the application of BDAC in healthcare OSCM to create sustainable value.

Analysis of social barriers to sustainable innovation and digitisation in supply chain

Organisations are consistently becoming more and more conscious about sustainability issues that are being raised on various platforms by regulatory bodies and other social activists. Digitisation of supply chains and other technologies like recycling has emerged as one solution that helps achieve sustainability goals by bringing more transparency into the system regarding emissions. Adopting these sustainability and digitisation-related technologies in the supply chain is a major issue, and there are many social issues related to their implementation and adoption. This study aims to identify social barriers to sustainable innovations and digitisation in the supply chain. A total of eight barriers are identified and analysed using BWM and DEMATEL methodologies. The results indicate that work-related circumstances and employment disruptions are the most prominent social barriers, which also influence other barriers. Organisations need to hire and train manpower in skills related to sustainable and digitisation technologies to secure their jobs and facilitate the adoption of these technologies in the supply chain.

Digital transformation, industrial structure change, and economic growth motivation: An empirical analysis based on manufacturing industry in Yangtze River Delta

China is in a critical stage of economic growth mode transformation. The digital transformation of the manufacturing industry may create new impetus and new models for economic growth. Taking the manufacturing industry of 25 prefecture-level cities in the Yangtze River Delta region as the research object, we explore the digital transformation process of the manufacturing industry and verifies its theoretical mechanism of promoting economic growth through the industrial structure. A panel model based on the improved Feder two-sector model and a multiple mediating effect model are established to explore the dynamic mechanism of manufacturing digital transformation to promote economic growth through industrial restructuring. The results show that the digital transformation of the manufacturing industry in the Yangtze River Delta region of China is relatively high, and the speed of digital transformation has been accelerating in recent years. The digital transformation of the manufacturing industry can promote the change in industrial structure and form a new driving force for economic growth. The key is to improve the level of industrial structure and extend the length of the industrial chain. Based on these, we propose measures to promote the transformation and upgrading of industrial structure for the sustainable development of China's economy.

The regulatory role of sustainable product design media and environmental performance in the impact of the Covid-19 epidemic on corporate sustainability: an application in Turkey

This research aims to investigate the effect of the COVID-19 epidemic, which entered the world agenda in 2019 and affected the whole world, on the corporate sustainability of businesses. The mediating effect of sustainable supply on this effect and the regulatory effect of environmental performance were investigated. The research was conducted among 235 businesses operating in Turkey. The data obtained using the survey method were analyzed in SPSS and AMOS analysis programs. As a result of the analyses obtained, it was determined that the COVID-19 epidemic significantly affected the corporate sustainability of the enterprises and that the environmental performance of the enterprises was a regulatory effect, together with the mediation of sustainable supply. It is understood day by day that COVID-19 negatively affects the economies of the countries. However, despite these negative effects; It is expected that the results of this research will contribute to the literature with a significant effect on the institutional sustainability of the COVID-19 epidemic.

Evaluating the circular economy-based big data analytics capabilities of circular agri-food supply chains: the context of Turkey

Agri-food supply chains (AFSCs) are one of the significant building blocks of agricultural production, and their sustainability aims are advanced by big data analytics (BDA) and the circular economy (CE). As access to safe, healthy, and high-quality food has become increasingly difficult, AFSCs need to leverage their capabilities for CE-based BDA to overcome sustainability challenges. However, a significant gap exists in the relevant literature on how to identify such capabilities to achieve sustainability goals. To build CE-based BDA capabilities, organisations need to orchestrate their resources and competencies and align them well with specific sustainability targets. In consideration of these issues, this study was conducted to identify the aforementioned capabilities and their effects on the performance of circular AFSCs from the perspective of a developing country. To this end, a three-stage multi-criteria decision-making model was developed and used in the examination of circular AFSCs in Turkey. The findings revealed that supply chain management (SCM) was the most important capability, followed by organizational, technical, environmental, economic, and social capabilities. Furthermore, big data infrastructure was the most important sub-capability ahead of financial benefits, top management support, sustainability and resilience, and food waste reduction. Finally, productivity improvement was determined as the most significant impact of CE-based BDA capabilities on circular AFSCs. This study can serve as a reference for managers and policy-makers on what BDA capabilities should be developed for circular AFSCs. It also contributes to addressing the agricultural production issues encountered by developing countries.

Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management

Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.

How improvisation drives lean search: The moderating role of entrepreneurial team heterogeneity and environmental uncertainty

Although lean search is seen as an important action in lean startup, previous studies have less knowledge on how to realize it, especially in the face of traditional plans that cannot cope with sudden changes in the environment. To fill the research gap, this study investigates the effects of improvisation (exploitative, explorative, and ambidextrous improvisation) on lean search. Meanwhile, this research also discusses the moderating effects of entrepreneurial team heterogeneity and the environmental uncertainty to identify the boundary conditions of this relationship. Supported by the cross-sectional data from 203 Chinese startups, the results show that explorative and ambidextrous improvisation are positively associated with lean search. However, the effect of exploitative improvisation on lean search is unsupported. Additionally, technology uncertainty positively moderates the relationship between exploitative improvisation and lean search. Market uncertainty positively moderates the relationship between explorative improvisation and lean search. However, the entrepreneurial team heterogeneity negatively moderates the relationship between ambidextrous improvisation and lean search. These findings contribute to understanding how startups could conduct lean search in a rapidly changing environment, which provides theoretical guidance for improving the success rate of startups.

Industry 4.0 as a Challenge for the Skills and Competencies of the Labor Force: A Bibliometric Review and a Survey

The latest technological development called Industry 4.0, like the previous industrial revolutions, has also brought a new challenge for people as a labor force because new technologies require new skills and competencies. By 2030 the existing generation in the labor market will have a skill gap threatening human replacement by machines. Based on bibliometric analysis and systematic literature review the main aims of this study are, on the one hand, to reveal the most related articles concerning skills, competencies, and Industry 4.0, and on the other hand, to identify the newset of skills and competencies which are essential for the future labor force. Determining the model of new skills and competencies in connection with Industry 4.0 technologies is the main novelty of the study. A survey carried out among the workers of mostly multinational organisations in Hungary has also been used to explore the level of awareness about those skills and Industry 4.0 related technologies, and this can be considered the significance of the empirical research.

Big Data Capability and Sustainable Competitive Advantage: The Mediating Role of Ambidextrous Innovation Strategy

After the rapid expansion of data variety, velocity and volume, human civilization experienced rapid changes in the period between the “IT” and “Data” ages. Researchers view big data and data capability as new sustainable competitive advantages that enhance the sustainability of organizational development. This paper aims to develop and empirically test a framework that will investigate how big data capability is achieved through exploitative, explorative and ambidextrous modes of innovation strategies, and will also explore how they can, in turn, build firms’ sustainable competitive advantage. Using data from surveys of 229 respondents working in Chinese manufacturing firms, we test the framework using regression and bootstrapped mediation analyses. It also shows how big data capability will make firms more inclined to implement exploitative innovation strategy and construct sustainable competitive advantage, as opposed to explorative innovation strategy; when viewed from an ambidextrous perspective, combined dimension of ambidextrous innovation strategy is found to partially mediate between big data capability and sustainable competitive advantage while balanced dimension of ambidextrous innovation strategy does not. The conclusions are of great significance because they will help firms to deal with challenges that arise in big data applications and digital transformation. The findings offer new insight into the strategic choices of organizational innovations.

Strategies to Respond to Technology Enhancement

This study identifies coping strategies to examine the behaviors adopted by team managers in addressing technostress. It evaluates the choice of coping strategies to increase performance. A study involving 3 companies and 45 respondents was conducted to identify coping strategies. However, as we chose to make a deeper interview after the first one, we continued our interviews with those who are available for 8 hours of interview, (2 hours each time); therefore we continued with 13 people. Overall, four interactional coping strategies were identified: Based on these, four new coping theories address technostress from an international perspective. This enriches the literature on coping strategies and technostress and the results explain a wide range of team managers' behaviors. Hence, it is necessary to adopt suitable policies to address effect of technostress.

Family Businesses and Their Transition to Industry 4.0

Industry 4.0 (I4.0) is the new paradigm shift impacting businesses today. This study explores the state of Bangladeshi family businesses in adapting to I4.0 using a qualitative research methodology. Top management personnel were interviewed to capture their insights on transitioning to I4.0. The thematic analysis revealed four themes, current state of the businesses, challenges faced, pandemic impact on human resources, and future plans. Findings indicate high awareness and greater adoption of digital practices with COVID-19 acting as a catalyst. Although training is emphasized, there is a lack of focus on both career and general counselling, which may prove to be detrimental in the future. The study takes a resource-based view to find the bundle of resources acting as conditions for the family firms to evolve into the I4.0, thereby making a practical contribution to understanding the role of family businesses in implementing I4.0 policies to enrich their human resource competencies and leverage the benefits of I4.0.

Assessing green innovation in supply chains: a systematic review based on causal mechanisms framework

Purpose

There have been several reviews of green, ecological and sustainable innovations, but a thorough assessment of green innovation (GI)'s mechanisms in a supply chain setting has not been attempted yet. The purpose of this paper is to review how GI was investigated in supply chains through the lens of a multilevel framework of innovation mechanisms.

Design/methodology/approach

The authors provide a comprehensive assessment of prior studies using a systematic literature review approach and content analysis of 136 papers identified from the Web of Science Core Collection database.

Findings

Current literature on green innovation supply chains (GISC) has been categorized according to three main causal mechanisms: situational, action-formation and transformational mechanisms. Three different levels of analysis were considered for the three mechanisms: macro, meso and micro. In addition, the authors have also assessed the value creation and appropriation outcomes of GI. The authors identified relevant research gaps in the extant literature and a set of propositions that may guide future research in this area.

Research limitations/implications

This review provides a novel perspective on GISC based on a multilevel theoretical framework of mechanisms.

Practical implications

The causal mechanisms assessment of GISC can be adopted by organizations to convince their SC partners to engage in collaborative and more ambitious initiatives in the field.

Social implications

The findings of this review could serve as an argument for more encompassing and ambitious GISC initiatives which can be of benefit to society.

Originality/value

A thorough assessment of the interacting mechanisms in GISC has not been attempted before. The authors identify gaps in current literature and provide several propositions for further research avenues based on causal mechanisms framework.

Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector

The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.

Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis

The Industry 4.0 (I4.0) concept paves the way for the circular economy (CE) as advanced digital technologies enable sustainability initiatives. Hence, I4.0-driven CE-oriented supply chains (SCs) have improved sustainable performance, flexibility and interoperability. In order to smoothly embrace circular practices in digitally enabled SCs, quantitative techniques have been identified as crucial. Therefore, the intersection of I4.0, CE, supply chain management (SCM) and quantitative techniques is an emerging research arena worthy of investigation. This article presents a bibliometric analysis to identify the established and evolving research clusters in the topological analysis by identifying collaboration patterns, interrelations and the studies that significantly dominate the intersection of the analysed fields. Further, this study investigates the current research trends and presents potential directions for future research. The bibliometric analysis highlights that additive manufacturing (AM), big data analytics (BDA) and the Internet of Things (IoT) are the most researched technologies within the intersection of CE and sustainable SCM. Evaluation of intellectual, conceptual and social structures revealed that I4.0-driven sustainable operations and manufacturing are emerging research fields. This study provides research directions to guide scholars in the further investigation of these four identified fields while exploring the potential quantitative methods and techniques that can be applied in I4.0-enabled SCs in the CE context.

Digital technologies as enablers of supply chain sustainability in an emerging economy

Vietnam is a country with significant potential for growth as a global centre for manufacturing, as supply chains look to reduce their over-reliance on China in the aftermath of COVID-19. The objective of this study is to better understand the current adoption rates and growth potential of emerging Industry 4.0 (I4.0) digital technologies and ascertain their potential to drive successful future sustainability initiatives amongst Vietnamese supply chain firms. These technologies offer a wide range of sustainability benefits, from a potential to reduce waste production and lower energy consumption to increased opportunities for recycling and industrial symbiosis. This empirical study surveys 223 Vietnamese supply chain experts to learn how digital technologies are being utilized in that region, what levels of future investment are expected, what preparatory measures are being taken to leverage new technologies, and what scope for improved supply chain sustainability exists. The findings indicate a low level of I4.0 digital technology adoption amongst Vietnamese supply chain firms, with the Internet of Things (IoT) currently being the most prevalent (48 percent adoption rate). Drones, Big Data Analytics and IoT are the I4.0 digital technologies expected to have the greatest future impact on Vietnamese supply chains. Whilst I4.0 digital technology adoption is still at this early stage, that may present a greater opportunity for driving future sustainability outcomes, than interrupting and retrofitting solutions to already-established networks and infrastructure.

In-transit interventions using real-time data in Australian berry supply chains

Purpose

The objective of this paper is to contribute to Australian berry supply chains with a relevant identification regarding the possible data driven interventions that stakeholders can take while the berries are in transit.

Design/methodology/approach

An exploratory series of semi-structured interviews was conducted through six Australian experts in the industry with more than 20 years of experience in Australian berry supply chains and the Australian perishable food industry, to identify key possible in-transit interventions that could be implemented in the Australian berry industry.

Findings

The analysis of the interviews revealed a total of 18 possible in-transit interventions. An important finding is that in-transit interventions are made possible by the use of real-time data gathered through IoT devices such as Active Radio Frequency Identification, Time and Temperature Indicators interacting with Wireless Sensor Networks. Another key finding is that Australian berry growers and retailers do possess the technologies and the resources necessary to make in-transit interventions possible, however they have yet applied these technologies to operational decision-making and interventions based on the product, rather focussing on supply chain transactions and events.

Research limitations/implications

Since the research focusses on an Australian context, its findings may or may not be applicable to other countries. The research is exploratory in nature, and its findings should be verified by future research, in particular to test whether the in-transit interventions proposed here can be implemented in a cost-efficient way.

Originality/value

To the authors' knowledge, this publication is the first known academic article to provide a clear understanding of the Australian berry industry from a supply chain and logistics perspective, and the first to explore possible data driven in-transit interventions in perishable food supply chains.

Theoretical Perspectives on Sustainable Supply Chain Management and Digital Transformation: A Literature Review and a Conceptual Framework

In an era where environmental and social pressures on companies are increasing, sustainable supply chain management is essential for the efficient operation and survivability of the organizations (members of the chain). Digital transformation and the adoption of new technologies could support the development of sustainable strategies, as they support supply chain processes, decrease operational costs, enable control and monitoring of operations and support green practices. The purpose of this paper is to explore the relationship between sustainable supply chain management and digital transformation through the adoption of specific technologies (Blockchain technology, big data analytics, internet of things). It aims at theory building and the development of a conceptual framework, enabling the explanation of under which circumstances the above combination could lead to the development of sustainable performances. It also aims to examine how companies can increase their competitive advantage and/or increase their business performance, contributing both to academics and practitioners. After conducting a literature review analysis, a significant gap was detected. There are a few studies providing theoretical approaches to examining all three pillars of sustainability, while at the same time analyzing the impact of big data analytics, internet of things and blockchain technology on the development of sustainable supply chains. Aiming to address this gap, this paper primarily conducts a literature review, identifies definitions and theories used to explain the different pillars of flexibility, and examines the effect of different technologies. It then develops a theoretical conceptual framework, which could enable both academics and practitioners to examine the impact of the adoption of different technologies on sustainable supply chain management. The findings of this research reveal that digital transformation plays an important role to companies, as the combination of different technologies may lead to the development of significant capabilities, increasing sustainable performances and enabling the development of sustainable strategies, which can improve companies’ position in the market.

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.

Managing Labor Sustainability in Digitalized Supply Chains: A Systematic Literature Review

With increasing concerns of labor issue risks within supply chains, both academia and practitioners are paying increasingly great attention to how to design and implement effective management approaches to enhance labor sustainability in supply chains. Furthermore, digitalization facilitates and brings both opportunities and challenges to this area. Using the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conducted a systematic literature review based on 50 selected papers from the Web of Science database. Starting from the supply chain level, this study identifies digital technology (DT)-enabled labor sustainability management practices, barriers to the adoption of DT in labor management practices, and the performance outcomes of such practices. In addition, we put forward solutions to eliminate those identified barriers to facilitate DT adoption in firms’ labor sustainability management. Last, future directions and research opportunities for both supply chain management and labor sustainability are summarized.

Triggering sustainable firm performance, supply chain competitive advantage, and green innovation through lean, green, and agile supply chain practices

Due to extensive industrial activities, many environmental challenges have devastated human beings and environmental integrity. Therefore, sustainable supply chain (SC) practices (lean, green, and agile - LGA) gained momentum in the manufacturing industry. Considering the importance of LGA-SC, this study investigates the impact of LGA-SC practices on green innovation (GI), supply chain competitive advantage (SCPA), supply chain responsiveness (SCR), and sustainable firm performance (SFP). Data were collected from employees of the manufacturing industry in China. The proposed conceptual framework was verified by using structural equation modeling. The empirical results indicate that LGA-SC practices are statistically associated with GI, SCR, SCPA, and SFP. Moreover, this research finds that GI and SCR play mediating roles between LGA-SC practices and SCPA. GC positively moderates the relationship between LGA-SC practices and GI, and IP also acts as a strong moderator between GI and SCPA. To the authors' best knowledge, this study is the pioneer to provide insights about a novel framework combing LGA-SC practices, SCPA, and SFP with mediating role of GI and moderating role of GI and IP. This study supports managers of the Chinese manufacturing sector to further extend strong roots for LGA-SC adoption.

Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model

Due to the increasing popularity of cost-based outsourcing and growing stakeholder concern about environmental, social, and technological issues, supply chain sustainability is vital in both developed and emerging economies. Bangladesh is an emerging economy and wood industry of Bangladesh is suffering from severe sustainability issues besides its growth. Hence, this article aims to examine the critical success factors (CSFs) for sustainability in the Bangladeshi wood industry, which is crucial to help supply chain managers engage in achieving sustainable development goals. This research investigated the CSFs and uncovered their interdependencies through the development of a methodology integrating a literature review, principal component analysis (PCA), interpretive structural modelling (ISM), and Matriced Impacts Croises Multiplication Appliquee aunClassement (MICMAC) techniques. PCA (n = 150) was used to identify and rank the CSFs for sustainability in the Bangladeshi wood industry while ISM (n = 9) and MICMAC were used to determine the driving and dependence power of the CSFs. The findings reveal that research and development, supplier relations, and using eco-friendly technology are the most significant CSFs of the Bangladeshi wood industry. Indispensable links revealing the driving and dependence power among the CSFs were also reported. To the best of our knowledge, this study is the first of its kind that examined the CSFs for supply chain sustainability in the Bangladeshi wood industry. The proposed methodology and findings will help managers in the Bangladeshi wood industry as well as other similar industries to understand the CSFs and reduce the complexity of decision-making in managing business process towards sustainability journey.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012-2020) to explore future research opportunities

Nowadays, the market competition becomes increasingly fierce due to diversified customer needs, stringent environmental requirements, and global competitors. One of the most important factors for companies to not only survive but also thrive in today's competitive market is their logistics performance. This paper aims, through a systematic literature analysis of 115 papers from 2012 to 2020, at presenting quantitative insights and comprehensive overviews of the current and future research landscapes of sustainable logistics in the Industry 4.0 era. The results show that Industry 4.0 technologies provide opportunities for improving the economic efficiency, environmental performance, and social impact of logistics sectors. However, several challenges arise with this technological transformation, i.e., trade-offs among different sustainability indicators, unclear benefits, lifecycle environmental impact, inequity issues, and technology maturity. Thus, to better tackle the current research gaps, future suggestions are given to focus on the balance among different sustainability indicators through the entire lifecycle, human-centric technological transformation, system integration and digital twin, semi-autonomous transportation solutions, smart reverse logistics, and so forth.

Environmental Problems: An Analysis of Students' Perceptions Towards Selective Waste Collection

The reduction, reuse, collection and recovery of recyclable materials are sustainable behaviors and people's awareness of them plays an important role in implementing strategies and policies in this field. The quantitative analysis performed on a group of 816 students of Politehnica University of Timisoara, aimed at finding answers to important environmental concerns and observing the students' behaviors of reuse and selective collection of the waste resulted from plastic containers, paper, aluminum, batteries, iron packaging waste, electronic equipment, used cooking oil and printer toner. The research has shown that 'increased amounts of waste' (63.5%) is among the first three concerns Romania has to deal with, besides 'air pollution' (67.9%) and 'deforestation' (63.7%). Moreover, the study highlights the existence of the behavior toward the selective waste collection among students (plastic - 60.3%, paper - 57.8%, and glass - 55.3%). although there are some areas (e.g., selectively collecting used cooking oil or printer toner, their level of knowledge regarding the color code for the recycling bins, etc.) that students still need to be familiarized with through different campaigns, trainings, courses, etc. The results can be used in the development of institutional strategies or of strategic documents targeting environmental protection and sustainable development.

Challenges and Benefits of Sustainable Industry 4.0 for Operations and Supply Chain Management—A Framework Headed toward the 2030 Agenda

Currently, Industry 4.0 (I4.0) represents a worldwide movement to improve the productivity and efficiency of operations and supply chain management (OSCM), which requires rethinking and changing the mindset of the way in which products are manufactured and services are used. Although the concept of I4.0 was not popularised in the ratification of the 2030 Agenda, I4.0 is a watershed in the implementation of the Sustainable Development Goals (SDGs). It can serve as a platform for the alignment of the SDGs with the ongoing digital transformation. However, the challenges to the integration of I4.0 and sustainability in OSCM, and the benefits of this integration, in line with the SDGs, remain unclear. Moreover, there is a lack of a standard structure that establishes links between these challenges and benefits to strategically guide organisations on the journey towards a sustainable OSCM 4.0 (S-OSCM4.0) aligned with the SDGs. Thus, the purpose of this paper is to propose an S-OSCM4.0 framework for organisations to attain sustainability and I4.0 in OSCM, in line with the 2030 Agenda. Based on a systematic literature review, 48 articles that complied with the selection criteria were analysed using content analysis. The research findings were synthesised into taxonomies of challenges and benefits, and these categories were linked into a step-by-step framework, following an inductive approach. The proposed framework represents a novel artefact that integrates taxonomies in order to holistically achieve sustainable digitalisation for people, prosperity and planet benefits, and sheds light on the potential contributions of S-OSCM4.0 to the SDGs.

COVID-19 and Its Implications to the Assessment of Sustainable Palm Oil Supply Chain Management: An Indonesian Perspective

Motivated by the low sustainability index and pressure to meet the global demand for eco-friendly crude palm oil (CPO) in the pandemic-ridden environment, this research aims to investigate the implications of the COVID-19 pandemic to assess the drivers of sustainable supply chain management (SSCM) of the Indonesian CPO sector to tackle supply chain disruptions. To achieve this aim, the study seeks to determine the sustainability drivers to accommodate the pandemic-ridden environment and if sustainability indicators can help improve the supply chain management of the CPO sector. A methodology is divided into two interrelated parts: first, based on a careful review of extant literature of the CPO sector and sustainable supply chain in the light of pandemic. The proposed methodology is then tested using the response data of 108 oil mills' representatives collected through survey questionnaires and analyzed using statistical tools of reliability, distribution, Kaiser–Meyer–Olkin (KMO), Confirmatory Factor Analysis (CFA), and diagnostic tests of CFA. The findings designate the environmental costs, rapidity, and adaptability as core economic indicators; the social and workforce development, health, and safety workforce development and consumer issues as crucial social indicators; while energy and material efficiency, management of waste and emissions, and sustainable suppliers as the best environmental indicators. This study provides a holistic platform on the implications of the pandemic to assess the SSCM of the CPO sector. These findings are expected to aid the industrial managers in employee skills and health protocols, customer service, and environmental management. The study is also anticipated to guide the supply-chain partners and government policymakers to take initiatives on SSCM in the context of the pandemic.

Achieving Strategic Alignment of Big Data Projects in Saudi Firms

Big data projects can fail due to the lack of alignment between the big data project strategy and the overall business strategy. This research considers organizational culture as an enabler of a better alignment between the two. To test the research hypothesis, a questionnaire was collected from several dozen IT decision-makers in Saudi organizations who have implemented big data projects. Statistical analysis using PLS indicates that the alignment of big data projects and overall business strategy is highly influenced by the five dimensions of organizational culture identified by Smit et al. (2008), namely strategy, leadership, adaptability, coordination, and team relationships

Disruptions in sourcing and distribution practices of supply chains due to COVID-19 pandemic: a sustainability paradigm

Purpose

The present research paper is an attempt to study how COVID-19 can affect the global sourcing practices of various supply chain intermediaries across the demand chain. This study aims to explore and is an attempt to understand the overall impact of COVID-19 on the sustainable operations of the firm such as sourcing, procurement, economic performance, social responsibility, consumption and distributions.

Design/methodology/approach

This study uses a quantitative technique using data collected from 708 respondents. Structural equation modeling (SEM) has been applied to test the proposed model and hypothesis.

Findings

The findings of the study suggest that sourcing practices, distribution and sustainability considerations of manufacturers, suppliers, distributors and retailers are affected by COVID-19 to a great extent but the pandemic has also led to making supply chain intermediaries understand the changing dynamics of the business scenario which can help them in their own strategic and business evolution.

Research limitations/implications

The current disruptions throughout global delivery chains caused by COVID-19 affect badly, the already poor-performing supply chains. Hence, the present study provides fresh insight on how organizations can limit the ill effects of COVID-19 by safeguarding some of their key sustainable operations in a post-pandemic business scenario.

Originality/value

The present study takes into consideration how core supply functions such as sourcing, distribution and manufacturing and various sustainable operations are disrupted by pandemic and its after-effects. This knowledge base can help business organizations to mitigate such problems/disruptions in the future.

Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19

The COVID-19 pandemic has severe impacts on global health and social and economic safety. The present study discusses strategies for turning the COVID-19 crisis into opportunities to use artificial intelligence (AI) and big data in business operations. Based on the shared experience and theoretical ground, researchers identified five major business challenges during the COVID-19 pandemic: production and supply-chain disruption, appropriate business model selection, inventory management, budget planning, and workforce management. These five challenges were outlined with eight business cases as examples of companies that had already utilized AI and big data for their business operations during the COVID-19 pandemic. The outcomes of this study provide valuable insights into contemporary social science research and business management with AI and big data applications as a business response to any crisis in the future.

Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy

The COVID-19 pandemic has disrupted supply chain operations globally. Nevertheless, resilient firms have the capacity to combat an unprecedented situation with the right strategic approach. The current research has developed an integrated research model that combines factors such as supply chain intelligence, supply chain communication, leadership commitment, risk management orientation, supply chain capability and network complexity to investigate supply chain resilience. The research model of this study was empirically tested with 309 responses collected from supply chain managers. Results revealed that supply chain resilience is measured with supply chain intelligence, supply chain communication, leadership commitment, risk management orientation, supply chain capability and network complexity and demonstrated a substantial variance R2 of 0.548% towards supply chain resilience. Practically, this study suggests that supply chain managers should focus on factors such as big data analytics, risk management orientation,1 supply chain communication and leadership commitment to enhance supply chain resilience and sustainable supply chain 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.

Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

The fourth industrial era, known as ‘Industry 4.0’ (I4.0), aided and abetted by the digital revolution, has attracted increasing attention among scholars and practitioners in the last decade. The adoption of I4.0 principles in Disaster Risk Management (DRM) research and associated industry practices is particularly notable, although its origins, impacts and potential are not well understood. In response to this knowledge gap, this paper conducts a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in DRM. The systematic literature review identified 144 relevant articles and then employed descriptive and content analysis of a focused set of 70 articles published between 2011 and 2021. The results of this review trace the growing trend for adoption of I4.0 tools and techniques in disaster management, and in parallel their influence in resilient infrastructure and digital construction fields. The results are used to identify six dominant clusters of research activity: big data analytics, Internet of Things, prefabrication and modularization, robotics and cyber-physical systems. The research in each cluster is then mapped to the priorities of the Sendai framework for DRR, highlighting the ways it can support this international agenda. Finally, this paper identifies gaps within the literature and discusses possible future research directions for the combination of I4.0 and DRM.

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

The new concept of quality in the digital era: a human resource empowerment perspective

Purpose

This study aims to identify the drivers of human resource empowerment in understanding the new concept of Quality 4.0 in the digital era.

Design/methodology/approach

First, the literature of quality management evolution in the fourth industrial revolution (Industry 4.0) and the position of the required workforce in Quality 4.0 were reviewed and then by using the opinions of experts and managers of Knowledge-Intensive Business Services (KIBS) firms, a set of driver effects on the readiness and ability of human resources was identified in the context of Quality 4.0. After identifying the drivers, cause-and-effect relationships among these drivers were investigated using the Grey DEMATEL technique.

Findings

A total of 29 Quality 4.0 drivers of readiness and workforce ability were identified, based on multiple interactions of quality management in different stages of the production cycle. They were divided into new valuation approaches, composite dimensions, team creativity and thorough inspection. “Technical abilities and capability to solve problems” was identified as the most significant driver.

Practical implications

Findings help KIBS firms to take necessary measures and plans. Consequently, they can increase the readiness and ability of human resources based on the changes in managing Quality 4.0. Also, considering the importance of each driver, they will be able to take a step towards total quality improvement.

Originality/value

Despite extensive research on the subject of the fourth Industrial Revolution, research on the human aspects required for managing Quality 4.0 is limited. This study was performed to examine the cause-and-effect relationships between human resource drivers to adapt to the changes in Quality 4.0.

Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance

Purpose

This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration.

Design/methodology/approach

Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method).

Findings

The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration.

Originality/value

Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.

The Green Blockchains of Circular Economy

Eco-friendly systems are necessitated nowadays, as the global consumption is increasing. A data-driven aspect is prominent, involving the Internet of Things (IoT) as the main enabler of a Circular Economy (CE). Henceforth, IoT equipment records the system’s functionality, with machine learning (ML) optimizing green computing operations. Entities exchange and reuse CE assets. Transparency is vital as the beneficiaries must track the assets’ history. This article proposes a framework where blockchaining administrates the cooperative vision of CE-IoT. For the core operation, the blockchain ledger records the changes in the assets’ states via smart contracts that implement the CE business logic and are lightweight, complying with the IoT requirements. Moreover, a federated learning approach is proposed, where computationally intensive ML tasks are distributed via a second contract type. Thus, “green-miners” devote their resources not only for making money, but also for optimizing operations of real-systems, which results in actual resource savings.

Data-driven quality improvement approach to reducing waste in manufacturing

Purpose

Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.

Design/methodology/approach

Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.

Findings

Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.

Practical implications

Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.

Originality/value

This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.