Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Digital capability and green innovation: The perspective of green supply chain collaboration and top management's environmental awareness

The emergence of the digital economy has accelerated digital transformation, and digitalization has shown new potential and solutions to increasingly severe environmental challenges. Based on the resource-based view, dynamic capability view, synergy effect and upper echelons theory, the connotation and measurement dimensions of digital capability and green supply chain collaboration are defined and improved. Then, a theoretical model of "digital capability-green supply chain collaboration-green innovation performance" is constructed. The influence mechanism and transmission process of digital capability on green innovation performance from the perspective of green supply chain collaboration is discussed. Meanwhile, the boundary condition of the influence of digital capability on green innovation performance in the view of top management's environmental awareness is explored. Finally, an empirical test is conducted based on the Chinese manufacturing corporates. The results indicate that green innovation performance is significantly and favorably impacted by digital capability, green supply chain collaboration plays a partial mediating role between digital capability and green innovation performance, and top management's environmental awareness can positively moderate the effect of digital capability on green innovation performance. This study offers valuable theoretical and practical enlightenments for manufacturing companies to foster the growth of green innovation through digital capability more effectively.

The social role of supply chain firms during the pandemic period

Purpose

Social risk management is vital for growth and business continuity. This study investigates the social risk shift in supply chain management during the Coronavirus Disease 2019 (COVID-19) pandemic.

Design/methodology/approach

Data were retrieved from Bloomberg between 2010 and 2021 regarding all supply chain enterprises from nine countries. The authors undertake a confirmatory examination of formulated hypotheses. Social supply chain risk (SSCR) refers to “firms that took the necessary steps to decrease social risks in their supply chain. Social risks involve the child or forced labor, poor working conditions, lack of a living and fair or minimum wage”. The authors complement the analysis and address the endogeneity issue using the dynamic generalized moments method (GMM).

Findings

A significant positive relationship between COVID-19 and SSCR was discovered in this study. Due to the COVID-19 pandemic, supply chain firms faced supply chain social risk. Notably, SSCR policies differ from one country to another during this period.

Research limitations/implications

The research has some limitations. The sample data are limited to 9 countries. Furthermore, it was somewhat difficult to determine the country-wise difference using COVID-19 as a dummy variable. Future research may adopt qualitative approaches, such as structural or semi-structural interviews.

Practical implications

The results have important implications for supply chain practitioners to consider the critical role of social risk in their operations. COVID-19 has exposed the new political economy and re-centered governments as the key actors in tackling grand challenges to safeguard workers, produce socially useful products and protect their stakeholders. Also, the study highlights the importance of governments and policymakers having a well-structured regulatory framework and environment for firms to comply with the social norms in their supply chain management. Finally, the study's findings should encourage supply chain managers to adopt a proactive mechanism that reduces the social risk impacts of pandemics.

Originality/value

Considering the historical backdrop of the COVID-19 pandemic, this study is unique in measuring the SSCR of enterprises from a worldwide viewpoint.

The effects of analytics capability and sensing capability on operations performance: the moderating role of data-driven culture

Studies indicate that organizational capability is a key factor in operational performance, and that both sensing and analytics capabilities have a significant influence on operational performance. This study develops a framework to examine the impact of organizational capability on operational performance, with a specific focus on the implementation of sensing and analytics capabilities. We combine strategic fit theory, the dynamic capability view, and the resource-based view to examine how micro, small, and medium enterprises (MSMEs) strategically integrate a data-driven culture (DDC) with their organizational capabilities to enhance operational performance. We carry out empirical research to investigate whether a DDC moderates the influence of organizational capability on operational performance. Structural equation modeling of survey data from 149 MSMEs reveals that both sensing and analytics capabilities have a positive impact on operational performance. The results also suggest that a DDC positively moderates the influence of organizational capability on operational performance. We discuss the theoretical and managerial implications of our findings, the limitations of the study, and opportunities for further research.

Risk management in sustainable supply chain: a knowledge map towards intellectual structure, logic diagram, and conceptual model

The global spread of COVID-19, international trade protectionism, geopolitical conflicts, and climate change presents challenges and risks to sustainable supply chains (SSCs). In recent years, scholarly interest in sustainable supply chain risk management (SSCRM) has continued to rise. A helpful literature review is necessary to enable supply chain practitioners to apply empirical findings from academic research or conceptual frameworks to their operations to maintain the stability and competitiveness of sustainable supply chains. The knowledge map of SSCRM is explored in this study using both quantitative and qualitative analysis. A total of 793 articles were retrieved to reveal the knowledge map of SSCRM. Scientometric and context analysis are combined in quantitative analysis to identify the intellectual structure of risk management research related to SSC. Then, a critical review is conducted in qualitative analysis to summarize and analyze the motivations, strategies, approaches, and tools of SSCRM. Combining the quantitative and qualitative analysis results, a conceptual model is constructed for SSCRM from three aspects: (1) risk identification, (2) risk assessment, and (3) risk mitigating and responding. Finally, future research directions are suggested based on the conceptual model for guiding the theories and practice of SSCRM. This study can work as a roadmap for providing appropriate risk management policies and toolkits to SSC, which could advance theoretical thinking on how to mitigate SSC risks.

Predicting supply chain risks through big data analytics: role of risk alert tool in mitigating business disruption

Purpose

As organizations globalize, they are facing twin challenges of (1) how to develop actionable intelligence from the vast amount of data flowing into their organization and (2) how to effectively manage the increasing risks to their supply chain. Therefore, the purpose of this paper is to bring these two issues on a single platform to understand how firms can effectively predict supply chain risk by developing and using BDA capabilities, through an automated risk alert tool.

Design/methodology/approach

The authors used a questionnaire-based survey methodology supported by secondary data to collect information related to managerial perceptions on how firms can develop a risk alert tool by improving BDA capabilities. A database of 213 senior and middle-level managers was developed and used to test the proposed hypothesis. Using econometric techniques, the authors identify the conditions necessary for such an automated risk management tool to be effective.

Findings

The results suggest that if organizations focus on developing an effective IT infrastructure supported by a strong BDA capability, they will be able to leverage these capabilities to develop an effective risk management tool. Moderating influences of Upstream and Downstream Supply Chain IT Infrastructure capabilities were also observed on different types of BDA capabilities within a firm. In conclusion, it was argued that the effectiveness of a risk alert tool is dependent on how well firms harness big data analytics capability.

Originality/value

The value of the research stems from the fact that it uses managerial surveys to identify specific BDA capabilities that can enable firms to develop risk resilience capabilities. In addition, the article is one of the few empirical studies that aims to identify how firms can use BDA capabilities within a supply chain context to develop an automated risk alert tool. The article, therefore, contributes to the literature that identifies the value of BDA capabilities within the context of supply chain risk management.

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.

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.

A Novel Fuzzy-Based VIKOR–CRITIC Soft Computing Method for Evaluation of Sustainable Supply Chain Risk Management

This article aims to evaluate sustainable supply chain risks using a novel fuzzy VIKOR–CRITIC technique. The research contributions of this study are twofold. First and foremost, this is the first attempt to integrate the fuzzy VIKOR approach with the CRITIC method in order to eradicate the inadequacies of the VIKOR method. Second, this is the first study to look at the sustainable supply chain risk management in Pakistan’s logistics industry. Four logistics companies were chosen for the study, and thirty criteria were established and divided into four categories using acquired data and literature studies. According to the findings, organizational risks are the most important to consider, whereas environmental hazards have the least influence. Supply delays, freight rate/oil price fluctuations, bankruptcy, and natural catastrophe are the four most important criteria in these categories. Limited suppliers, cargo tracking, IT system failure, and international politics are the four least significant criteria in the four risk categories. The findings are useful for the logistics industry operating in CPEC for risk mitigation and sustainable operation. The research may be used as a guideline for risk identification and management by practitioners and decision-makers in Pakistani logistics organizations.

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.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

Using Big Data for Sustainability in Supply Chain Management

In the literature, several frameworks have been proposed to help sustainability management in supply chains. Nevertheless, they present a number of shortcomings. With the aim of overcoming these shortcomings, this paper proposes a framework for sustainable supply chain management composed of six dimensions: methodology, organization, stakeholders, maturity model, human resources, and technology. The main innovations of the framework are that (1) it includes a methodology that acts as a guide to sustainability management and improvement in a holistic way by using a balanced scorecard for any type of supply chain and covering the whole project life cycle; (2) it combines quantitative and qualitative methods for sustainability assessment; (3) it describes the techniques and technology to be used in each task of the methodology; and (4) it identifies the past impact of SC sustainability, as well as predicting its future impact, using Big Data analytics. The practical utility, completeness, and level of detail of the framework were validated through questionnaires answered by both five academics and three professionals. In addition, the framework was applied to a case study to (1) validate its usefulness and (2) to improve it with the feedback obtained.

Enabling Blockchain Based SCM Systems with a Real Time Event Monitoring Function for Preemptive Risk Management

The risk of supply chain disruption is usually related to daily disturbances in supply chain operations (e.g., demand fluctuations) and some emergency risks, such as earthquakes and epidemic outbreaks. During a crisis, companies need agility to quickly find new suppliers and open auxiliary sales channels to meet customer needs and remain competitive. However, identifying “event” is one of the most difficult challenges of current decision support systems. If the system encounters an emergency, it is usually unable to promptly notify users of the warning to avoid risks. A sensible solution is to incorporate the real-time event-monitoring system into SCM (i.e., supply chain management) in order to share emergency information in the early stage for preemptive management in the supply chain. On the other hand, in order to process confidential supply chain data with other members, the SCM infrastructure requires secure data sharing. The blockchain-based SCM system can improve the transparency of traceability to ensure that the supply chain system provides high-quality products and protects data privacy and security. The view is taken; therefore, in this work, we combined a method of real-time event detection using collected Twitter data and blockchain technology for event monitoring to improve the visibility of the supply chain system and take preemptive measures for risk avoidance. The experiments show some interesting results and potentials for future work in the field of the agile supply chain.

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review

In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing literature to ascertain recent developments and uncover future areas of research. Despite the increasing number of publications on BNs in the domain of SC uncertainty, an extensive review on their application to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peer-reviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions. The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.

Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda

While the potential benefits of integrating digital technologies and supply chain management have been widely reported, less is known concerning the current state-of-the-art literature on big data-driven sustainable supply chains. Therefore, this study aims to systematise published studies which address the implications of big data for sustainable supply chain management. Through a systematic literature review, this work makes three significant contributions: (a) it provides an overview of extant literature on this topic in recent years; (b) it proposes seven gaps in the literature in order to foster future investigations on big data-driven sustainable supply chains; (c) it offers four lessons for business practitioners aiming to use big data for sustainable supply chain practices. These lessons suggest that: developing big data analytics capability has to become a business priority in order to effectively build competitive sustainable supply chains; big data has benefits for each of the dimensions of the triple-bottom-line in supply chains; the implementation of big data for sustainability in supply chains presents some challenges for firms; the development of complementary organizational capabilities is needed to overcome challenges and facilitate the benefits of big data technology for sustainable supply chain management.

Sustainable Supply Chain in the Era of Industry 4.0 and Big Data: A Systematic Analysis of Literature and Research

Supply chain sustainability (SCS) in the age of Industry 4.0 and Big Data is a growing area of research. However, there are no systematic and extensive studies that classify the different types of research and examine the general trends in this area of research. This paper reviews the literature on sustainability, Big Data, Industry 4.0 and supply chain management published since 2009 and provides a thorough insight into the field by using bibliometric and network analysis techniques. A total of 87 articles published in the past 10 years were evaluated and the top contributing authors, countries, and key research topics were identified. Furthermore, the most influential works based on citations and PageRank were obtained and compared. Finally, six research categories were proposed in which scholars could be encouraged to expand Big Data and Industry 4.0 research on SCS. This paper contributes to the literature on SCS in the age of Industry 4.0 by discussing the challenges facing current research but also, more importantly, by identifying and proposing these six research categories and future research directions.

How Does the Contingent Sustainability–Risk–Cost Relationship Affect the Viability of CSR? An Emerging Economy Perspective

Sustainability has become a core strategic initiative for firms in the global economy. Its key benefits aside, sustainability may increase a firm’s risks, undermining its prospective value. The intricate relationship among sustainability’s impact on various dimensions of firm risk is poorly understood, particularly for firms operating in emerging economies. The purpose of this study is to address this gap by developing a nuanced framework for the sustainability–risk relationship in various industries in emerging economies. A multi-method approach was used to collect both quantitative and qualitative data through interviews and site visits for supply chain members of four industries. A fuzzy AHP method was used to illustrate cross-industry differences in sustainability-induced firm risks. These differences are further illustrated through inductive, interpretive analysis of semi-structured interviews. Sustainability behaves as a limits-to-growth system and engenders different risk profiles across four industries. For all firms in emerging economy, sustainability initiatives increase various unanticipated risks. Thus, these firms must saliently tailor sustainability initiatives uniquely suitable for their industry to avoid compromising their value proposition. Insights gleaned from this study may assist both buyers from multinational corporations in the developed economy to propagate sustainability initiatives and suppliers in the emerging economy to implement sustainability initiatives more saliently.

The Impact of Big Data Analytics on Company Performance in Supply Chain Management

Big data analytics can add value and provide a new perspective by improving predictive analysis and modeling practices. This research is centered on supply-chain management and how big data analytics can help Romanian supply-chain companies assess their experience, strategies, and professional capabilities in successfully implementing big data analytics, as well as assessing the tools needed to achieve these goals, including the results of implementation and performance achievement based on them. The research method used in the quantitative study was a sampling survey, using a questionnaire as a data collection tool. It included closed questions, measured with nominal and ordinal scales. A total of 205 managers provided complete and useful answers for this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. The major contributions of this research highlight the fact that companies are concerned with identifying new statistical methods, tools, and approaches, such as cloud computing and security technologies, that need to be rigorously explored.

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.