Big data analytics in supply chain and logistics: an empirical approach

Industry 4.0 and life cycle assessment: Evaluation of the technology applications as an asset for the life cycle inventory

Industry 4.0 technologies present transformative potential in data acquisition for production activities, promising to revolutionize the Life Cycle Inventory process. Despite acknowledging their utility in environmental impact analysis, a gap exists in understanding the specific applicability of these technologies to fulfill ISO 14044 criteria. This study addresses the gap by introducing innovative approaches to Life Cycle Assessment through Industry 4.0 technologies. Beyond existing research, technologies directly impacting LCA development are identified, along with a classification for optimal usage in the LCA process. The crucial role of these technologies in enhanced data collection across life cycle phases is highlighted, introducing a scoring mechanism to identify the technology excelling in enabling Life Cycle Inventory development. Employing a developed framework and systematic literature review, the study aims to identify Industry 4.0 technologies in manufacturing that facilitate LCA. Findings illuminate potential contributions across different product life cycle stages, with cyber-physical systems, the Internet of Things, and Simulation and Modelling identified as the most effective technologies for constructing Life Cycle Inventories. The outcomes provide guidance for practitioners in integrating Industry 4.0 technologies into manufacturing activities, offering valuable insights for environmental sustainability assessment.

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.

Big data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation

Purpose

Big data analytics (BDA) capabilities can affect supply chain performance in several ways. The main purpose of this study was to understand how BDA capabilities could affect supply chain performance through supply chain resilience and supply chain innovation.

Design/methodology/approach

The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. Accordingly, 187 responses were collected and analyzed using partial least squares (PLS) in the SmartPLS3.

Findings

The results showed that BDA capabilities improve supply chain performance through resilience and innovation of the supply chain.

Originality/value

The present study also contributed to the existing literature by demonstrating the mediating role of supply chain resilience and supply chain innovation between BDA capabilities and supply chain performance. In this context, some theoretical and managerial implications were proposed and discussed.

A systematic review of AR/VR in operations and supply chain management: maturity, current trends and future directions

Purpose

This research aims to provide systematic insight into the current maturity of augmented reality (AR) and virtual reality (VR) in operations and supply chain management (OSCM), by analyzing the existing literature, contemporary concepts, data and gaps for future research directions.

Design/methodology/approach

This research uses information from existing literature regarding timeline trends, publishers, research design and method, industry engagement, geographic location, active authors and affiliated universities, concentrated topics, theories and maturity in its review. A collection of publishing databases from 1997 to 2021 were explored using the keywords “Augmented Reality,” “Virtual Reality” and “Operations” and/or “Supply Chain” in their title and abstract to examine which publications to be included. Based on the search, a total of 164 journal articles were selected, and information on a chain of variables was collected.

Findings

There has been a significant publication growth over the past 25 years regarding the adoption of AR/VR in OSCM. Key findings indicate that 52% of the publications were focused on manufacturing, with only 10% of the existing literature using background theories. AR/VR can be observed at the introduction and growth phase and have yet to reach their maturity. Furthermore, there is limited utilization of AR/VR as drivers in facilitating sustainable practices in OSCM by academics and practitioners, albeit a strong promise exists. Finally, the prospective applications of AR/VR toward post-COVID-19 supply chains recovery require special attention.

Research limitations/implications

This systematic review is limited to considering only academic articles available from Emerald, Elsevier, Taylor and Francis, Springer, Scopus, JSTOR and EBSCO containing the keyword parameters.

Originality/value

The study used a bibliometric review to identify the trends and maturity in the evolution of AR/VR in OSCM. This research provides a better understanding of current research practices and offers directions toward the adoption of AR/VR in OSCM.

Effect of Big Data Analytics in Reverse Supply Chain

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasises on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 330 industries. Of the 330 surveys that were mailed, 125 completed surveys were returned, corresponding to a response rate of 37.87 percent, which was slightly greater than previous studies (Queiroz and Telles, 2018).The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized.

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

Purpose

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

Design/methodology/approach

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

Findings

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

Research limitations/implications

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

Originality/value

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

Supply chain resilience during the COVID-19: empirical evidence from an emerging economy

Purpose

Although the advances in the supply chain resilience (SCR) literature, there is a critical gap concerning this understanding in a high disruption context, such as in the case of the COVID-19. This paper aims to investigate some dimensions (agility, robustness, disruption orientation and resource reconfiguration) of the SCR during this unprecedented disruption in the Brazilian supply chain context.

Design/methodology/approach

Supported by the resource-based view, dynamic capabilities and the SCR literature, we developed a model, which in turn was analyzed and validated by partial least squares structural equation modelling.

Findings

The results revealed that while resource reconfiguration and supply chain disruption orientation positively affect SCR, we found a non-significant effect of supply chain robustness and agility.

Practical implications

The findings suggest that in a considerable disruption scenario, managers with their supply chain operations in emerging economies should prioritize the development of resources to support the disruption orientation and manage the scarce resources adequately by reconfiguring them.

Originality/value

Our study is one of the first that reported the dynamics of the SCR dimensions in an emerging economy during the COVID-19.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

Purpose

Big data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.

Design/methodology/approach

The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.

Findings

The results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.

Originality/value

The present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Adoption of Artificial Intelligence in Supply Chain Risk Management

The study aims to examine factors that influence the adoption-diffusion process of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM) across manufacturing, wholesale trade, retail trade, and transportation industries in India. As part of this study, eleven constructs that influence the adoption-diffusion stages of AI in SCRM were identified and examined. A survey was conducted to collect data from supply chain executives, risk professionals, and AI consultants across the manufacturing, wholesale trade, retail trade, and transportation industries in India. Partial least squares structural equation modeling (PLS-SEM) was used to study the data. Results show that these factors have varying degrees of influence and direction on the three stages of adoption of AI in SCRM. The study will enable the leadership team in the organizations to build a roadmap for the adoption, implementation, and routinization of AI in SCRM.

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Background

The current level of industrialization has generated many challenges worldwide, including ecological hazards, climate change, and the overuse of non-renewable natural resources, thereby creating an increasing demand for achieving the goal of the Triple Bottom Line (TBL). In this regard, Industry 4.0 can be used as a crunch point to contribute to the production process that can help achieve sustainable development.

Purpose

While the Malaysian government proposed the “Industry4ward” approach to enhance technological adoption, there is scarce empirical evidence in the literature that validates SMEs for Industry 4.0. Using Dynamic Capability View (DCV), this study proposes a framework that includes core determinants like top management commitment, supply chain integration, and IT infrastructure, that can significantly influence Industry 4.0 implementation toward achieving TBL sustainability.

Design/methodology/approach

Employing simple random sampling, the study adopted a quantitative approach based on 199 useable respondent's feedback collected through a survey questionnaire of 900 employees from Malaysian SMEs. The statistical analysis was performed using Structural Equation Modeling (Partial Least Square, SmartPLS 3.3.2).

Findings

The results show that top management and IT infrastructure significantly impact Industry 4.0 implementation and sustainability. In contrast, the analysis also demonstrates that supply chain integration is insignificant to Industry 4.0 implementation in SMEs. The findings also indicate that the relationship between the determinants of Industry 4.0 and TBL sustainability can be mediated by the “effective implementation” of Industry 4.0.

Recommendations

The study highlights the practical consequences of the role and use of the determinants in Industry 4.0 implementation. Its findings help managers and policy-makers to optimize value creation to achieve sustainable development goals.

Limitations and future research

Focusing only on Malaysian manufacturing SMEs may restrict the generalization of the study; thus, a benchmarking analysis from other industrial settings is encouraged. The questionnaire-based survey is a further limitation of the study.

Contributions of Industry 4.0 to supply chain resilience

Purpose

This study aims at identifying the contribution of Industry 4.0 (I4.0) integration into supply chains (SCs) to the enhancement of SC resilience.

Design/methodology/approach

A scoping review was conducted so that the relevant literature on SC resilience, and I4.0 integrated into SC management was examined.

Findings

The authors summarize the main findings from existing research and propose three research directions: (1) empirical validation of the contribution of I4.0 ICTs to SC resilience; (2) explore the role of processing-actuation technologies in enhancing restorative capacity; and (3) integration between I4.0 ICTs and omni-channel strategy as a means to resilience development at consumer and retail levels. The literature on the design of resilient smart SCs is far outnumbered by works reporting applications of I4.0 ICTs at different SC tier levels. However, the authors’ scoping review organizes the information available on these themes, setting the ground for the development of new theoretical propositions.

Originality/value

The integration of digital technologies from I4.0 can fundamentally change the SC management, acting as enablers of a more effective response to disruptions. However, the digital transformation of SCs is still incipient, and literature is particularly sparse when considering the contribution of I4.0 to the resilience of SCs.

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

Purpose

In this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.

Design/methodology/approach

The hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.

Findings

It is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.

Practical implications

The findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.

Originality/value

To the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

Purpose

The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.

Design/methodology/approach

The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.

Findings

The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.

Originality/value

This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

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

Purpose

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

Design/methodology/approach

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

Findings

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

Research limitations/implications

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

Originality/value

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

The conceptual framework on integrated flexibility: an evolution to data-driven supply chain management

Purpose

This paper aims to establish a relationship between supply chain integration (SCI) and supply chain flexibility (SCF) to develop a two-dimensional approach, i.e. integrated flexibility.

Design/methodology/approach

Building on a relational view and dynamic capability theory, this paper argues that integrated flexibility is the strategy that enables organisations to achieve different positions and states to create distinctive capability. The article has proposed the conceptual framework that connects different supply chain strategies and practices to improve supply chain performance (SCP) considering the cross-disciplinary approach.

Findings

The conceptual framework around the new perspective, i.e. integrated flexibility, is built to deal with issues related to operations management. The paper suggests examining the mediating effect of SCF between SCI and SCP and the moderating role of knowledge management (KM), data analytics (DA) and quality management (QM) practices on their relationship. Moreover, research direction in terms of propositions and implications are developed to showcase how underlying practices streamline the supply chain and lead to superior SCP.

Practical implications

The proposed framework discusses the degree of integration and flexibility levels to guide practitioners in designing a supply chain strategy with their partners and answering how much resources need to be extended to achieve flexible operations and realise SCP.

Originality/value

Authors have developed an entirely new integrated flexibility concept that provides a base to sustain in the competitive market. The foundation of integrated flexibility is built on relational view and dynamic capability theory and supported by DA, QM and KM.

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

Purpose

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

Design/methodology/approach

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

Findings

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

Practical implications

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

Originality/value

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

Applying Industry 4.0 technologies in the COVID–19 sustainable chains

Purpose

The purpose of this paper is 1) to investigate the effects on the crucial Industry 4.0 technological innovations that interact between the real and virtual worlds and that are applied in the sustainable supply chain process; 2) to contribute to the identification of the opportunities, the challenges and the gaps that will support the new research study developments and 3) to analyze the impact of the Industry 4.0 technologies as facilitators of the sustainable supply chain performance in the midst of the Coronavirus (COVID-19).

Design/methodology/approach

This research is performed through a bibliographic review in the electronic databases of the Emerald Insight, the Scopus and the Web of Science, considering the main scientific publications on the subject.

Findings

The bibliographic search results in 526 articles, followed by two sequential filters for deleting the duplicate articles (resulting in 487 articles) and for selecting the most relevant articles (resulting in 150 articles).

Practical implications

This article identifies the opportunities and the challenges focused on the emerging Industry 4.0 theme. The opportunities can contribute to the sustainable performance of the supply chains and their territories. The Industry 4.0 can also generate challenges like the social inequalities related to the position of the man in the labor market by replacing the human workforce with the machines. Therefore, the man-machine relationship in the Industry 4.0 era is analyzed as a gap in the literature. Therefore, as a way to fill this gap, the authors of this article suggest the exploration of the research focused on the Society 5.0. Also known as “super-smart society,” this recent theme appeared in Japan in April 2016. According to Fukuda (2020), in addition to the focus on the technological development, the Society 5.0 also aims at the quality of life and the social challenge resolutions.

Originality/value

This article contributes to the analysis of the Industry 4.0 technologies as facilitators in the sustainable supply chain performance. It addresses the impacts of the Industry 4.0 technologies applied to the supply chains in the midst of the COVID-19 pandemic, and it analyzes the research gaps and limitations found in the literature. The result of this study can add value and stimulate new research studies related to the application of the Industry 4.0 technologies as facilitators in the supply chain sustainable performance. It can encourage the studies related to the COVID-19 impacts on the sustainable supply chains, and it can promote the research development on the relationship among the man, the machine and the labor in the Fourth Industrial Revolution.

Examining the role of logistics 4.0 enabled dynamic capabilities on firm performance

Purpose

The study investigates the effect of technological capabilities, organizational capabilities and environmental capabilities on Logistics 4.0 capabilities and also examines the effect of Logistics 4.0 capabilities on firm performance.

Design/methodology/approach

The proposed theoretical framework is tested using WarpPLS 6.0 software. We selected samples from the Automotive Component and Allied Manufacturers in South Africa. Initially, we sent the structured questionnaire online using Google forms to 800 potential respondents. After doing follow ups, we received 230 completed survey responses. Further, data preparation is done using established scientific approach and we checked suitability of its use in structural equation modelling. After ensuring all necessary checks are completed, the results are found satisfactory to further proceed with testing of research hypotheses.

Findings

It is observed that technological capabilities, organizational capabilities and environmental capabilities show significant effect on Logistics 4.0 capabilities. However, the outcome of technological capabilities and environmental capabilities on Logistics 4.0 capabilities (ß = 0.27) is found stronger than organizational capabilities. Logistics 4.0 capabilities shows significant effect on firm performance.

Practical implications

It is important that the sustainability goals are aligned with Logistics 4.0 strategies. Managers need to increase focus towards development of Logistics 4.0 dynamic capabilities that enhance agility and responsiveness in the supply chain. Managers should check the financial performance and market conditions continuously to further review logistics performance as this can influence the overall firm performance.

Originality/value

This study advances the literature on Logistics 4.0 applications in operations management by investigating the key links such as Logistics 4.0 capability development and firm performance.

Organizational learning and Industry 4.0: findings from a systematic literature review and research agenda

Purpose

Industry 4.0 has been one of the most topics of interest by researches and practitioners in recent years. Then, researches which bring new insights related to the subjects linked to the Industry 4.0 become relevant to support Industry 4.0's initiatives as well as for the deployment of new research works. Considering “organizational learning” as one of the most crucial subjects in this new context, this article aims to identify dimensions present in the literature regarding the relation between organizational learning and Industry 4.0 seeking to clarify how learning can be understood into the context of the fourth industrial revolution. In addition, future research directions are presented as well.

Design/methodology/approach

This study is based on a systematic literature review that covers Industry 4.0 and organizational learning based on publications made from 2012, when the topic of Industry 4.0 was coined in Germany, using data basis Web of Science and Google Scholar. Also, NVivo software was used in order to identify keywords and the respective dimensions and constructs found out on this research.

Findings

Nine dimensions were identified between organizational learning and Industry 4.0. These include management, Industry 4.0, general industry, technology, sustainability, application, interaction between industry and the academia, education and training and competency and skills. These dimensions may be viewed in three main constructs which are essentially in order to understand and manage learning in Industry 4.0's programs. They are: learning development, Industry 4.0 structure and technology Adoption.

Research limitations/implications

Even though there are relatively few publications that have studied the relationship between organizational learning and Industry 4.0, this article makes a material contribution to both the theory in relation to Industry 4.0 and the theory of learning - for its unprecedented nature, introducing the dimensions comprising this relation as well as possible future research directions encouraging empirical researches.

Practical implications

This article identifies the thematic dimensions relative to Industry 4.0 and organizational learning. The understanding of this relation has a relevant contribution to professionals acting in the field of organizational learning and Industry 4.0 in the sense of affording an adequate deployment of these elements by organizations.

Originality/value

This article is unique for filling a gap in the academic literature in terms of understanding the relation between organizational learning and Industry 4.0. The article also provides future research directions on learning within the context of Industry 4.0.

Sustainability of Commercial Banks Supported by Business Intelligence System

This article was focused on establishing whether Business Intelligence (BI) systems provide sustainability to commercial banks by influencing their financial condition. As part of the search for a solution to the research problem, a hypothesis was formulated which assumes that the use of the Business Intelligence management system improves the financial condition of commercial banks. To assess this impact, a novel comparative method was used, which assumed comparing financial condition indicators in three aspects: before and after the implementation of the Business Intelligence system (comparison over time), with average indicators of a group of banks (comparison to the industry), with reference to changes in the overall economic situation. As a result of the method used, a synthetic indicator of the impact of using Business Intelligence (ABI) was calculated. This study was conducted in relation to six out of the thirteen largest commercial banks listed on the Warsaw Stock Exchange in 2020, which have implemented the Business Intelligence system since 2001. The assets of the examined banks cover 60% of the assets of commercial banks in Poland. As a result of the study, a positive impact of using the BI system on selected areas of the financial condition of commercial banks was identified. In particular, this impact relates to areas of productivity, the quality of assets and liabilities, profitability and debt. The generalized results of this study allow for the determination of cause and effect relationships between the use of the BI system in commercial banks and the improvement of the financial condition indicators as well as sustainability banking.

Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach

Purpose

The purpose of this paper is to present a theoretical approach based on the balanced scorecard (BSC) with regard to performance measurement – PM in supply chains for the Industry 4.0 era.

Design/methodology/approach

This paper combines the literature of PM and specifically the BSC with the literature related to the dimensions of supply chain in the context of Industry 4.0.

Findings

Dimensions extracted from the literature based on supply chains within the context of Industry 4.0 showed a strong alignment with the four perspectives of the BSC, which make it suitable to be considered as a performance measurement system (PMS) for supply chains in this new context.

Research limitations/implications

From theoretical perspective, this study contributes to the limited literature on PM for supply chains in Industry 4.0 era. The study proposes a supply chain 4.0 Scorecard and strongly support researchers to conduct future empirical researches in order to get a deeper understanding about PM in supply chains in the Industry 4.0 era. As limitations, the theoretical framework proposed needs further empirical research in other to validate it and obtain new insights over the investigation conducted and presented into this paper.

Practical implications

Practitioners can use this study as a guide to develop more effective performance measurement systems – PMSs in their organizations.

Originality/value

This research is unique as it addresses a significant knowledge gap related to PM in supply chains in the Industry 4.0 era. It brings a significant contribution in terms of understanding how to measure performance in supply chains in this new era.

Industry 4.0 and digital supply chain capabilities

Purpose

The Industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional business models and hastening the need for a redesign and digitisation of activities. In this context, the literature concerning the digital supply chain (DSC) and its capabilities are in the early stages. To bridge this gap, the purpose of this paper is to propose a framework for digital supply chain capabilities (DSCCs).

Design/methodology/approach

This paper uses a narrative literature approach, based on the main Industry 4.0 elements, supply chain and the emerging literature concerning DSC disruptions, to build an integrative framework to shed light on DSCCs.

Findings

The study identifies seven basic capabilities that shape the DSCC framework and six main enabler technologies, derived from 13 propositions.

Research limitations/implications

The proposed framework can bring valuable insights for future research development, although it has not been tested yet.

Practical implications

Managers, practitioners and all involved in the digitalisation phenomenon can utilise the framework as a starting point for other business digitalisation projects.

Originality/value

This study contributes to advancing the DSC literature, providing a well-articulated discussion and a framework regarding the capabilities, as well as 13 propositions that can generate valuable insights for other studies.

Diagnosis of delivery vulnerability in a logistics system for logistics risk management

Purpose

Delivery vulnerability is a critically important theme in logistics risk management. However, while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to investigate delivery vulnerabilities in a logistics system using its own accumulated data.

Design/methodology/approach

This study utilizes pragmatic business analytics to derive insights on logistics risk management from operations data in a logistics system. Additionally, normal accident theory informs the discussion of its management implications.

Findings

This study’s analytical results reveal that a tightly coupled logistics system can align with normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components but also interactive ones.

Research limitations/implications

The tailored business analytics comprise a research foundation for logistics risk management. Additionally, the important research implications of this study’s analytical results arrived at via such results’ integration with normal accident theory demonstrate the value of that theory to logistics risk management.

Practical implications

The trade-offs between logistics risk and logistics-system efficiency should be carefully evaluated. Moreover, improvements to such systems’ internal resilience can help to alleviate potential logistics vulnerabilities.

Originality/value

This pioneering analytical study scrutinizes the critical vulnerability issues of a logistics service provider and therefore represents a valuable contribution to the field of logistics risk management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of delivery-vulnerability data.

Towards Analytics-Enabled Efficiency Improvements in Maritime Transportation: A Case Study in a Mediterranean Port

The current digitalization trend, the increased attention towards sustainability, and the spread of the business analytics call for higher efficiency in port operations and for investigating the quantitative approaches for maritime logistics and freight transport systems. Thus, this manuscript aims at enabling analytics-driven improvements in the port transportation processes efficiency by streamlining the related information flow, i.e., by attaining shorter time frames of the information and document sharing among the export stakeholders. We developed a case study in a mid-sized European port, in which we applied Process Mining (PM)—an emerging type of business analytics—to a seven-month dataset from the freight export process. Four process inefficiencies and an issue that can jeopardize the reliability of the time performance measurements were detected, and we proposed a draft of solutions to cope with them. PM enabled enhancements in the overall export time length, which might improve the vessels’ turnover and reduce the corresponding operational costs, and supported the potential re-design of performance indicators in process control and monitoring. The results answer the above-mentioned calls and they offer a valuable, analytics-based alternative to the extant approaches for improving port performance, because it focuses on the port information flow, which is often related to sustainability issues, rather than the physical one.

Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics

Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.