A global exploration of Big Data in the supply chain

Supply chain digitalization and performance: A moderated mediation of supply chain visibility and supply chain survivability

The COVID-19 crisis has notably impacted global supply chains as it has disrupted manufacturing operations. To recover from the aforementioned disruptions, supply chain digitalization [SCD] is increasingly being acknowledged to help the recovery process. Based on this, scholars have called for additional research on how SCD can enhance supply chain visibility [SCV] and boost supply chain performance [SCP] in turbulent environments. Based on 399 valid responses collected through cross-sectional method from Turkish manufacturing firms and using a non-probabilistic sampling method [i.e., purposive sampling], this research explores the effect of SCD on SCP. The mediating role of SCV and the moderating role of supply chain survivability [SCS] on the SCD-SCP relationship were also explored. The findings showed that SCD has a positive effect on SCP. SCD has a positive effect on SCV. SCV has a positive effect on SCP. The link between SCD and SCP is mediated by SCP. The results also revealed that SCS moderated the SCD-SCV link such that SCD has a stronger, positive relationship with SCV when SCS is high than when it's low. SCS moderates the SCD-SCP link, such that at low levels of SCS, the positive effect of SCD on SCP is weakened. The indirect positive effect of SCD on SCP via SCV is strongest when supply chain survivability is high. The findings suggest that SCD can improve cost-effectiveness, promote communication and information efficiency, and enhance supply chain resilience to improve performance after disruptions. This study provides insightful new implications for both supply chain literature and practitioners.

Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model

The outbreak of COVID-19 has accelerated the building of resilient supply chains, and supply chain digitalization is gradually being recognized as an enabling means to this end. Nevertheless, scholars generally agree that more empirical studies will need to be conducted on how digitalization can facilitate supply chain resilience at various stages and enhance supply chain performance in a highly uncertain environment. To echo the call, this study develops a theoretical influence mechanism of "supply chain digitalization → supply chain resilience → supply chain performance" based on dynamic capability theory. The proposed relationships are validated using survey data collected from 210 Chinese manufacturing companies. The results help identify the paths digitalization and supply chain resilience can take to improve supply chain performance in a turbulent environment. The different roles of three supply chain resilience capabilities, namely absorptive capability (before the disruption), response capability (during the disruption), and recovery capability (after the disruption), which impact on supply chain performance differently, are highlighted. In addition, it is found that digitalization can bring a differential impact on these three supply chain resilience capabilities through different aspects of resource and structural adjustment measures. The findings also confirm the mediating role of absorptive capability, response capability, and recovery capability between digitalization and supply chain performance. During crisis, supply chain digitalization can increase cost-effectiveness, enhance information and communication efficiency, and promote supply chain resilience to achieve better performance. For theoretical contribution, this study enriches the research on supply chain digitalization and resilience by underpinning the relationships between the two with dynamic capability theory. For practical contribution, the research findings provide insights for enterprises to leverage digitalization to strengthen resilience in supply chain.

The knowledge trajectory and structure of the supply chain integration: a main path and cluster analysis

Purpose

Supply chain integration (SCI) dominates supply chain strategy and is receiving increasing academic attention. The purpose of this paper is to provide a systematic review of the knowledge trajectory and structure of the SCI field.

Design/methodology/approach

Based on 3,533 papers extracted from the Web of Science (WoS), this paper adopts the main path analysis (MPA) method to detect three distinct knowledge development trajectories. Coupling-based clustering is combined with MPA to reveal three critical subfields.

Findings

The findings show that the definition, content and dimensions of SCI lack unified conclusions. The influencing factors and performance consequences of SCI are long-standing research elements. Building theoretical models and integrated systems and applying blockchain technology to improve SCI are the key research contents. The intertwining of collaboration and SCI cannot be ignored, and the green SCI may be a hot topic in the future.

Research limitations/implications

This study explores knowledge in the SCI field based on the limited literature collected by WoS rather than all published papers. The omissions of some relevant papers and books may exist.

Practical implications

The study methodology provides a framework for similar studies in the future, and the results help researchers to get a comprehensive picture of the knowledge trajectory and structure of the SCI field.

Originality/value

Compared to existing reviews, MPA combines cluster analysis to develop a synthetic framework of the knowledge trajectory and structure in the SCI domain. It contributes to a systematic review of the development of SCI.

Unlocking supply chain agility and supply chain performance through the development of intangible supply chain analytical capabilities

Purpose

Increasingly, studies are reporting supply chain analytical capabilities as a key enabler of supply chain agility (SCAG) and supply chain performance (SCP). This study investigates the impact of environmental dynamism and competitive pressures in a supply chain analytics setting, and how intangible supply chain analytical capabilities (ISCAC) moderate the relationship between big data characteristics (BDC's) and SCAG in support of enhanced SCP.

Design/methodology/approach

The study draws on the literature on big data, supply chain analytical capabilities, and dynamic capability theory to empirically develop and test a supply chain analytical capabilities model in support of SCAG and SCP. ISCAC was the moderated construct and was tested using two sub-dimensions, supply chain organisational learning and supply chain data driven culture.

Findings

The results show that whilst environmental dynamism has a significant relationship on the three key BDC's, only the volume and velocity dimensions are significant in relation to competitive pressures. Furthermore, only the velocity element of BDC's has a significant positive impact on SCAG. In terms of moderation, the supply chain organisational learning dimension of ISCAC was shown to only moderate the velocity aspect of BDC's on SCAG, whereas for the supply chain data driven culture dimension of ISCAC, only the variety aspect was shown to moderate of BDC on SCAG. SCAG had a significant impact on SCP.

Originality/value

This study adds to the existing knowledge in the supply chain analytical capabilities domain by presenting a nuanced moderation model that includes external factors (environmental dynamism and competitive pressures), their relationships with BDC's and how ISCAC (namely, supply chain organisational learning and supply chain data driven culture) moderates and strengthens aspects of BDC's in support of SCAG and enhanced SCP.

A large-scale real-world comparative study using pre-COVID lockdown and post-COVID lockdown data on predicting shipment times of therapeutics in e-pharmacy supply chains

Purpose

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

Design/methodology/approach

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

Findings

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

Research limitations/implications

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

Practical implications

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

Originality/value

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

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.

The role of Big Data in the business challenge of Covid-19: a systematic literature review in managerial studies

2020 was globally greatly affected by the Covid-19 pandemic caused by SARS-CoV-2, which is still today impacting and profoundly changing life globally for people but also for firms. In this context, the need for timely and accurate information has become vital in every area of business management. The spread of the Covid-19 global pandemic has generated an exponential increase and extraordinary volume of data. In this domain, Big Data is one of the digital innovation technologies that can support business organizations during these complex times. Based on these considerations, the aim of this paper is to analyze the managerial literature concerning the issue of Big Data in the management of the Covid-19 pandemic through a systematic literature review. The results show a fundamental role of Big Data in pandemic management for businesses. The paper also provides managerial and theoretical implications.

Decision-Making under the Risk, Uncertainty and COVID-19 Pandemic Conditions Applying the PL9A Method of Logistics Planning—Case Study

The next industrial revolution, which coincided with the COVID-19 pandemic, is prompting a different look at the issue of supply chain change management. A new perspective should take into account the aspect of supply chain efficiency at multiple levels. Efficient logistics is green and energy-saving, both of which need to be systematically integrated with the logistical planning processes. The dynamic changes on the demand and supply side resulting from social, political, and economic transformations have significantly influenced the shaping of long-term supply chains. The development of new manufacturing and logistics technologies prompts the development and implementation of new integrated planning methods to support supply chain management processes. Modern supply chains are oriented towards operations in a dynamically changing socio-economic environment. The new methods are capable of incorporating dynamic adaptation of logistics infrastructure which respond to changing relationships between supply and demand. To meet the identified problems of complexity, relevance, and time-consumption of the logistic planning process in modern enterprise management, the PL9A method of logistic planning and 9A LOGPLANNER application for logistics planning were developed. The article presents the results of experimental and simulation studies on the improvement of logistic processes in a working manufacturing enterprise with application of the PL9A method. The results of the experimental work indicate that the application of the PL9A method embedded in the 9A LOGPLANNER software makes it possible to dynamically simulate any number of logistics system variants in a short period of time, while reducing risk and obtaining tangible benefits in terms of energy and ecological efficiency.

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.

Using blockchain technology to drive operational excellence in perishable food supply chains during outbreaks

Purpose

This paper aims to explore the potential of blockchain technology (BT) to support the operational excellence in perishable food supply chain (PFSC) during outbreaks, by doing use-case analysis.

Design/methodology/approach

A systematic literature review is performed to determine the dimensions of operational excellence in the food supply chain (FSC), then a single use-case analysis is conducted to explore the potential of blockchain in order to achieve operational excellence for PFSC during the pandemics by applying context, interventions, mechanism and outcomes (CIMO) logic.

Findings

The findings of this study reveal that blockchain capabilities such as immutability and transparency, visibility, traceability, integration and interoperability, disintermediation and decentralisation, smart contracts and consensus mechanism provide better sustainable operational excellence outcomes for PFSCs to be more responsive, flexible, efficient and collaborative to cope with the impacts of COVID-19.

Research limitations/implications

This research employs only one real case with multiple PFSC participants. Statistical generalisation is not possible at this stage of the research. However, the findings are not restricted to this single use-case.

Practical implications

This study provides a research direction to explore the potential of BT to achieve operational excellence in the PFSC during outbreaks and generates prescriptive knowledge for better managerial decision-making across the PFSC during outbreaks.

Originality/value

This research conducts semi-structured interviews with different participants in one blockchain ecosystem to understand multiple participants' perspectives of operational excellence within PFSC.

Logistics 4.0 in Brazil: Critical Analysis and Relationships with SDG 9 Targets

The logistics sector is characterised as an important pillar of the Brazilian economy, significant regarding revenue and job creation. This study aims to critically analyse the logistical operations developed by Brazilian companies in the context of Logistics 4.0 and to structure a SWOT (strengths, weaknesses, opportunities and threats) matrix from the information gathered. In addition, relationships were established between the structured SWOT matrix and the targets of Sustainable Development Goal 9 (SDG 9). The methodological approach taken by the study consisted of semi-structured interviews with ten experts and data were analysed through content analysis. Once structured the SWOT matrix, the results were related with the targets of the SDG 9. The study presented the weaknesses and threats stand out in relation to the strengths and opportunities. When compared with the targets of SDG 9, it is possible to observe that many weaknesses are related to infrastructure and innovation. The main contributions of the study are the following: it is one of the few studies which holistically examined the sector in Brazil from a Logistics 4.0 perspective and the study points out some essential needs which should be addressed. The information presented here can broaden the debates on this topic and assist companies and government in the transition to digital transformation.

Celebrating IJPDLM's 50th anniversary: a reflection on its contributions and future directions

Purpose

This article celebrates the 50th anniversary of IJPDLM, reflects on the contribution of IJPDLM to the field of logistics and supply chain management (LSCM) and discusses future directions for the journal.

Design/methodology/approach

Descriptive analysis of manuscripts received and accepted by IJPDLM during 2015–2019 is used to provide an overview of the journal. Content analysis of selected articles is used to highlight important contributions of the journal. Changes made since 2020 are highlighted to inform future directions of IJPDLM. Invited articles are discussed and used to clarify future directions.

Findings

IJPDLM has made tremendous progress in informing and shaping the field of LSCM. Key issues addressed include sustainability and reverse logistics, omni-channel, e-commerce, retail logistics, risk, resilience, volatility, and complexity and digital technology innovation. The journal has expanded the use of methods beyond the typical qualitative and quantitative methods to explore the use of design science, experiment, conjoint analysis, qualitative comparative analysis, narrative analysis. The invited articles provide (1) a historical reflection of the purpose of the journal when it was launched, (2) new guidance on how to develop theories using literature review and grounded theories and (3) understanding of startups and supply chain ecosystems.

Practical implications

Some exemplar articles are highlighted to explain how IJPDLM informs LSCM managers, companies and policy makers.

Originality/value

This article explains the recent development and sets future directions for the LSCM field.

The Effects of Cryptocurrency Trading Websites on Airlines’ Advertisement Campaigns

In future years, airline companies will be leaning more and more towards cryptocurrencies to implement their digital marketing strategies as leaders seek to gain an understanding of the factors affecting airlines’ visibility parameters. Cryptocurrency investment websites are currently experiencing rising demand, making them an appropriate site for paid advertisements. The above factors suggest the need for airlines to harvest cryptocurrency investment and platform users in their favour. To this end, it can be beneficial for airlines’ web promotions to link certain web analytics metrics to cryptocurrency trading site metrics. For research purposes, web analytics data were monitored and gathered for 2 consecutive years from 10 globally leading cryptocurrency trading companies and 10 airline websites. A three-stage model was adopted by the authors. In the first stage, statistical analysis was implemented using cryptocurrency and airline metrics, followed by fuzzy cognitive mapping and agent-based modelling stages. The findings of the study indicate that engagement with cryptocurrency trading websites has a positive impact on airline websites’ global ranking and visibility parameters. The outcomes of this research provide noteworthy digital marketing strategies which can be addressed by airline companies to increase their website visitors and optimise visibility parameters with the assistance of cryptocurrency trading websites.

Influence of intellectual capital and integration on operational performance: big data analytical capability perspectives

Purpose

In the digital economy, as artificial intelligence applications increase, big data analytical capability (BDAC) plays a crucial role, and intellectual capital is growing in importance. This study aims to examine the possible benefits and effects of intellectual capital, BDAC and integrations on operational performance.

Design/methodology/approach

This study collected data from firms in Asia, and a total of 257 senior managers completed a questionnaire. Confirmatory factor analysis and structural equation modeling (SEM) is used for statistical analysis.

Findings

Intellectual capital positively correlates with BDAC, and BDAC positively relates to internal integration but not to external integration. Furthermore, both internal integration and external integration positively correlate with operational performance. This study supports that internal integration is a significant mediator in the influence of BDAC on operational performance.

Practical implications

First, the authors provide empirical evidence that intelligent capital in intangible resources helps firms to build BDAC. Second, this study stresses the importance of BDAC in business, which enhances the integration of the whole supply chain and results in superior operational performance.

Originality/value

This is a first attempt from the perspective of intelligent capital and uses SEM to emphasize the relationships among BDAC, supply chain integration and performance based on unique and irreplaceable intangible resources, thus providing a new perspective on the contributing factors of BDAC.

The Impact of Organic Traffic of Crowdsourcing Platforms on Airlines’ Website Traffic and User Engagement

With airline companies increasingly relying on crowdsourcing websites to deploy their digital marketing strategies, marketeers and strategists seek to acquire an understanding of the factors affecting airlines’ organic traffic and user engagement. Such an understanding is acquired through the consideration of variables that influence a company’s organic traffic and user engagement and their correlation to each other. A three-stage data-driven analysis is used to examine the correlation between the foregoing variables and to consider strategies that can be implemented to optimize organic traffic and user engagement. The first section gathers data from five airline companies’ websites and five crowdsourcing websites over an interval of 180 days. The second stage creates an exploratory diagnostic model, through Fuzzy Cognitive Mapping, to visually illustrate the cause-and-effect correlations between the examined metrics. Finally, a predictive micro-level agent-based model simulates optimization strategies that can be used to improve organic traffic and user engagement. The results of this study, reveal that crowdsourcing organic traffic increases airline websites’ user engagement through paid campaigns, while a limited correlation was found to exist between the average duration of a user to organic traffic. The results of this study provide tangible digital marketing strategies which can be used by airline companies to improve the influence of their digital marketing strategies on their users.

Communicating supply chain sustainability: transparency and framing effects

Purpose

Firms employ various forms of disclosure to demonstrate commitment to and involvement in sustainable supply chain management (SSCM) practices. This research provides guidance to firms employing framing strategies when communicating their SSCM with external stakeholders like consumers as part of their supply chain transparency efforts.

Design/methodology/approach

The authors employed a middle-range theorizing approach to understand the context of SSCM practices and mechanisms of variously framed communication methods to disclose sustainability information to consumers. The authors conducted two experiments in an e-waste recycling context, studying how sustainable information disclosed to consumers using attribute framing and goal framing can affect consumers' attitudes. The authors also examined the moderating role of consumers' environmental involvement.

Findings

Results suggest that when attribute framing is used, firms should avoid framing the attribute from a negative valence. When goal framing is used, messages with consequences stated as “avoid loss” yield the most substantial effect. Additionally, framing effects are more significant for consumers with higher-than-average environmental involvement.

Originality/value

The authors’ results contribute to the ongoing theorization of SSCM by providing contextual understanding of how to communicate sustainability information. Corroborating evidence from marketing, framing effects are found to be context specific, thereby elucidating the framing literature more fully to the SSCM context. The authors extend this literature by studying attribute framing and comparing the effectiveness of all possible goal framing combinations of valence and gain/loss perspective in the SSCM communication context.

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.

Enabling integrated business planning through big data analytics: a case study on sales and operations planning

Purpose

The purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.

Design/methodology/approach

The research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.

Findings

The results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.

Originality/value

This study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

Does digitalising the supply chain contribute to its resilience?

Purpose

Supply chain resilience (SCR) is a key concept for managers who wish to develop the capacity to enhance their supply chain’s (SC’s) ability to cope with unexpected turbulence. SC digital tools are often seen as a solution that provides more visibility, anticipation and collaboration (SCR capability factors). The purpose of this paper is to investigate the link between SCR and SC digitalisation

Design/methodology/approach

A sample was considered with 300 managers in the field of SCM, and the results were analysed using factor analysis and structural equation modelling (SEM). SEM was employed to test the impact of the degree of digital maturity and SC digital tools on SCR.

Findings

SC digitalization is characterised by the degree of digital maturity and the adoption of SC digital tools. The degree of digital maturity has a strong influence on digital tool adoption. SCR is positively impacted by both the degree of digital maturity and the adoption of digital tools.

Research limitations/implications

The findings do not indicate which tools contribute the most to SCR.

Practical implications

Managers should reflect on the need to continue digitalizing their SCs if they want greater SCR in the current uncertain environment.

Originality/value

This is the first quantitative study that focuses on assessing the impact of the degree of digital maturity and the SC digital tools adopted on SCR. Validation of the hypotheses model confirms the positive impact of SC digitalisation on SCR for researchers and managers.

What Is Required to Be a Data Scientist?

This study characterized required skills and competences for data specialist roles by analysing job advertisements for data scientists and other related professionals. It was performed using a content analysis technique named centring resonance analysis (CRA). With the support of this technique, demanded skills were grouped into categories that allow a better understanding of each role as well as differences and similarities among roles were observed and analysed. This study also summarized our findings in an orientation framework to classify six data specialists' roles according to business and technical skills as well as to experience and educational demands. Professional experience seems, in general, to be more valued than academic background. This work sheds light on better differentiating job roles related to data science, which could guide companies that recruit such specialists by better defining job requirements. For universities, these findings support the development of new analytics and data science programs.

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

Purpose

Designing knowledge management (KM) systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy.

Design/methodology/approach

A bibliographic coupling method is applied over a dataset of 128 peer-reviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm.

Findings

This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies and big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and KM and represents a ‘bridge’ between the other three clusters.

Research limitations/implications

Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions.

Originality/value

This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy.

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.

Stock visibility for retail using an RFID robot

Purpose

The combination of the latest advancements in information and communication technologies with the latest developments in AutoID technologies, especially radio frequency identification (RFID), brings the possibility of high-resolution, item-level visibility of the entire supply chain. In the particular case of retail, visibility of both the stock count and item location in the shop floor is crucial not only for an effective management of the retail supply chain but also for physical retail stores to compete with online retailers. The purpose of this paper is to propose an autonomous robot that can perform stock-taking using RFID for item-level identification much more accurately and efficiently than the traditional method of using human operators with RFID handheld readers.

Design/methodology/approach

This work follows the design science research methodology. The paper highlights a required improvement for an RFID inventory robot. The design hypothesis leads to a novel algorithm. Then the cycle of development and evaluation is iterated several times. Finally, conclusions are derived and a new basis for further development is provided.

Findings

An autonomous robot for stock-taking is proven feasible. By applying a proper navigation strategy, coupled to the stream of identifications, the accuracy, precision, consistency and time to complete stock-taking are significantly better than doing the same task manually.

Research limitations/implications

The main limitation of this work is the unavailability of data to analyze the actual impact on the correction of inventory record inaccuracy and its subsequent implications for the supply chain management. Nonetheless, it is shown that figures of actual stock-tacking procedures can be significantly improved.

Originality/value

This paper discloses the potential of deploying an inventory robot in the supply chain. The robot is called to be a key source of inventory data conforming supply chain management 4.0 and omnichannel retail.

Emerging procurement technology: data analytics and cognitive analytics

Purpose

The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?

Design/methodology/approach

This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.

Findings

The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.

Originality/value

While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.

The social process of Big Data and predictive analytics use for logistics and supply chain management

Purpose

The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations.

Design/methodology/approach

The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC.

Findings

Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations.

Practical implications

This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place.

Originality/value

The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area.

The impact of Big Data analytics and data security practices on service supply chain performance

Purpose

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

Design/methodology/approach

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

Findings

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

Practical implications

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Originality/value

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

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.

Understanding the value of big data in supply chain management and its business processes

Purpose

The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective.

Design/methodology/approach

A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles.

Findings

By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data.

Research limitations/implications

This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence.

Practical implications

Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data.

Originality/value

Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

Creation of unstructured big data from customer service

Purpose

Customer service provision is a growing phenomenon on social media and parcel shipping companies have been among the most prominent adopters. This has coincided with greater interest in the development of analysis techniques for unstructured big data from social media platforms, such as the micro-blogging platform, Twitter. Given the growing use of dedicated customer service accounts on Twitter, the purpose of this paper is to investigate the effectiveness with which parcel shipping companies use the platform.

Design/methodology/approach

This paper demonstrates the use of a combination of tools for retrieving, processing and analysing large volumes of customer service-related conversations generated between parcel shipping companies and their customers in Australia, UK and the USA. Extant studies using data from Twitter tend to focus on the contributions of individual entities and are unable to capture the insights provided by a holistic examination of the interactions.

Findings

This study identifies the key issues that trigger customer contact with parcel shipping companies on Twitter. It identifies similarities and differences in the approaches that these companies bring to customer engagement and identifies the opportunities for using the medium more effectively.

Originality/value

The development of consumer-centric supply chains and relevant theories require researchers and practitioners to have the ability to include insights from growing quantities of unstructured data gathered from consumer engagement. This study makes a methodological contribution by demonstrating the use of a set of tools to gather insight from a large volume of conversations on a social media platform.

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

Purpose

The purpose of this paper is to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM.

Design/methodology/approach

A survey on SCM levels of 1,000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent response rate.

Findings

The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM.

Research limitations/implications

This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America.

Practical implications

This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context.

Originality/value

This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.

Practitioners understanding of big data and its applications in supply chain management

Purpose

Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.

Design/methodology/approach

This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.

Findings

First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.

Research limitations/implications

The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.

Practical implications

The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.

Originality/value

This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.

Big data in spare parts supply chains

Purpose

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

Design/methodology/approach

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

Findings

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

Research limitations/implications

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

Practical implications

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

Originality/value

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.

Resource commitment and sustainability: a reverse logistics performance process model

Purpose

The purpose of this paper is to extend existing and motivate future sustainable supply chain management (SCM) and logistics research by examining a structure-conduct-performance framework linking resource commitment to sustainable SCM, reverse logistics, and operational performance. A sustainable reverse logistics capability is investigated as mediating the performance benefits associated with resource commitments to sustainable SCM.

Design/methodology/approach

Survey methods and structural equation modeling were used to collect and analyze data from 180 supply chain professionals.

Findings

The results of a mediated model suggest that resource commitments may be used to develop a sustainable reverse logistics capability, reducing the environmental impact of reverse logistics activities. A strong sustainable reverse logistics capability results from resources committed specifically to sustainable reverse logistics and a commitment to the sustainability of the supply chain.

Research limitations/implications

This study applied a purposefully general sampling procedure. Specific industries may have additional constraints (e.g. risk, transparency, governance factors) that directly impact reverse logistics. These constraints are limitations of the study as well as opportunities for future research. Resource commitment is critical to the success of an overall firm strategy to build a sustainable supply chain, especially when considering reverse logistics.

Practical implications

As managers examine the benefits of sustainable SCM, they must consider the resources required. For firms engaging in sustainable SCM, developing a sustainable reverse logistics capability is a key success factor for improved performance.

Originality/value

Given the growing acceptance and importance of sustainable SCM, this research provides insights to managers and academics regarding the key mediating role of a sustainable reverse logistics capability when integrated into existing and future supply chain research frameworks and processes.

How supply chain analytics enables operational supply chain transparency

Purpose

The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency.

Design/methodology/approach

Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression.

Findings

The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT.

Research limitations/implications

This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements.

Practical implications

The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities.

Originality/value

This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.