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

Embracing supply chain digitalization and unphysicalization to enhance supply chain performance: a conceptual framework

Purpose

The paper presents a research methodology that could be used to carry out a systematic literature review on the current state of the art of the technological development in the field of the digitalization and unphysicalization of supply chains (SCs). A three-dimensional conceptual framework focusing on the relationship between Digital Technologies (DTs), business processes and SC performance is presented. The study identifies the emerging practices and areas of SC management that could be positively affected by the implementation of DTs. With this in mind, the emerging practices have a high probability to be considered future best practices.

Design/methodology/approach

A systematic literature review was conducted on DTs in SC management. The methodology used aims to algorithmically and objectively standardize the information incorporated into thousands of scientific documents. Selected papers were analyzed to investigate the recent literature on SC digitalization and unphysicalization. A total of 87 DTs were selected to be analyzed and subsequently grouped into 11 macro-categories. 17 business processes linked to SC management are taken into account and 17 different impacts on SC management are presented. From a set of 1,585 papers, 5,060 emerging practices were collected and singularly summarized combining DT, business process and impact on SC performance.

Findings

A unique analytical perspective provided represents an important evolution when trying to organize the current literature on SC management. The widely used DTs in the practices and the most considered business processes and impacts are highlighted and described. The three-dimensional conceptual framework is graphically represented to allow for the emergence of the best combinations of DT, business process and impact on SC performance. These combinations suggest the most promising areas for the implementation of the emerging practices for SC digitalization and unphysicalization. Additional findings identify and define the most important contexts in which Big Data contributes to SC performance.

Originality/value

The research methodology used is offering progress through which to systemize the current practices as well as detect the potential of digitalization and unphysicalization under the three-dimensional conceptual framework. The paper provides a structured proposal for promising future research directions, assuming that the five research gaps as findings of this research could be the basis for prescriptions, as well as a future research agenda and theory development. Moreover, this research contributes to current managerial issues concerning SC management, referred to data and information management, efficiency and productivity of SC processes, market performance, SC relationship management and risk management in SC.

Digitalization of Supply Chain Management with Industry 4.0 Enabling Technologies: A Sustainable Perspective

Supply chain management is one of the most prominent areas that needs to incorporate sustainability to achieve responsible consumption and production (SDG 11).It has been identified that there are limited studies that have presented the significance of different Industry 4.0 technologies from the perspective of sustainable SCM. The purpose of this study is to discuss the role of Industry 4.0 technologies in the context of sustainable SCM, as well as to identify important areas for future research. The PRISM framework is followed to discuss the role and significance of sustainable SCM and the integration of Industry 4.0-enabling technologies such as the Internet of Things (IoT), cloud computing, big data, artificial intelligence (AI), blockchain, and digital twin for sustainable SCM. The findings of the study reveal that there are limited empirical studies for developing countries and the majority are emphasized in case studies. Additionally, a few studies have focused on operational aspects, economics, and automation in SCM. The current study is able to contribute to the significance and application of IoT, cloud computing, big data, AI, blockchain, and digital twin in achieving sustainable SCM in the future. The current study can be expanded to discuss the Industry 4.0-enabling technologies in analyzing sustainability performance in any organization using environmental, social, and governance (ESG) metrics.

Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Purpose

Although big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?

Design/methodology/approach

The authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.

Findings

In the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.

Originality/value

The authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.

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.

Supply chain quality management 4.0: conceptual and maturity frameworks

Purpose

The research objective is rooted in the principle of providing new insights and a collective perspective regarded as Supply Chain Quality Management 4.0 (SCQM4.0), an integration of all three concepts – Industrie 4.0, quality management and supply chain management.

Design/methodology/approach

A thorough review of historical developments and existing integration trends among Industrie 4.0, quality and supply chain approaches along with future research directions outlined in the main literature, was conducted. This work establishes a knowledge base on research topics, issues of integration and synergies with a concentration on the potential for deeper integration with supply chain operations.

Findings

This article not only introduced the term SCQM4.0 and proposed a definition for it, but also contributed a novel conceptual SCQM4.0 framework and evolutionary perspective through the SCQM4.0 maturation model. Stemming from the gaps, opportunities and benefits identified in the literature, the conceptual SCQM4.0 framework builds on the high potential of the SCQM4.0 constructs to achieve successful governance and implementation. Under the SCQM4.0 maturity framework, it provides a clear evolutionary path underpinned by the SCQM4.0 constructs.

Research limitations/implications

In the effort toward a successful SCQM4.0 implementation, the proposed SCQM4.0 maturity frameworks will be a “road map” for businesses to develop fully and actively in supply chain operations, bringing quality products and services for the company. Industry practitioners are encouraged to perform gap analysis and direct the implementation of the strategy to establish an excellent SCQM4.0.

Originality/value

This is one of the pioneering studies integrating all three concepts (Industrie 4.0, quality management and supply chain management), connecting the link and discovering more synergies to support the future development of more holistic management models. SCQM4.0 is expected to expand on the strengths, synergies and established relationships between technologies 4.0, quality and supply chain, contributing toward a pioneering and quality supply chain.

Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership

Due to the COVID-19 pandemic, there is an unprecedented crisis for businesses. The small and medium enterprises (SMEs) have been impacted even more, due to their limited resources. Extant literature has prescribed many treatments on how SMEs could survive in post COVID-19 situation, but studies did not analyse how big data driven innovation could improve supply chain management (SCM) process in the post COVID-19 pandemic under the moderating influence of SME technology leadership support. Thus, there is a research gap in this important domain. The aim of this study is to examine the impact of big data driven innovation and technology capability of the SME on its supply chain system. The study also investigates the moderating role of SME technology leadership support on SME performance in the post COVID-19 scenario. With the help of literature and resource-based view (RBV) and dynamic capability view (DCV) theory, a theoretical model has been developed conceptually. Later the model is validated using structural equation modelling (SEM) technique with 327 usable respondents from SMEs from India. The study found that both big data driven innovation and the techno-functional capability of SME impacts supply chain capability which in turn impacts the SME performance in the post COVID-19 scenario. The study also finds that there will be a moderating impact of SME technology leadership support on SME performance.

A Review of Digital Transformation on Supply Chain Process Management Using Text Mining

Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external organizations and systems. This study presents a narrative review explaining the existing knowledge on digital transformation in supply chain process management using text mining. It summarizes the existing literature to explain the current state of the art in supply chain digitalization. This comprehensive review identifies the most important topics and technologies and determines the future trends in this emerging field. We investigate the articles published in Web of Science and Scopus databases and use text mining techniques (clustering and topic modeling) on the article contents. Using VOS viewer, a bibliometric analysis of 395 articles with 12,700 references is analyzed. The contents of the articles are explored using text mining approaches. The synthesized results reveal that the most important topics in digital transformation are “sustainable supply chain management” and “circular economy and industry 4.0 technologies”. The study further discovers big data, data analytics, blockchain, artificial intelligence, machine learning, and the Internet of Things as the most critical technologies for facilitating supply chain digital transformation. Finally, an overlay heatmap analysis of the research articles found that digital transformation, supply chain management, industry 4.0, decision-making, and sustainability are emerging trends in supply chain digitalization.

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.

Data analytics capability and servitization: the moderated mediation role of bricolage and innovation orientation

Purpose

Despite the potential influence of data analytics capability on servitization, the understanding of the underlying mechanisms of this influence remains unclear. This study aims to explore how data analytics capability affects servitization by examining the mediation effect of bricolage and the conditional role of innovation orientation.

Design/methodology/approach

This study employs the moderated mediation method to examine the proposed research model with archival data and multiple-respondent surveys from 1,206 top managers of 402 manufacturing firms in the Yangtze River Delta area in China.

Findings

Bricolage partially mediates the positive relationship between data analytics capability and servitization, and innovation orientation positively moderates this effect.

Practical implications

Manufacturers can leverage bricolage to materialize data analytics capability for servitization. Manufacturers should also pursue an innovation orientation to fully glean the benefits of bricolage in transforming data analytics capability into servitization.

Originality/value

This study opens the black box of how data analytics capability affects servitization by revealing the underlying mechanism of bricolage and the boundary condition role of innovation orientation for this mechanism. It offers valuable insights for practitioners to leverage data analytics to improve servitization through developing bricolage and cultivating a culture of innovation orientation.

Business Intelligence and Business Value in Organisations: A Systematic Literature Review

Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.

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.

Omni-channel integration: the matter of information and digital technology

Purpose

This paper aims to explore how omni-channel data flows should be integrated by specifying what data, omni-channel agents and information and digital technologies (IDTs) should be considered and connected.

Design/methodology/approach

A multiple case study method is used with 17 British companies. The studies are supported by 68 interviews with the case companies and their consumers, 5 site visits, 4 focus group meetings and the companies’ archival data and documentations.

Findings

This paper provides novel frameworks for omni-channel data flow integration from consumer and business perspectives. The frameworks consist of omni-channel agents, their data transactions and their supporting IDTs. Relatedly, this paper formalizes the omni-channel data flow integration in the forms of horizontal, vertical and total integrations and explores their contributions to the adaptability of omni-channel, as a complex adaptive system (CAS). It also discusses that how inter-organizational governance mechanisms can support data flow integration and their relevant IDT implementations.

Research limitations/implications

The breadth and depth of the required IDTs for omni-channel integration prove the necessity for omni-channel systems to move toward total integration. Therefore, supported by CAS and inter-organizational governance theories, this research indicates how data flow integration and IDT can transform the omni-channel through self-organization and autonomy capability enhancement.

Originality/value

This research’s recommended frameworks provide a robust platform to formalize data flow integration as the omni-channel's core driver. Accordingly, it moves the literature from a basic description of “what omni-channel is” and provides a novel and significant debate on what specific data should be shared at what levels between which agents of the omni-channel, and with what type of relationship governance mechanism, to assure omni-channel horizontal, vertical and total integrations.

Managing information asymmetry in public–private relationships undergoing a digital transformation: the role of contractual and relational governance

Purpose

Inter-organisational governance is an important enabler for information processing, particularly in relationships undergoing digital transformation (DT) where partners depend on each other for information in decision-making. Based on information processing theory (IPT), the authors theoretically and empirically investigate how governance mechanisms address information asymmetry (uncertainty and equivocality) arising in capturing, sharing and interpreting information generated by digital technologies.

Design/methodology/approach

IPT is applied to four cases of public–private relationships in the Dutch infrastructure sector that aim to enhance the quantity and quality of information-based decision-making by implementing digital technologies. The investigated relationships are characterised by differing degrees and types of information uncertainty and equivocality. The authors build on rich data sets including archival data, observations, contract documents and interviews.

Findings

Addressing information uncertainty requires invoking contractual control and coordination. Contract clauses should be precise and incentive schemes functional in terms of information requirements. Information equivocality is best addressed by using relational governance. Identifying information requirements and reducing information uncertainty are a prerequisite for the transformation activities that organisations perform to reduce information equivocality.

Practical implications

The study offers insights into the roles of both governance mechanisms in managing information asymmetry in public–private relationships. The study uncovers key activities for gathering, sharing and transforming information when using digital technologies.

Originality/value

This study draws on IPT to study public–private relationships undergoing DT. The study links contractual control and coordination as well as relational governance mechanisms to information-processing activities that organisations deploy to reduce information uncertainty and equivocality.

Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach

Purpose

Over the years, technology development has rationalized supply chain processes. The demand economy is disrupting every sector causing the supply chain to be more innovative than ever before. The digitalization of the supply chain fulfils this demand. Several technologies such as blockchain, big data analytics, 3D printing, Internet of things (IoT), artificial intelligence (AI), augmented reality (AR), etc. have been innovated in recent years, which expedite the digitalization of the supply chain. The paper aims to analyse the applicability of these technological enablers in the digital transformation of the supply chain and to present an interpretive structural modelling (ISM) model, which presents a sequence in which enablers can be implemented in a sequential manner.

Design/methodology/approach

This paper employed the ISM approach to propose a various levelled model for the enablers of the digital supply chain. The enablers are also classified graphically based on their driving and dependence powers using matrix multiplication cross-impact applied to classification (MICMAC) analysis.

Findings

The study indicates that the enablers “big data analytics”, “IoT”, “blockchain” and “AI” are the most powerful enablers for the digitalization of the supply chain and actualizing these enablers should be a topmost concern for organizations, which want to exploit new opportunities created by these technologies.

Practical implications

This study presents a systematic approach to adopt new technologies for performing various supply chain activities and assists the policymakers better organize their assets and execution endeavours towards digitalization of the supply chain.

Originality/value

This is one of the initial research studies, which has analysed the enablers for the digitalization supply chain using the ISM approach.

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.

Supply chain sustainability: learning from the COVID-19 pandemic

Purpose

This paper, a pathway, aims to provide research guidance for investigating sustainability in supply chains in a post-COVID-19 environment.

Design/methodology/approach

Published literature, personal research experience, insights from virtual open forums and practitioner interviews inform this study.

Findings

COVID-19 pandemic events and responses are unprecedented to modern operations and supply chains. Scholars and practitioners seek to make sense of how this event will make us revisit basic scholarly notions and ontology. Sustainability implications exist. Short-term environmental sustainability gains occur, while long-term effects are still uncertain and require research. Sustainability and resilience are complements and jointly require investigation.

Research limitations/implications

The COVID-19 crisis is emerging and evolving. It is not clear whether short-term changes and responses will result in a new “normal.” Adjustment to current theories or new theoretical developments may be necessary. This pathway article only starts the conservation – many additional sustainability issues do arise and cannot be covered in one essay.

Practical implications

Organizations have faced a major shock during this crisis. Environmental sustainability practices can help organizations manage in this and future competitive contexts.

Social implications

Broad economic, operational, social and ecological-environmental sustainability implications are included – although the focus is on environmental sustainability. Emergent organizational, consumer, policy and supply chain behaviors are identified.

Originality/value

The authors take an operations and supply chain environmental sustainability perspective to COVID-19 pandemic implications; with sustainable representing the triple bottom-line dimensions of environmental, social and economic sustainability; with a special focus on environmental sustainability. Substantial open questions for investigation are identified. This paper sets the stage for research requiring rethinking of some previous tenets and ontologies.

Effect of information technology-enabled supply chain integration on firm's operational performance

Purpose

Using the assumptions of the resource-based view, relational view and swift, even flow theories and the overarching principles of supply chain management, the study aims to test the role of information technology (IT) capability (cross-functional application, supply chain application and data consistency) in enabling supply chain integration (SCI; internal, customer and supplier integration) and the impact of SCI on firm's operational performance in terms of quality, delivery, production cost, inventory level, customer service and product-mix flexibility.

Design/methodology/approach

The structural equation modeling approach is used to test theoretical predictions underlying the relationship among dimensions of IT capability, SCI and operational performance based on data obtained from senior executives of 108 large manufacturing firms listed in the Tokyo Stock Exchange.

Findings

The results suggest that IT capability has positive impact on SCI, except for data consistency, which is found to have negative impact on internal integration. The results further indicate that SCI, especially customer integration, has positive and significant impact on all operational performance indicators.

Practical implications

The findings inform future initiatives associated with the SCI improvement via specific IT capabilities. When undertaking such initiatives, managers are advised to consider the differential impact of the following IT capabilities on SCI: cross-functional applications, supply chain applications, and data consistency capability.

Originality/value

The study makes an empirical contribution to the body of knowledge by demonstrating the value of the multidimensional representation and analysis of IT capability, SCI, and operational performance given a differential and even opposed influence by some of the dimensions in specific business contexts.

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.

Digitization of manufacturing: the role of external search

Purpose

Manufacturers seek to innovate and improve processes using new digital technologies. However, knowledge about these new technologies often resides outside a firm's boundaries. The authors draw on the concept of absorptive capacity and the literature on open innovation to explore the role of external search in the digitization of manufacturing.

Design/methodology/approach

The authors developed and distributed a survey to manufacturing firms in Switzerland, for which 151 complete responses were received from senior managers. The authors used multiple linear regressions to study the relations among the breadth and depth of external search, firms' adoption of digital technologies and operational performance outcomes.

Findings

External search depth was found to relate positively to higher adoption of computing technologies and shop floor connectivity technologies. No significant correlation was found between external search breadth and firms' adoption of digital technologies. Regarding performance outcomes, there is some evidence that increased adoption of digital technologies relates positively to higher volume flexibility, but not to increased production cost competitiveness.

Practical implications

Manufacturing firms that aim to digitize their processes can benefit from inbound open process innovation, but its utility varies for different clusters of digital technologies. Generally, the findings suggest that firms should build strong ties with a few external knowledge partners rather than surface relations with many.

Originality/value

This study contributes to the growing literature on the digitization of manufacturing with an analysis of the relation between firms' external search and their adoption of digital technologies. It adds early empirical insights to the literature on open process innovation.

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.

Understanding supply chain analytics capabilities and agility for data-rich environments

Purpose

Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator.

Design/methodology/approach

The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model.

Findings

The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented.

Originality/value

The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

Operations strategy of cloud-based firms: achieving firm growth in the Big Data era

Purpose

Cloud computing is a major enabling technology for Industry 4.0 and the Big Data era. However, cloud-based firms, who establish their businesses on cloud platforms, have received scant attention in the extant operations management (OM) literature. To narrow this gap, the purpose of this paper is to investigate cloud-based firms from an operations strategy perspective.

Design/methodology/approach

A two-phase multi-method approach was adopted. In the first phase, content analysis of 27 reports from cloud-based firms was conducted, aided by text mining keyword extraction. Two data-related operations capabilities were identified and hypotheses were posited regarding the relationships between data resources (DR), operations capabilities and firm growth (FG). In the second phase, a sample of 190 cloud-based firms was collected. Seemingly unrelated regression and bootstrapping method were employed to test the proposed hypotheses using the survey data.

Findings

The content analysis indicates data as a key resource and both data processing capability and data transformational capability as critical operations capabilities of cloud-based firms. FG is regarded as a top priority in the cloud context. The regression results indicate that DR and the two capabilities contribute to the growth of cloud-based firms. Moreover, a follow-up bootstrapping analysis reveals that the mediating effects of the two capabilities vary between different types of FG.

Originality/value

To the authors’ best knowledge, this is one of the first OM studies on cloud-based firms. This study extends the operations strategy literature by identifying and testing the key operations capabilities and priorities of cloud-based firms. It also provides insightful implications for industrial practitioners.

Building supply chain risk resilience

Purpose

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

Design/methodology/approach

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

Findings

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

Originality/value

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