Supply chain dynamics, big data capability and product performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Canchu Lin ◽  
Anand Kunnathur ◽  
Jeffrey Forrest
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Knowledge Sharing ◽  
Relationship Building ◽  
Product Performance ◽  
Chain Dynamics ◽  
Chain Management ◽  
Content Type ◽  
Supply Chain Dynamics

PurposeThe purpose of this study is to examine big data capability's impact on product improvement and explore supply chain dynamics including relationship building and knowledge sharing as important contribution to big data capability.Design/methodology/approachThe research model is tested with survey data. Data analysis results empirically support the proposed model and the hypothesized relationships between the concepts.FindingsFirst, the hypothesis testing results of this study show that big data capability directly enhances product improvement. Second, this study shows that supply chain relationship building and knowledge sharing are positively related to the development of big data capability.Research limitations/implicationsIn supply chain management, there are multiple factors, besides relationship building, that serve as conditioners to knowledge sharing's effect on product performance. We only examined the role of relationship building in this area.Practical implicationsFindings from this research encourage firms to take advantage of their supply chain resources to develop a big data capability that positively contributes to firm performance.Originality/valueThe contribution lies in that it brings to light this step that connects big data capabilities and market and financial performance, which is missing in prior research. This study contributes to the literature by identifying supply chain management activities, more specifically, supply chain relationship building and knowledge sharing, as antecedents to big data capability. This helps to extend this emergent enterprise of big data research to a new area and points to new directions for future research.

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

2018 ◽  
Vol 38 (7) ◽  
pp. 1589-1614 ◽  
Author(s):  
Morten Brinch
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Process ◽  
Business Processes ◽  
Value Theory ◽  
Value Capture ◽  
Chain Management ◽  
Content Type ◽  
Process Perspective

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.

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

2019 ◽  
Vol 39 (6/7/8) ◽  
pp. 887-912 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation ◽  
Equation Modeling ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Supply Chain Agility ◽  
The Usa

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.

Antecedents and consequences of electronic supply chain management diffusion

2017 ◽  
Vol 28 (2) ◽  
pp. 699-718 ◽  
Author(s):  
Hsiu-Fen Lin
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Knowledge Sharing ◽  
Human Resource Development ◽  
Human Resource ◽  
Research Model ◽  
Chain Management ◽  
Content Type ◽  
Operational Capability ◽  
Antecedents And Consequences

Purpose Grounded in the resource-based view and social exchange theory, the purpose of this paper is to develop a research model that offers a comprehensive understanding of the antecedents and consequences of electronic supply chain management (e-SCM) diffusion. Design/methodology/approach Survey data from 142 managers (in charge of e-SCM projects in their companies) of large Taiwanese firms were collected and used to test the hypotheses using hierarchical moderated regression analysis. Findings The results indicate that information technology deployment capability, operational capability, human resource capability, and knowledge sharing are important antecedents of e-SCM diffusion. In turn, higher levels of e-SCM diffusion lead to greater competitive performance. This study also finds that knowledge sharing plays a moderating role by strengthening the relationship between organizational capabilities (e.g. operational capability and human resource capability) and e-SCM diffusion. Practical implications Managers should recognize that human resource development activities (recruiting, training, and managing valuable e-SCM personnel) are an important source of e-SCM diffusion. Similarly, managers must establish the connection between human resource capabilities and e-SCM diffusion (i.e. “soft-side” e-SCM) such as hiring and retaining skilled e-SCM personnel, training and development for e-SCM personnel, and measuring e-SCM personnel’s global mindset over time. Originality/value Theoretically, this study aims to provide a research model that is capable of understanding the antecedents and consequences of e-SCM diffusion. From the managerial perspective, the findings of this study provide valuable decision guides for practitioners to help them identify and develop firm internal capabilities and social mechanisms that foster e-SCM diffusion.

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

2019 ◽  
Vol 49 (7) ◽  
pp. 706-726 ◽  
Author(s):  
Annibal Sodero ◽  
Yao Henry Jin ◽  
Mark Barratt
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Technology Use ◽  
Predictive Analytics ◽  
Social Process ◽  
Theory Approach ◽  
Chain Management ◽  
Content Type ◽  
The Social

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.

How “smart cities” will change supply chain management

2015 ◽  
Vol 20 (3) ◽  
pp. 237-248 ◽  
Author(s):  
Elcio M. Tachizawa ◽  
María J. Alvarez-Gil ◽  
María J. Montes-Sancho
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Network Structure ◽  
Smart Cities ◽  
Supply Network ◽  
Supply Networks ◽  
Chain Management ◽  
Content Type ◽  
Integrative Framework

Purpose – The purpose of this paper is to analyze the impact of smart city initiatives and big data on supply chain management (SCM). More specifically, the connections between smart cities, big data and supply network characteristics (supply network structure and governance mechanisms) are investigated. Design/methodology/approach – An integrative framework is proposed, grounded on a literature review on smart cities, big data and supply networks. Then, the relationships between these constructs are analyzed, using the proposed integrative framework. Findings – Smart cities have different implications to network structure (complexity, density and centralization) and governance mechanisms (formal vs informal). Moreover, this work highlights and discusses the future research directions relating to smart cities and SCM. Research limitations/implications – The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM has been proposed. Practical implications – Smart cities and big data alone have limited capacity of improving SCM processes, but combined they can support improvement initiatives. Nevertheless, smart cities and big data can also suppose some novel obstacles to effective SCM. Originality/value – Several studies have analyzed information technology innovation adoption in supply chains, but, to the best of our knowledge, no study has focused on smart cities.

A global exploration of Big Data in the supply chain

2016 ◽  
Vol 46 (8) ◽  
pp. 710-739 ◽  
Author(s):  
Robert Glenn Richey ◽  
Tyler R. Morgan ◽  
Kristina Lindsey-Hall ◽  
Frank G. Adams
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Operations Management ◽  
Success Factors ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Key Success Factors ◽  
Starting Point

Purpose Journals in business logistics, operations management, supply chain management, and business strategy have initiated ongoing calls for Big Data research and its impact on research and practice. Currently, no extant research has defined the concept fully. The purpose of this paper is to develop an industry grounded definition of Big Data by canvassing supply chain managers across six nations. The supply chain setting defines Big Data as inclusive of four dimensions: volume, velocity, variety, and veracity. The study further extracts multiple concepts that are important to the future of supply chain relationship strategy and performance. These outcomes provide a starting point and extend a call for theoretically grounded and paradigm-breaking research on managing business-to-business relationships in the age of Big Data. Design/methodology/approach A native categories qualitative method commonly employed in sociology allows each executive respondent to provide rich, specific data. This approach reduces interviewer bias while examining 27 companies across six industrialized and industrializing nations. This is the first study in supply chain management and logistics (SCMLs) to use the native category approach. Findings This study defines Big Data by developing four supporting dimensions that inform and ground future SCMLs research; details ten key success factors/issues; and discusses extensive opportunities for future research. Research limitations/implications This study provides a central grounding of the term, dimensions, and issues related to Big Data in supply chain research. Practical implications Supply chain managers are provided with a peer-specific definition and unified dimensions of Big Data. The authors detail key success factors for strategic consideration. Finally, this study notes differences in relational priorities concerning these success factors across different markets, and points to future complexity in managing supply chain and logistics relationships. Originality/value There is currently no central grounding of the term, dimensions, and issues related to Big Data in supply chain research. For the first time, the authors address subjects related to how supply chain partners employ Big Data across the supply chain, uncover Big Data’s potential to influence supply chain performance, and detail the obstacles to developing Big Data’s potential. In addition, the study introduces the native category qualitative interview approach to SCMLs researchers.

The mediating role of blockchain technology in improvement of knowledge sharing for supply chain management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maryam philsoophian ◽  
Peyman Akhavan ◽  
Morteza Namvar
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Knowledge Sharing ◽  
Performance Metrics ◽  
Equation Modeling ◽  
Chain Management ◽  
Content Type ◽  
Know How ◽  
Business Partners ◽  
Blockchain Technology

PurposeSharing knowledge with business partners is a challenging issue as firms need to share their valuable know-how assets with individuals or other companies out of their organizational boundaries. As supply chain management (SCM) deals with various stakeholders, firms face difficulties with privacy and ownership when they share their know-how with suppliers or business partners. This study introduces blockchain technology as a mediator in improving knowledge sharing (KS) practices in supply chains.Design/methodology/approachThe data have been collected from surveys with 116 experts working in blockchain start-ups and organizations, and the authors used structural equation modeling for its analysis.FindingsThe results show that two features of blockchain technology, namely transparency and security, have the highest impacts on mediating knowledge sharing impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of SCM, speed is highly improved when blockchain technology is used for knowledge sharing. Their study provides guidance for managers on how to improve SCM performance through KS, which is empowered by a blockchain system.Originality/valueThe authors’ findings help organizations to improve supply chain actions, improve innovation, enhance competitive advantage and increase the speed of relationships in the supply chain. The research also contributes literature by analyzing the key factors showing how knowledge sharing structure may be improved by blockchain technology which would be helpful for both academics and practitioners.

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

2018 ◽  
Vol 29 (2) ◽  
pp. 555-574 ◽  
Author(s):  
Morten Brinch ◽  
Jan Stentoft ◽  
Jesper Kronborg Jensen ◽  
Christopher Rajkumar
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Delphi Study ◽  
Questionnaire Survey ◽  
Chain Management ◽  
Content Type ◽  
Process Framework ◽  
Planning Processes ◽  
Process Perspective

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.

Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management

2017 ◽  
Vol 37 (1) ◽  
pp. 10-36 ◽  
Author(s):  
Florian Kache ◽  
Stefan Seuring
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Empirical Research ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Environment ◽  
Special Focus ◽  
Chain Management ◽  
Content Type

Purpose Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM. Design/methodology/approach Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique. Findings Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective. Research limitations/implications These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM. Originality/value The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

A model for examining the effect of knowledge sharing and new IT-based technologies on the success of the supply chain management systems

Kybernetes ◽  
2019 ◽  
Vol 49 (2) ◽  
pp. 229-251 ◽  
Author(s):  
Habibeh Zeraati ◽  
Lila Rajabion ◽  
Homa Molavi ◽  
Nima Jafari Navimipour
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Knowledge Sharing ◽  
Radio Frequency Identification ◽  
Structural Equation ◽  
Near Field ◽  
Equation Modeling ◽  
Chain Management ◽  
Content Type ◽  
The Social

PurposeThis research specifies the factors impacting on the success of supply chain management (SCM) systems in the organizations. This paper aims to assess the effect of knowledge sharing, the vehicular ad hoc network (VANET), radio frequency identification technology (RFID) and near field communications (NFC) and the social capabilities of information technology (IT) and information and communication technology (ICT)on the success of the SCM systems and the simplification of the SCM challenges and other factors affecting its success.Design/methodology/approachA questionnaire is designed for measuring the elements of the proposed model. The questionnaires are revised by experts with experiences in SCM. For statistical analysis, SPSS 24.0 and SMART- PLS (partial least squares) 3.2.6 software package are used. The structural equation modeling (SEM) analysis procedure is conducted in two stages. The reliability analysis and confirmatory factor for analyzing the dimensions and items are included in the first stage. The second stage involves evaluating the assumptions through the SEM.FindingsThe results have depicted that four variables (knowledge sharing, VANET, RFID and NFC, and the social capabilities of using IT) affect the success of SCM systems.Originality/valueThis research specifies the factors impacting on the success of SCM in the organizations. These technologies aid companies in improving their performance in the SCM and facilitating coherence and collaboration.

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