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.

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.

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.

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.

An unstructured big data approach for country logistics performance assessment in global supply chains

2020 ◽  
Vol 40 (4) ◽  
pp. 439-458 ◽  
Author(s):  
Aseem Kinra ◽  
Kim Sundtoft Hald ◽  
Raghava Rao Mukkamala ◽  
Ravi Vatrapu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Performance Assessment ◽  
Performance Management ◽  
Design Science ◽  
Text Corpus ◽  
Chain Management ◽  
Content Type ◽  
Text Corpora

PurposeThe purpose of this study is to explore the potential for the development of a country logistics performance assessment approach based upon textual big data analytics.Design/methodology/approachThe study employs design science principles. Data were collected using the Global Perspectives text corpus that describes the logistics systems of 20 countries from 2006–2014. The extracted texts were processed and analysed using text analytic techniques, and domain experts were employed for training and developing the approach.FindingsThe developed approach is able to generate results in the form of logistics performance assessments. It contributes towards the development of more informed weights of the different country logistics performance categories. That said, a larger text corpus and iterative classifier training is required to produce a more robust approach for benchmarking and ranking.Practical implicationsWhen successfully developed and implemented, the developed approach can be used by managers and government bodies, such as the World Bank and its stakeholders, to complement the Logistics Performance Index (LPI).Originality/valueA new and unconventional approach for logistics system performance assessment is explored. A new potential for textual big data analytic applications in supply chain management is demonstrated. A contribution to performance management in operations and supply chain management is made by demonstrating how domain-specific text corpora can be transformed into an important source of performance information.

The Future of Big Data Analytics in Supply Chain Management: Results from a Delphi Study

2017 ◽  
Vol 2017 (1) ◽  
pp. 12100
Author(s):  
Bernhard Roẞmann ◽  
Angelo Canzaniello ◽  
Heiko Von Der Gracht ◽  
Evi Hartmann
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Delphi Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Chain Management ◽  
The Future

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.

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