How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study

Wamba, Samuel Fosso; Akter, Shahriar; Edwards, Andrew; Chopin, Geoffrey; Gnanzou, Denis

doi:10.1016/j.ijpe.2014.12.031

Cited by 1,209 publications

(580 citation statements)

References 57 publications

Supporting

Mentioning

459

Contrasting

Unclassified

Order By: Relevance

“…Current studies on the application of BDBA within LSCM -that we define in this study as supply chain analytics (SCA)-have focused mainly on analyzing definitions and different perspectives (F o s s o Wamba et al, 2015), or identifying opportunities for supply chain research and education (Waller and Fawcett, 2013). However, these scholars emphasise that BDBA is still in its infancy and there are yet studies to explore BDBA in LSCM.…”

Section: Introductionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,076

750

View full text Add to dashboard Cite

The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) -that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

show abstract

Section: Introductionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,076

750

View full text Add to dashboard Cite

show abstract

“…BD is much more than data analytics (Fosso Wamba et al, 2015), due to five key features: volume, velocity and variety (McAfee & Brynjolfsson, 2012), later expanded with value (due to the importance of extracting value from the available data) (Leventhal, 2013) and veracity (in order to underline the importance of quality data and the chance to trust them) (White, 2012).…”

Section: Introductionmentioning

confidence: 99%

Exploring the inbound and outbound strategies enabled by user generated big data: Evidence from leading smartphone applications

et al. 2017

View full text Add to dashboard Cite

The Open Innovation paradigm has been increasingly considered as a relevant approach to innovation. Among the different sources, the end users are particularly meaningful. Scholars highlighted several methods and strategies to involve them in the innovation process by asking, observing and giving them the chance to actually co-create. Digital technologies are expanding the span of opportunities in this direction, gathering a huge amount and variety of data while the end user enjoy a digital product, these data can be named as User Generated Big Data (UGBD). The aim of this research is to understand whether UGBD can contribute in User Innovation and to highlight the enabled strategies to create value through them.Leveraging on a multiple case study (Twitter, Spotify, Strava and Deliveroo), the paper first classify UGBD among the methods to foster User Centered Innovation, second it defines two strategies to create value relying on UGBD. First, companies can leverage on a Using Data strategy -addressing both the end user or other player in the ecosystem -fostering service innovation through an inbound approach. Second, a Selling data strategy can be pursued, addressing new clients and fostering business model innovation, enlarging the company's value chain in an outbound perspective.

show abstract

“…These new technologies, linked with the cloud computing is creating a hyperconnected network of devices affordable by anyone (Harris et al, 2015). Big Data aspects, described by Wamba et al (2015) as a combination of data Volume, Velocity, Variety, Veracity and Value were a limitation to extract added value from these data-collection. Considering how important and relevant must be considered the ability to use these data to support the decision making process in real time, we can expect in a near future that this kind of technologies, accessible through Software as a Service model will spread in the whole supply chain.…”

Section: The Upcoming Logistic Environmentmentioning

confidence: 99%

Categorisation of the Main Disruptive Events in the Sensitive Products Transportation Supply Chains

Schoen

Sanchís

Poler

et al. 2018

Int. J. Prod. Manag. Eng.

View full text Add to dashboard Cite

Abstract:The upcoming logistic environment is about to modify deeply the way we supply products. In fact, some new trends are going to require more and more agility between a large number of stakeholders in open and dynamic networks. This should be possible to achieve thanks to new data collection and treatment abilities. Considering this moving technological and logistic environment, it appears necessary to define and categorize more specifically the main disruptive events that can affect a supply chain. In fact, amount of data are collected on the field and must be helpful to make relevant decisions in case of disruption. In order to understand automatically what these data mean, it is necessary to detect and classify the disruptive events in order to find the best adaptation. This paper focuses on the sensitive products' supply chains, that are facing with agility high requirements, based on their ability to detect disruptive events. We take as an example the blood supply chain.

show abstract

How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study

Cited by 1,209 publications

References 57 publications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Exploring the inbound and outbound strategies enabled by user generated big data: Evidence from leading smartphone applications

Categorisation of the Main Disruptive Events in the Sensitive Products Transportation Supply Chains

Contact Info

Product

Resources

About