| Description |
1 online resource |
| Series |
International journal of operations & production management ; volume 37, number 1 |
|
International journal of operations & production management ; v. 37, no. 1
|
| Contents |
Cover ; Editorial board ; Guest editorial ; Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management; Making sense of Big Data -- can it transform operations management? ; Big data breaches and customer compensation strategies; Exploring the influence of big data on city transport operations: a Markovian approach ; Role of social media in retail network operations and marketing to enhance customer satisfaction |
| Summary |
Big data analytics (BDA) is defined as a holistic approach to managing, processing and analyzing the 5V data-related dimensions (i.e., volume, variety, velocity, veracity and value) to create actionable insights for delivering sustained value, measuring performance and establishing competitive advantages (Fosso Wamba et al., 2015). BDA has captured the imagination of both practitioners and scholars for its high operational and strategic potentials across various industries including marketing, financial services, insurance, retailing, healthcare, and manufacturing. For example, manufacturing firms including GE, Rolls Royce and Ford have been successfully using BDA for maintenance (e.g., engine failures) and supplier risk management (Jobs et al., 2015). BDA has also improved business intelligence on the behaviour of customers as well as consumer profiling (European Commission, 2013). As such, the extant literature identifies big data as the "next big thing in innovation" (Gobble, 2013, p.64), "the fourth paradigm of science" (Strawn, (2012), or "the next frontier for innovation, competition, and productivity" (Manyika et al., 2011, p.1). The papers comprise seven standalone research articles. Five articles are published in this issue: Kache and Seuring (2017), Matthias et al. (2017), Sykes et al. (2017), Mehmood et al. (2017), and Ramanathan et al. (2017). The remaining articles are published in IJOPM regular issues and are Aloysius et al. (2016), and Chong et al. (2016) |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed March 8, 2017) |
| Subject |
Big data.
|
|
Decision making -- Data processing
|
|
BUSINESS & ECONOMICS -- Industrial Management.
|
|
BUSINESS & ECONOMICS -- Management.
|
|
BUSINESS & ECONOMICS -- Management Science.
|
|
BUSINESS & ECONOMICS -- Organizational Behavior.
|
|
Big data
|
|
Decision making -- Data processing
|
| Form |
Electronic book
|
| Author |
Wamba, Samuel Fosso, editor
|
| ISBN |
9781787142855 |
|
178714285X |
|
