Big data and predictive analytics for supply chain and organizational performance

By Angappa Gunasekaran, Thanos Papadopoulos, Rameshwar Dubey, Samuel Fosso Wamba, Stephen J. Childe, Benjamin T. Hazen, Shahriar Akter. 2017

1,040 citations recorded by the source corpus.

Subfields: Management Information Systems, Marketing, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/ca68d86366c38eef8451917197191093d18865a6

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Citations in the corpus

Impact of Industry 4.0 on supply chain performance (2020, background): “This, subsequently, can improve order acceptance decision making and allow for better planning throughout the supply chain (Gunasekaran et al. 2017; Schoenherr and Speier-Pero 2015).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “…al. (2015), Addo-Tenkorang and Helo (2016), Richey et al. (2016), Oesterreich and Teuteberg (2016), Küpper et al. (2016), Schrauf and Berttram (2016), Gunasekaran et al. (2016, 2017), Nguyen et al. (2017), Moghaddam and Nof (2017), Hofmann and Rüsch (2017), Choi, Wallace, and Wang (2018),…”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “Recent studies have called for a better understanding of the claimed positive relation between big data and innovation success (Gunasekaran et al., 2017; Johnson et al., 2017).”
The adoption of digital technologies in supply chains: Drivers, process and impact (2021, methodology): “Some studies have used survey method to empirically test the relationships by measuring the operational efficiency related indicators (Chavez et al., 2017; Chen et al., 2015; Gunasekaran et al., 2017; Ranganathan et al., 2011).”
Role of big data analytics in developing sustainable capabilities (2019, background): “Such practice involves three stages consisting of big data acceptance, assimilation and routinization (Wang et al., 2018; Gunasekaran et al., 2017; Hazen et al., 2012).”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, methodology): “The number of networked sensors used in automotive, retail and transportation have increased at the rate of 30% per year (Manyika et al., 2011), with the perception that the sensor-based technology could substantially reduce the operational cost by 10–25% (Hahn and Packowski, 2015).”
Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018, background): “Gunasekaran et al. (2017) also confirmed that big data analytics improved supply chain efficiencies, quicker response to changes, enhance relationship and planning capabilities.”
Big data analytics capability in supply chain agility (2019, positive): “Supply chain visibility and big data analytics capabilities are complementary, in the sense that each supports the other (Gunasekaran et al. 2017).”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “The main reasons for low uptake are the lack of understanding of how it can be implemented, the inability to identify suitable data (Schoenherr and Speier-Pero, 2015), low acceptance, routinization and assimilation of BDA by organizations and SC partners (Gunasekaran et al., 2017), and data security”
Achieving superior organizational performance via big data predictive analytics: A dynamic capability view (2019, background): “leading to higher organizational performance (Gunasekaran et al., 2017; Schoenherr & Speier-Pero, 2015; Waller & Fawcett, 2013).”
Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience (2019, background): “To reduce information lead times and to improve the reliability of the information, the organizations need supporting infrastructure and processes that enable them to quickly acquire, process and analyse big data (Hazen et al. 2014; Gunasekaran et al. 2017).”