The DOI matches its Crossref record.
How to Use Big Data to Drive Your Supply Chain
Abstract
Big data analytics has become an imperative for business leaders across every industry sector. Analytics applications that can deliver a competitive advantage appear all along the supply chain decision spectrum—from targeted location-based marketing to optimizing supply chain inventories to enabling supplier risk assessment. While many companies have used it to extract new insights and create new forms of value, other companies have yet to leverage big data to transform their supply chain operations. This article examines how leading companies use big data analytics to drive their supply chains and offers a framework for implementation based on lessons learned.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Big data analytics in supply chain management between 2010 and 2016: Insights to industries
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.11.017 · Cited by 582
Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2017.04.001 · Cited by 535
Spieske, A., & Birkel, H. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107452 · Cited by 499
Akter, S., Fosso Wamba, S., & Dewan, S. (2017) · Production Planning & Control · doi:10.1080/09537287.2016.1267411 · Cited by 478
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023) · Annals of Operations Research · doi:10.1007/s10479-022-04983-y · Cited by 409
Metadata
- Authors
- Sanders, N. R.
- Venue
- California Management Review
- Publisher
- SAGE Publications
- Published
- 2016-05
- Volume
- 58
- Issue
- 3
- Pages
- 26-48
- ISSN
- 0008-1256 (ISSN-L)
- 2162-8564
- OpenAlex
- W2354065779
- Cited by
- 226
- References
- 0
- FWCI
- 38.00
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: How to Use Big Data to Drive Your Supply Chain. https://app.cochat.ai/papers/10.1525/cmr.2016.58.3.26
- The paper
- Sanders, N. R. (2016). How to Use Big Data to Drive Your Supply Chain. California Management Review. https://doi.org/10.1525/cmr.2016.58.3.26
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
