CoChat
Sign Up
Cited by 102Big Data and Business IntelligenceImpact of AI and Big Data on Business and SocietyBig Data Technologies and Applications

The DOI matches its Crossref record; 8 of the 9 references it cites were found in a registry or an index.

Big data and analytics in operations and supply chain management: managerial aspects and practical challenges

Open access
Papadopoulos, Thanos, Gunasekaran, Angappa, Dubey, Rameshwar, Fosso Wamba, Samuel·2017·Production Planning & Control
DOI 10.1080/09537287.2017.1336795
Open this paper in CoChat — free accountOpen PDF

Abstract

‘Big Data & Analytics’ (BD&A) has become increasingly important over the last years in both academic and practitioner worlds. Organisations are trying to harness the benefits of BD&A, defined as ‘a...

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/09537287.2017.1336795

Cites

8 of 8 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Challenges such as data quality, privacy concerns, and the need for analytical expertise need to be addressed for successful implementation of big data.

    “However, challenges such as data quality, privacy concerns, and the need for analytical expertise need to be addressed for successful implementation [151].”

    From Transformative Procurement Trends: Integrating Industry 4.0 Technologies for Enhanced Procurement Processes (2023) · cites this paper as [151]

    The Introduction (page unavailable) identifies data quality as part of big-data veracity, and §2 describes inaccurate or unstable data as a challenge in one big-data application. Section 2 also mentions expert involvement in a specific analytical framework, but it does not establish a general need for analytical expertise in implementation. The cited passages do not address privacy concerns; their discussion of security concerns relates specifically to cloud-computing providers. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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

Cites

Flagged first, then by citations

1 of the 9 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. Davenport T. H., 2012, MIT Sloan Management Review, 54, 43
  2. Langley J. C. J., 2014, Third-party Logistics Study: The State of Logistics Outsourcing
  3. McAfee A., 2012, Harvard Business Review October, 61
  4. Perrey J., 2013, McKinsey Quarterly
  5. 10.1002/bse.v17:8 · DOI

Cited by

Sorted by citations

Related

Metadata

Authors
Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S.
Affiliations
  • Kent Business School, University of Kent, Kent, UK
  • University of Massachusetts Dartmouth (US)
  • MBS School of Business (FR)
  • Toulouse Business School, Toulouse University, Toulouse, France
ORCID
Venue
Production Planning & Control
Publisher
Informa UK Limited
Published
2017-07-11
Volume
28
Issue
11-12
Pages
873-876
ISSN
  • 0953-7287 (ISSN-L)
  • 1366-5871
DOI
10.1080/09537287.2017.1336795
OpenAlex
W2734532144
Cited by
100
References
9
Citations by year
2026: 3 · 2025: 11 · 2024: 15 · 2023: 16 · 2022: 15 · 2021: 13 · 2020: 15 · 2019: 3 · 2018: 7 · 2017: 2
FWCI
12.62
Type
journal article
Language
en
Open access
Locations
  • Production Planning & Control (publishedVersion)
  • Kent Academic Repository (University of Kent) (submittedVersion)
  • HAL (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
big data, data analytics, operations management, supply chain management, managerial aspects, practical problems, data-driven decision making

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