Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 646

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Measuring supply chain performance: current research and future directions

Shepherd, Craig, Günter, Hannes·2006·International Journal of Productivity and Performance Management

Abstract

Purpose: This paper aims to go some way towards addressing the dearth of research into performance measurement systems and metrics of supply chains by critically reviewing the contemporary literature and suggesting possible avenues for future research. Design/methodology/approach: The article provides a taxonomy of performance measures followed by a critical evaluation of measurement systems designed to evaluate the performance of supply chains. Findings: The paper argues that despite considerable advances in the literature in recent years, a number of important problems have not yet received adequate attention, including: the factors influencing the successful implementation of performance measurement systems for supply chains; the forces shaping their evolution over time; and, the problem of their ongoing maintenance. Originality/value: The paper provides a taxonomy of measures and outlines specific implications for future research.

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 · 10.1108/17410400610653219

Cites

1 of 1 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

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

Show all 1 references

Cited by

See all 646

Metadata

Authors
Shepherd, C., & Günter, H.
Venue
International Journal of Productivity and Performance Management
Publisher
Emerald
Published
2006-04-01
Volume
55
Issue
3/4
Pages
242-258
ISSN
  • 0043-8022 (ISSN-L)
  • 1741-0401
OpenAlex
W2041968086
Cited by
646
References
1
FWCI
30.80
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: Measuring supply chain performance: current research and future directions. https://app.cochat.ai/papers/10.1108/17410400610653219
The paper
Shepherd, C., & Günter, H. (2006). Measuring supply chain performance: current research and future directions. International Journal of Productivity and Performance Management. https://doi.org/10.1108/17410400610653219

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.