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Cited by 518

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Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach

Meade, L. M., Sarkis, J.·1999·International Journal of Production Research

Abstract

The objective of this paper is to introduce a decision methodology and structure for manufacturing (and organizational) agility improvement. The methodology allows for the evaluation of alternatives (e.g. projects) to help organizations become more agile, with a specific objective of improving the manufacturing business processes. An agile enterprise is one whose processes are designed to respond effectively to unanticipated change. One of the difficulties in designing and analysing business processes, in general, is that they are operational designs that need to incorporate strategic attributes. In order to evaluate alternatives that impact the business processes, a networked hierarchical analysis model based on the various characteristics of agility, is proposed. This evaluation model will be based on the analytic network process methodology for solving complex and systemic decisions. An actual example of a small manufacturing enterprise provides some managerial insights into the methodology.

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Metadata

Authors
Meade, L. M., & Sarkis, J.
Venue
International Journal of Production Research
Published
1999-02
Volume
37
Issue
2
Pages
241-261
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W2041240465
Cited by
518
References
0
FWCI
10.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach. https://app.cochat.ai/papers/10.1080/002075499191751
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Meade, L. M., & Sarkis, J. (1999). Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach. International Journal of Production Research. https://doi.org/10.1080/002075499191751

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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.