Shukla, V. and Naim, Mohamed Mohamed  ORCID: https://orcid.org/0000-0003-3361-9400
2017.
Detecting disturbances in supply chains - the case of capacity constraints.
International Journal of Logistics Management
28
(2)
, pp. 398-416.
10.1108/IJLM-12-2015-0223
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Abstract
Purpose The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. The purpose of this paper is to demonstrate the feasibility of automatically and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon. Design/methodology/approach Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature-based technique. Findings The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra. Originality/value Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Business (Including Economics) Research Institutes & Centres > Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC) |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor |
| Uncontrolled Keywords: | Supply chain risk, Clustering, Disturbance detection, Capacity constraint |
| Publisher: | Emerald Group Publishing Limited |
| ISSN: | 0957-4093 |
| Date of First Compliant Deposit: | 25 April 2016 |
| Date of Acceptance: | 17 April 2016 |
| Last Modified: | 23 Nov 2024 11:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/89945 |
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