Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Detecting disturbances in supply chains - the case of capacity constraints

Shukla, V. and Naim, Mohamed Mohamed 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

[img]
Preview
PDF - Accepted Post-Print Version
Download (466kB) | Preview

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: Business (Including Economics)
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: 01 Jul 2019 08:05
URI: http://orca.cf.ac.uk/id/eprint/89945

Citation Data

Cited 4 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics