An approach for analysing supply chain complexity drivers through interpretive structural modelling

Sujan Piya*, Ahm Shamsuzzoha, Mohammad Khadem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Today’s greater product variety, shorter product life cycle, and lower production costs are pushing companies to look beyond their own boundaries, thereby, creating complexity in the management of the supply chain. To manage such complexity, it is imperative that the management understand the associated complexity drivers and their interrelationships. This study identified twenty-three drivers responsible for supply chain complexity and classified them by using various criteria. In addition, the study presents a structural model using interpretive structural modelling (ISM) methodology to understand the inter-relationships between one driver to another. The research findings showed that drivers such as customer need, competitor action, and government regulation are beyond the control of supply chain partners, and have found the highest dominance with respect to supply chain complexity. Conversely, drivers related to tactical issues such as production planning and control, logistics and transportation, forecasting error, and marketing and sales are found to be the dependent drivers. Remaining drivers, such as company culture, number of suppliers, product variety, and organisational structure fall between the former two classifications. These drivers are related to strategic issues and require action from the upper level of the management hierarchy.

Original languageEnglish
Pages (from-to)311-336
Number of pages26
JournalInternational Journal of Logistics Research and Applications
Volume23
Issue number4
DOIs
Publication statusPublished - Jul 3 2020
Externally publishedYes

Keywords

  • complexity drivers
  • driver classification
  • interpretive structural modelling (ISM)
  • ISM digraph
  • Supply chain complexity

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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