Integrated analytical hierarchy process and grey relational analysis approach to measure supply chain complexity

Abstract

Purpose

The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).

Design/methodology/approach

Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.

Findings

The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.

Originality/value

In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

Citation

Piya, S., Shamsuzzoha, A., Khadem, M. and Al Kindi, M. (2021), "Integrated analytical hierarchy process and grey relational analysis approach to measure supply chain complexity", Benchmarking: An International Journal, Vol. 28 No. 4, pp. 1273-1295. https://doi.org/10.1108/BIJ-03-2020-0108