Analytical target cascading for multi-level supply chain decisions in cloud perspective

Abstract

Purpose

The purpose of this paper is to adopt a three-stage cloud-based management system for optimizing greenhouse gases (GHG) emission and marketing decisions with supplier selection and product family design in a multi-level supply chain with multiple suppliers, one single manufacturer and multiple retailers.

Design/methodology/approach

The manufacturer purchases optional components of a certain functionality from his alternative suppliers and customizes a set of platform products for retailers in different independent market segments. To tackle the studied problem, a hierarchical analytical target cascading (ATC) model is proposed, Jaya algorithm is applied and supplier selection and product family design are implemented in its encoding procedure.

Findings

A case study is used to verify the effectiveness of the ATC model in solving the optimization problem and the corresponding algorithm. It has shown that the ATC model can not only obtain close optimization results as a central optimization method but also maintain the autonomous decision rights of different supply chain members.

Originality/value

This paper first develops a three-stage cloud-based management system to optimize GHG emission, marketing decisions, supplier selection and product family design in a multi-level supply chain. Then, the ATC model is proposed to obtain the close optimization results as central optimization method and also maintain the autonomous decision rights of different supply chain members.

Citation

Huang, Y., Gao, K., Wang, K., Lv, H. and Gao, F. (2022), "Analytical target cascading for multi-level supply chain decisions in cloud perspective", Industrial Management & Data Systems, Vol. 122 No. 6, pp. 1480-1498. https://doi.org/10.1108/IMDS-06-2021-0402