Analytical target cascading for multi-level supply chain decisions in cloud perspective
| DOI | https://doi.org/10.1108/IMDS-06-2021-0402 |
| Published date | 14 December 2021 |
| Date | 14 December 2021 |
| Pages | 1480-1498 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Yun Huang,Kaizhou Gao,Kai Wang,Haili Lv,Fan Gao |
Analytical target cascading for
multi-level supply chain decisions
in cloud perspective
Yun Huang
School of Business, Macau University of Science and Technology, Taipa, Macao
Kaizhou Gao
Macau Institute of Systems Engineering,
Macau University of Science and Technology, Taipa, Macao
Kai Wang
Economics and Management School, Wuhan University, Wuhan, China
Haili Lv
School of Transportation and Logistics Engineering,
Institute of Logistics System Science and Engineering,
Wuhan University of Science and Technology, Wuhan, China, and
Fan Gao
School of Business, Macau University of Science and Technology, Taipa, Macao
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.
Keywords Cloud technology, Analytical target cascading, Greenhouse gas, Supplier selection, Product family
design, Jaya
Paper type Research paper
1. Introduction
With the rapid development of cloud technologies, for example, Cloud Computing and
Internet of Things (Atzori et al., 2010), new opportunities were created to better optimize the
supply chain decisions. From a cloud perspective on supply chain systems, the information of
supply chain partners can be shared to make a centralized decision, while keeping their
IMDS
122,6
1480
This research is financially supported by Macau University of Science and Technology (Grant No. FRG-
18-022-MSB).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 27 June 2021
Revised 7 September 2021
Accepted 29 September 2021
Industrial Management & Data
Systems
Vol. 122 No. 6, 2022
pp. 1480-1498
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-06-2021-0402
decision autonomies. Thus, the conflicting and contractual relations between supply chain
individuals could be maintained when co nsidering coordination issues (Leuk el and
Kirn, 2011).
As the prevailing of product modularization and outsourcing, the success of a particular
product relies on the product design and on the performance of the upstream suppliers (Fine
et al., 2005;Ali et al., 2021). Integrating supplier selection with product family design into
supply chain optimization has attracted attention from researchers and industry gradually
for the past several decades (Lee et al., 2009;Zhang et al., 2016). Meanwhile, the negative
impact of greenhouse gases (GHG) emission on the environment cannot be ignored
nowadays. It is reported by the United States Environmental Protection Agency that from
1990 to 2015, due to GHG emission, the warming impact on the earth increased by 37% (www.
epa.gov/climate-indicators/greenhouse-gases). Reducing GHG emission sho uld also be
carefully considered into the product and supply chain configuration, which can bring
safer and cleaner factories, reduce cost, enhance public relations, etc. (Plambeck, 2012).
Most existing research for optimizing supply chain problems used central optimization
methods or all-in-one (AIO) methods from a cloud perspective (Ali et al., 2021). However, the
AIO method can hardly allow each supply chain member to maintain its decision rights with
only one decision model. Analytical target cascading (ATC) is a model-based, multilevel,
hierarchical optimization method for translating system-level design targets to design
specifications (Huang et al., 2006;Tosserams et al., 2006), which can keep the autonomous
decision right through the system optimization process (Zhang et al., 2016). This paper
contributes to the existing literature on integrating marketing and GHG emission into
product and supply chain design by cloud technology, applying the ATC model.
The manufacturer purchases alternative components of a certain functionality from his
optional suppliers or remanufactures from components recycled to produce a set of platform
products to meet the requirements from the retailers in different market sectors. For the
components suppliers, they face the problem of deciding the component bidding prices. The
manufacturer, as the core enterprise, balances its profit and GHG emission, selects the best
suppliers and components and determines the wholesale prices and GHG emission decisions.
The retailers have to decide on the retail prices. ATC model will be used to deal with supply
chain coordination problems while maintaining most of its features of decision-making and
distributed computing. Specifically, the following research questions are proposed:
(1) How to optimize the marketing, GHG emission, product configuration, supplier and
transportation mode selection decisions of the three-level supply chain members,
while maintaining their autonomies in decision rights under the current supply chain
setting by cloud technology?
(2) How to convert the three-level supply chain optimization problem into an ATC
model?
(3) How to solve the ATC model in corresponding with the decision procedure?
We provide a three-stage cloud-based management system and an ATC model to study the
decision-making between the members in a green supply chain. The following contributions
are made in the paper. First, a three-stage cloud-based management system is proposed to
help implement the decision optimization of supply chain individuals. Second, an ATC model
is formulated to address the multi-level supply chain optimization problem. The ATC model
actually consists of two sub-problems. The first sub-problem is between the manufacturer
and the retailers and the second one is between the manufacturer and the suppliers. The
manufacturer, as the upper level, obtaining the sales data from different markets through the
cloud system, and developing the architecture for the product family, cascades the targets to
Analytical
target
cascading cloud
perspective
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