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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yun Huang ◽  
Kaizhou Gao ◽  
Kai Wang ◽  
Haili Lv ◽  
Fan Gao
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Product Family ◽  
Optimization Method ◽  
Analytical Target Cascading ◽  
Product Family Design ◽  
Decision Rights ◽  
Content Type ◽  
Target Cascading ◽  
Multi Level

PurposeThe 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/approachThe 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.FindingsA 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/valueThis 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.

Global Product Family Design: A Mathematical Model for Simultaneous Decision of Module Commonalization and Supply Chain Configuration

2011 ◽  
Author(s):  
Kikuo Fujita ◽  
Hirofumi Amaya ◽  
Ryota Akai
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Product Family ◽  
Product Variety ◽  
Optimization Method ◽  
Product Family Design ◽  
Design Production ◽  
Supply Chain Configuration ◽  
The Given ◽  
Chain Configuration

Today’s manufacturing has become global at all aspects of marketing, design, production, distribution, etc. While product family design has been an essential viewpoint for meeting with the demand for product variety, its meaning is becoming more broad and complicated with linking product design with issues on market systems, supply chain, etc. This paper calls such a design situation ‘global product family design,’ and firstly characterizes its components and complexity. Following them, this paper develops a mathematical model for the simultaneous decision problem of module commonalization strategies under the given product architecture and supply chain configuration through selection of manufacturing sites for module production, assembly and final distribution as an instance of the problems. This paper demonstrates some numerical case studies for ascertaining the validity and promise of the developed mathematical model with an optimization method configured with a genetic algorithm and a simplex method. Finally, it concludes with some discussion on future works.

Analytical Target Cascading in Product Family Design

2006 ◽  
pp. 225-240 ◽  
Author(s):  
Michael Kokkolaras ◽  
Ryan Fellini ◽  
Harrison M. Kim ◽  
Panos Y. Papalambros
Keyword(s):  
Product Family ◽  
Analytical Target Cascading ◽  
Product Family Design ◽  
Target Cascading

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

scholarly journals Reading on and between the lines: risk identification in collaborative and adversarial buyer–supplier relationships

2018 ◽  
Vol 23 (4) ◽  
pp. 351-376 ◽  
Author(s):  
Yiyi Fan ◽  
Mark Stevenson
Keyword(s):  
Social Capital ◽  
Supply Chain ◽  
Risk Identification ◽  
Social Capital Theory ◽  
Future Research ◽  
Supplier Relationships ◽  
Content Type ◽  
Signalling Theory ◽  
Multi Level ◽  
The Impact

Purpose This paper aims to investigate how supply chain risks can be identified in both collaborative and adversarial buyer–supplier relationships (BSRs). Design/methodology/approach This research includes a multiple-case study involving ten Chinese manufacturers with two informants per organisation. Data have been interpreted from a multi-level social capital perspective (i.e. from both an individual and organisational level), supplemented by signalling theory. Findings Buyers use different risk identification strategies or apply the same strategy in different ways according to the BSR type. The impact of organisational social capital on risk identification is contingent upon the degree to which individual social capital is deployed in a way that benefits an individual’s own agenda versus that of the organisation. Signalling theory generally complements social capital theory and helps further understand how buyers can identify risks, especially in adversarial BSRs, e.g. by using indirect signals from suppliers or other supply chain actors to “read between the lines” and anticipate risks. Research limitations/implications Data collection is focussed on China and is from the buyer side only. Future research could explore other contexts and include the supplier perspective. Practical implications The types of relationships that are developed by buyers with their supply chain partners at an organisational and an individual level have implications for risk exposure and how risks can be identified. The multi-level analysis highlights how strategies such as employee rotation and retention can be deployed to support risk identification. Originality/value Much of the extant literature on supply chain risk management is focussed on risk mitigation, whereas risk identification is under-represented. A unique case-based insight is provided into risk identification in different types of BSRs by using a multi-level social capital approach complemented by signalling theory.

A multi-objective supplier selection model for green supply chain network under uncertainty

2018 ◽  
Vol 13 (3) ◽  
pp. 605-625 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Hadis Derikvand
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Objective Function Value ◽  
Selection Problem ◽  
Green Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model ◽  
Supplier Selection Problem

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

A multi-objective optimization model for sustainable supply chain network with using genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Ehtesham Rasi ◽  
Mehdi Sohanian
Keyword(s):  
Supply Chain ◽  
Performance Indicators ◽  
Supplier Selection ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Sustainability Performance ◽  
Multi Objective ◽  
Sustainable Supplier Selection

Purpose The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network. Design/methodology/approach The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system. Findings The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach. Practical implications The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling. Originality/value There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

Integrating supplier selection in optimal product family design

2011 ◽  
Vol 49 (14) ◽  
pp. 4195-4222 ◽  
Author(s):  
X.G. Luo ◽  
C.K. Kwong ◽  
J.F. Tang ◽  
S.F. Deng ◽  
J. Gong
Keyword(s):  
Supplier Selection ◽  
Product Family ◽  
Product Family Design

Green supplier selection using an AHP-Entropy-TOPSIS framework

2015 ◽  
Vol 20 (3) ◽  
pp. 327-340 ◽  
Author(s):  
James Freeman ◽  
Tao Chen
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Selection Criteria ◽  
Second Phase ◽  
Structured Interviews ◽  
Entropy Weight ◽  
Two Phase ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Green Supplier Selection

Purpose – This paper aims to focus on development of a green supplier selection model using an index system based on a combination of traditional supplier and environmental supplier selection criteria. Strategies that balance economic and environmental performance are increasingly sought after as enterprises that increasingly focus on the sustainability of their operations. Green supply chain management (GSCM) in particular, enables the integration of environmentally friendly suppliers into the supply chain to be systematised to fit with specific environmental regulations and policies. More persuasively, GSCM allows enterprises to improve profits whilst lowering impacts on the global environment. Design/methodology/approach – A two-phase survey approach was adopted for the research. For the first phase, semi-structured interviews with senior management representatives of the case company – a Chinese-based electronic machinery manufacturer – were used to determine green supplier selection criteria. For the second phase, a two-part questionnaire survey was undertaken, the first part providing the data for an analytic hierarchy process (AHP) analysis of the first-phase criteria and the second with collecting data for an Entropy weight analysis. The resultant AHP and Entropy weights were then combined to form compromised weights – which, using technique for order preference by similarity to the ideal solution (TOPSIS) methodology, were translated into preferential rankings of suppliers. Findings – Senior managers were found to rank traditional criteria more highly than environmental alternatives – the implication being that for the company, concerned, it may take some time before environmental awareness is fully assimilated into GSCM practice. Originality/value – The paper moves us a significant step closer to the application more widely, of innovative AHP-Entropy/TOPSIS methodology to real-world SCM problems.

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