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A Group Decision Making Support System in Logistics and Supply Chain Management A group decision making support system in logistics and supply chain management Morteza Yazdani a,∗, Pascale Zarate a, Adama Coulibaly a, Edmundas Kazimieras Zavadskas b a University of Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France b Research Institute of Smart Building Technologies, Vilnius Gediminas Technical University, Lithuania a b s t r a c t Purpose: The paper proposes a decision support system for selecting logistics providers based on the quality function deployment (QFD) and the technique for order preference by the similarity to ideal solu- tion (TOPSIS) for agricultural supply chain in France. The research provides a platform for group decision making to facilitate decision process and check the consistency of the outcomes. Methodology: The proposed model looks at the decision problem from two points of view consider- Keywords: ing both technical and customer perspectives. The main customer criteria are confidence in a safe and Group decision support system durable product, emission of pollutants and hazardous materials, social responsibility, etc. The main tech- Fuzzy linguistic variables nical factors are financial stability, quality, delivery condition, services, etc. based on the literature review. Logistic provider The second stage in the adopted methodology is the combination of quality function deployment and the Quality function deployment technique for order preference by similarity to ideal solution to effectively analyze the decision prob- Supply chain lem. In final section we structure a group decision system called GRoUp System (GRUS) which has been Technique for order preference by similarity developed by Institut de Recherche en Informatique de Toulouse (IRIT) in the Toulouse University. to ideal solution Results: This paper designs a group decision making system to interface decision makers and customer values in order to aid agricultural partners and investors in the selection of third party logistic providers. Moreover, we have figured out a decision support system under fuzzy linguistic variables is able to assist agricultural parties in uncertain situations. This integrated and efficient decision support system enhances quality and reliability of the decision making. Novelty/Originality: The novelty of this paper is reflected by several items. The integration of group multi- criteria decision tools enables decision makers to obtain a comprehensive understanding of customer needs and technical requirements of the logistic process. In addition, this investigation is carried out un- der a European commission project called Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) which models risk reduction and elimination from the agricultural supply chain. Ultimately, we have implemented the decision support tool to select the best logistic provider among France logis- tics and transportation companies. 1. Introduction cision support systems had been conceived through the work of Scott Morton. The approach tries to analyze strategic decisions to Knowledge-based decision models are getting significant atten- offer support to decision makers (DMs) in a complex and poorly tion in academia and industry. A vast amount of original research structured situation. DSSs have some advantages in decision mak- and thesis projects have been carried out in order to make robust ing process through assisting decision makers in their tasks and decision support systems (DSS) to facilitate managerial decisions. improving quality of decision process ( Zarate, 2013 ). The concept Decision Support Systems are categorized as a specific class of of DSS comes from a balance between human judgment and in- computerized information system that supports management de- formation process by a computer. There are three fundamental cision making activities. By the early 1970 s, the concept of de- components of DSSs. Firstly; there is database management sys- tem (DBMS) which serves as a data bank for the DSS. The second component is Model-based management system (MBMS). The role ∗ Corresponding author. of MBMS is analogous to that of a DBMS and, finally the method E-mail addresses: [email protected] , [email protected] of dialog generation and management system (DGMS) ( Erfani, (M. Yazdani), [email protected] (P. Zarate), [email protected] (A. Coulibaly), [email protected] (E.K. Zavadskas). http://dx.doi.org/10.1016/j.eswa.2017.07.014 Afrougheh, Ardakani, & Sadeghi, 2015; Power, Sharda & Burstein, manage all those practices and in this way, the physical and infor- 2015; Khademolqorani & Hamadani, 2013; Marakas, 2003 ). mation flow exchanged among all players in a supply chain ( Konig The selection of logistics providers (LPs) is an emergence of to- & Spinler, 2016 ). day’s competitive market. With the development and advancement Development of the new theories and methodologies in logis- of the supply chain theories, the selection of logistic providers tics and supply chain management can lead to the higher level for the function of logistics support becomes considerable. Over intelligent and advanced systems. Such kind of systems enable the last decades, the direction of decision support systems has supply chain experts to facilitate information-sharing, high qual- changed drastically. Computer and industrial professionals made ified decisions and to increase the value to products and ser- effort s to systematize decision making process in manufacturing vices by internal coordination ( Chandra & Kumar, 20 0 0 ). Supply and production sectors ( Guo, Ngai, Yang, & Liang, 2015 ). For exam- chain management has tied up with the application of information ple, Zha, Sriram, Fernandez, and Mistree (2008) modeled a com- technology (IT) which brings competitive advantages of knowl- promise decision support problem technique and the fuzzy syn- edge sharing with customers and stakeholders to improve coordi- thetic decision model (FSD) to quantitatively incorporate qualita- nation and communication among suppliers and partners for com- tive design knowledge and preferences of designers for multiple, panies ( Ngai, Peng, Alexander, & Moon, 2014 ). The selection of lo- conflicting attributes. They argue that the model is generic and gistic providers for the function of logistics support becomes con- flexible enough to be used in a variety of decision problems. Appli- siderable. Over the last decades, the direction of decision sup- cation of a decision support system in supply chains by employing port systems has changed drastically. To monitor the materials multi-criteria decision-making is a constant challenge ( Kristianto, cost in a garment manufacturer, a decision support model has as- Gunasekaran, Helo, & Sandhu, 2012; Scott, Ho, Dey & Talluri, 2015; sisted decision-makers in selecting efficient ways to reduce to- Shi, Yan, Shi, & Ke, 2015 ). Integrated models are highly appreci- tal manufacturing costs ( Wong & Leung, 2008 ). Couple of review ated because each method has a unique function. However, choos- projects has been conducted in terms of intelligent models, de- ing the most accurate approach is often a dilemma and intriguing cision support tools and system in supply chain field ( Seuring, practical question faced by supply chain managers. An unsuitable 2013; Taticchi, Tonelli & Pasqualino 2013 ). Seuring (2013) argued integrated model can cause terrible results and failure of the sys- that the performance of sustainability and supply-chain manage- tem. ment must be researched practically by a strategic decision-making To adopt a reliable and practical decision making model, we support model. Liu, Wang and Liu (2012) structured a sustainabil- propose the integration of quality function deployment (QFD) and ity analysis framework with the integration of life cycle assess- technique for order preference by similarity to ideal solution (TOP- ment and a multi-criteria decision-making process to support en- SIS) with aid of fuzzy linguistic variables made by group of deci- vironmental, social and the economic aspects of the supply chain sion makers. From the technical and practical viewpoint, the pro- management. Bhattacharya, Mohapatra, Kumar, Dey, Brady and Ti- posed model has some advantages when it approaches a group de- wari (2014) demonstrated a green supply chain performance mea- cision system with such combination. In MCDM modeling, it is rec- surement perspective and made an effort to deliver a collabora- ommended to weight decision factors (attributes) using combined tive decision-making model using fuzzy analytical network pro- structures ( Tavana, Yazdani, & Di Caprio, 2017 ). In many decision cess. Accorsi, Manzini, and Maranesi (2014) developed an origi- making problems, the reliability of the decision criteria is strictly nal decision-support system for the design, management, and con- dependent on the stakeholders and customers’ preferences as ex- trol of warehousing systems with solid DBMS architecture. Guo ternal weights. It is tough for customers to deliver judgments via et al. (2015) proposed radio frequency identification -based in- solid and numerical values. Fuzzy linguistic variables allow us to telligent decision support system to handle production monitor- assure the quality of judgment and then fuzzy MCDM conducts ing and scheduling in a distributed manufacturing environment. optimal procedure to final objectives. An efficient and flexible de- A decision model for supplier selection and in stochastic, multi- cision tool which is able
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