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    Implementation and Validation of Multi Attribute Decision Making Technique for Supplier Selection in Supply Chain Management

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    IC 15-111.pdf (743.2Kb)
    Date
    2015
    Author
    Nirmal, Nital P.
    Nirmal, Rakesh P.
    Bhatt, Mangal G.
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    Abstract
    In Today’s Competitive Environment it is impossible to successfully produce low cost & high quality product, without satisfactory supplier. Thus one of the important purchasing decisions in smoothly running Supply Chain Management (SCM) is Supplier Selection. In this paper, the research paper pattern with the importance of SCM and its different Modelling areas, Supplier Selection Methodology (SSM) to learn the different research work carried out for Supplier Selection criteria evaluation and show the various methods for Supplier Criteria Evaluation and Ranking in SCM field have been discussed. In the current research paper authors aims to implement and validate the Complex Proportional Assessment (COPRAS) method in supplier selection and in conclusion comparing with ELECTRI-III ((Elimination Et Choix Traduisant la Realite elimination and choice translation reality) the multi attribute decision making method with the same input data. After analyzing and comparing it authors explore that the COPRAS method is more sensitive and simpler then ELECTRI-III which is also one of the Multi Attribute Decision Making (MADM) techniques. while in the given model provide relatively more sensitive than other supplier selection methods, same methodology can applied for selecting supplier in the Manufacturing, Pharmaceutical, Automobile, Ship Building, Agricultural Industries, FMCG (Fast Moving Consumer Goods), etc. where supplier plays vital role for the industries. The method will also use to selection phase of different areas of SCM Like, Logistics and transportation decision making, Reverse Supply Chain Management, related issues, Vendor Managed Inventory (VMI), Strategic decision making and uses of Information technology in SCM.
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    http://hdl.handle.net/11718/14066
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    • 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence [70]

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