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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/8832

Title: Decision support system for lean, agile and leagile manufacturing
Authors: Al-Tamimi, Abdulaziz
Al-Masoud, Hesham
Keywords: Decision support system
Agile
Leagile manufacturing
Lean
Manufacturing system
Analytical hierarchy process
Saudi Arabia
Industries
Issue Date: 2007
Abstract: The objective of this research is to develop a methodology for evaluating whether an existing manufacturing system operates under traditional, lean, agile or leagile manufacturing. The research is carried out as follows: Measuring factors and characteristics factors should be defines from the literature to built the model by Analytical Hierarchy Process (AHP). More after, a questionnaire was built to distribute it to internal and external experts according to their qualifications. The composed data is adjusted using Expert Choice (EC) software to get the Expert Opinions Ratings. Other questionnaire was developed to dispense to plants for getting their response. a Decision Support System (DSS) using a Visual Basic was developed to come with an Existing Evaluating Rating of plant. Finally, the Experts Opinion Rating and Existing Evaluating Rating were compared to conclude that either the manufacturing system strategy is traditional, lean, agile or leagile manufacturing.. To resolve the manufacturing system in order to become lean, agile or leagile; a lot of tools will help in becoming lean like Cellular Manufacturing, Total Quality Management, Pokayoke, Kaizen , Value Stream Mapping, 5 S, Takt Time, address issues within its supply chain management, increase its focus on customer service and improve the quality of its IT applications. and so on. Three case studies have been carried out with reference to the three companies which are Saudi Mechanical Industries (SMI) Company, Advanced Electronics Company (AEC) and Saudi Light Company (SLC).
Description: This study has been conducted & Submitted in partial fulfillment of the requirements for the degree of Master of Science in the Industrial Engineering Department, College of Engineering, King Saud University December 2007
URI: http://hdl.handle.net/123456789/8832
Appears in Collections:College of Engineering

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