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DC Field | Value | Language |
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dc.contributor.advisor | Chakraborty, Partha Sarathi | - |
dc.contributor.advisor | S. Nallusamy | - |
dc.contributor.advisor | Majumdar, Gautam | - |
dc.contributor.author | K. Balakannan | - |
dc.date.accessioned | 2022-12-27T08:44:18Z | - |
dc.date.available | 2022-12-27T08:44:18Z | - |
dc.date.issued | 2017 | - |
dc.date.submitted | 2017 | - |
dc.identifier.other | TC2751 | - |
dc.identifier.uri | http://20.198.91.3:8080/jspui/handle/123456789/1944 | - |
dc.description.abstract | ABSTRACT In this era of globalization and fluctuating markets, supply chain management has gained more significance among the corporate. Supply chain management is an important activity in manufacturing which influences product life cycles, price levels, delivery schedule, customer satisfaction, inventory etc., Supply chain management integrates all key business activities through improved relationship at all levels of supply chain. In the present scheme of things, in a manufacturing industry inventory is pitched as one of the predominant resources that require to be handled effectively. The aim of the first section of the research was to develop a mixed- integer linear programming model to configure the closed loop supply chain (CLSC) network that could be optimized for maximizing the profit by determining the fixed order quantity inventory policy in various sites at multiple periods. In onward supply chain, a standard inventory policy could be followed when the product moves from manufacturer to end user, but it is very difficult to manage the inventory in the reverse supply chain of the product with the same standard policy. The proposed model examines the standard policy of fixed order quantity by considering three major types of return-recovery pair such as, commercial returns, end-of-use returns, end-of-life returns and their inventory positioning at multiple periods. Raw material supplier, manufacturer, distributer, retailer, customers and for major returnscollection sites like repair site, disassembly site, recycling site and disposal site were included in the network to develop this CLSC network model. The objective of this section was to maximize the profit through CLSC by determining the optimal inventory of product and part mix during multiple periods. The proposed model to configure the CLSC network was solved by using IBM ILOG CPLEX OPL studio and the results of the model were analysed with numerical investigations followed by sensitivity analysis. In universal spirited environment, automotive industries are desired to perform efficiently to meet the maximum percentage of demand by minimum cost. The objective of the second section of the research section was to create a balance scorecard model for the evaluation of reliability and performance of automotive manufacturing industries to evaluate their supply and demand chain system. Using this new idea, the performance of industries could be assessed regarding their supply and demand chain system with the major four criteria like design and development, manufacturing point, financial requirement and consumer’s point of view. The main four features of the industries are policies and firm coordination, design and execution, effectiveness of shipment and information technology usage totally covered by the new designed balanced scorecard with twenty five evaluation points. A broad survey was carried out in Indian automotive manufacturing industries with well structured questionnaires to collect the necessary data. The comparison between multinational, public limited, private limited and small scale organizations were carried out to measure the performance variation of their supply and demand chain system. Based on the correlation of the above four features, a structural equation model was designed to improve the supply and demand chain management system for automotive manufacturing industries and found that the ‘α’ coefficient was above 0.80, hence, the balanced score card was taken as reliable. The third section of the research was to study the application of Fuzzy AHP method for evaluating Green supply chain management strategies for automobile manufacturing company. The strategies were calculated by the model of Fuzzy AHP, the main attributes, sub attributes and measurement indicators were defined based on the automobile manufacturing process. The fourth section of the study deals with the selection of suppliers. Traditionally in Supply chain management, the key focus and scope has been on managing the flow of materials and goods from suppliers through manufacturing and distribution chain to the customer. The idea of demand chain management is based on the principle of using demand instead of supply as the factor integrating the information needs in the supply chain. The key in demand chain management is the continuous flow of the demand information from customers and end users through distribution and manufacturing to suppliers. The shared objective of the chain is fulfilling of customer demand. The most important controlling inputs are rolling forecasts and plans, point-of-sales data, daily orders, management decisions and performance feedback. The final section of the research was on the green supply chain management which is the basic tool for integration of raw materials procurement, production handling and material distribution. The added benefits of this process is effective management capacity, accuracy in demand forecasting and enhanced delivery performance by making the supply chain more sustainable and effective. The organizations should adopt ecological balance, eco-friendly strategies for the establishment of harmonic supply chain management. Already various investigations have been done and theoretical and empirical models developed in the field of supply chain management. The results of the study revealed that "green manufacturing” share to be the most worthwhile attribute in the present Green supply chain management strategy. The overall objective of this research on the supply and demand chain performance parameters has been made with a view on green manufacturing in an automobile industry. From the convincing studies conducted, the results were evaluated and various suggestions were presented for improvement. | en_US |
dc.format.extent | x, 179 p. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Jadavpur Univesity, Kolkata, West Bengal | en_US |
dc.subject | Supply Chain Management (SCM) | en_US |
dc.subject | Demand Chain Management (DCM) | en_US |
dc.subject | Fuzzy Logic Decision Making Method | en_US |
dc.subject | Analytic Hierarchy Process | en_US |
dc.subject | Multi-Grade Fuzzy Approach | en_US |
dc.subject | Closed loop supply chain (CLSC) network | en_US |
dc.subject | Fuzzy AHP method | en_US |
dc.title | Performance improvement of an automobile industry using supply and demand chain management | en_US |
dc.type | Text | en_US |
dc.department | Jadavpur Univesity. Department of Adult and Continuing Education & Extension | en_US |
Appears in Collections: | Ph.D. Theses |
Files in This Item:
File | Description | Size | Format | |
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PhD thesis (ACEE) K. Balakannan.pdf | 2.01 MB | Adobe PDF | View/Open |
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