This paper identifies how to achieve maximized customer benefit by using e-commerce as a tool to compress the supply chain, examines cost-reduction, improved efficiency, streamlined supply chain processes and fashion replenishment to determine how to gain outputs that lead to customer benefits. …
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h higher volumes of consumers across the world due to high adoption levels of Internet consumption has reduced dependency on decentralized procurement and distribution systems, allowing them to centralize these functions for efficiency, time, and also cost savings. In Sweden, as one example, 90 percent of consumers use the Internet (Entertainment NewsWeekly 2011). In Singapore, younger consumers are buying fashion products via the Internet at a growth rate of approximately 10 percent annually (Ramchandani 2011). In the United States, e-commerce sales are estimated, currently, at $227.6 billion (Steigrad 2011). It is these growth patterns in online fashion consumption that continue to provide new opportunities to compress traditional supply chains to include more efficiency and replenishment for fashion products that have very limited life cycles. Reducing the dependency on traditional collaborations, the newer, unrestricted geographic boundaries imposed by less efficient procurement and distribution systems and even changing consumer trends for purchasing and demand continue to drive new synergies for using e-commerce as a fashion supply model. Research aims and objectives This research project aims to identify how to achieve maximized customer benefit by using e-commerce as a tool to compress the supply chain. Cost-reduction, improved efficiency, streamlined supply chain processes and fashion replenishment will be examined to determine how to gain outputs that lead to customer benefits. The research objectives are as follows: 1. Determine the nature of consumer fashion market demand driving new e-commerce procurement and distribution models. 2. Identify the current e-commerce supply chain models currently finding success in key fashion markets. 3. Determine how...
This research project will tackle the issues of e-commerce within the supply chain under the premise that there is no pre-existing template that provides greater benefit to the customer. Thus, the project will be wholly exploratory in design. The traditional agile supply chain methodology involves demand-driven systems. In such a chain, market data and information are exchanged from the business to all layers of the supply chain process in order to forecast and deliver replenishment to meet consumer demand. However, agile supply networks seem to only have this title for their ability to provide merchandise by moving sourcing closer to specific target consumer markets (Barnes and Lea-Greenwood). Even under agile systems, merchandise planning failures have caused price increases on consumer goods, high levels of excess inventories, and significantly lower margins that come from demand uncertainty and collaborative failures within this vast network of procurement and distribution. Why is this? In the sourcing process, considerable investment is included in testing procedures once the textile raw materials have been purchased. All quality assurance processes are different depending on the fashion merchandiser, however it usually includes quality checks of threads, buttons, zipper durability, and testing fabric swatches/samples against existing quality standards templates. The Hong Kong Clothing Company, as one example, boasts an agile supply chain network that includes rigorous quality checks prior to even distributing the raw materials/textiles to the production floor in-house.
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base 35 1.9.3.Design Methods 36 1.9.4.Analysis of Construction 40 4.User Guide 46 5.Testing 47 1.10.Test Techniques and Methods 47 1.11.Test Plan 48 6.Evaluation 51 1.12.Stage Wise Evaluation 51 1.12.1.Evaluation of Analysis 51 1.12.2.Implementation Methodology 52 1.12.3.Testing 52 1.13.Problems and Modifications 53 1.14.Experiences 53 1.15.Future work 54 7.Bibliography 55 8.Appendix A – Class Diagram 59 9.Appendix B – Logical Data Model 60 1.
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