Improvement of Material Flow in the Production and Supply Chain of ...

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JIT was firstly occurred in Japan in the early 1970s. It decreases waste by supplying parts only when the assembly process ask for them. And the heart of JIT is the kanban card. (12MANAGE, viewed 2008) “In a JIT environment, both earliness and tardiness must be discouraged since early finished jobs increase inventory cost while late jobs lead to customers’ dissatisfaction and loss of business goodwill” (Wong, Kwong & etc., 2006). The typical attention areas of Just-In-Time include: Inventory reduction; Smaller production lots and batch sizes; Quality control; Complexity reduction and transparency; Flat organization structure and delegation; and Waste minimization. (12MANAGE, viewed 2008) Even though, there is a high risk of the reliability of suppliers. And the production might be great influenced by the cost of material and market. In this paper, when discuss the filed of optimize the supply chain management, it would be considered to use the JIT method. Lean production was raised by MIT (Massachusettes Institute of Technology). It is an approach to improve a manufacturing performance. The key of lean production is the minimization of the amount of all the resources used in the various activities of the enterprise. At all levels of the organization lean production employ teams of multi-skilled workers. Except that it use highly flexible, increasingly automated machines to produce volumes of products in potentially enormous variety. It helps identifying and eliminating no value adding actives in design, production, supply chain management, and dealing with the customers. (12MANAGE, viewed 2008) Agile Manufacturing is based on low cost and high quality, improve and change the productions following the market as soon as possible. The keystone of AM is time. During the whole cycle life of production, furthest satisfy the customers’ requirements to enhance the enterprise ascendancy. Theory of Constraint is used to continually improve and solve the constraint of bottleneck resources during the production material flow. The application of TOC is broad in various industries, for example Distribution, Supply Chain, Project Management, etc. The rationale of TOC is to find out the bottleneck in the production line, and try to solve the problems in order to make the production cycle short, also reduce the inventory. The thinking processes of TOC give us a series of steps, which combine cause-effect and our experience and intuition to gain knowledge. TOC can lead people how should they think. By this way, the manager can better understand the world around and better understand how to improve. (12MANAGE, viewed 2008) Depend on the project researched in this paper, production line model to simulate assembly line and improve supply chain management will be set up. Simulation is an important topic in a wide scope, and it is a powerful and economical way to solve problems. With the development of society and technology there are more and more kinds of simulation software. Some of the simulation software have mentioned and discussed in the first chapter. Here only gives a short abstract. Discrete event simulation has traditionally been defined by items. This modeling paradigm has served the simulation industry well, but fell far short for many industries in which the pieces 6
mindset simply does not accurately portray their particular processes. During recent years Simulation Dynamics has been working with industries where the item paradigm falls short as a descriptive tool. This work has led to the development of a revolutionary set of simulation tool built on the Extend simulation engine (Pheips, Parsons & Siprelle, 2002). In order to set up a simulation model the first thing have to do is build up a basic model and find out the parameters required. The original data have to be analyzed firstly. In recent years, the software Statistical Program for Social Science (SPSS) is used more and more in statistical analysis work. It is proved that SPSS is a great method to analysis statistical data. Because of it outstanding performances, in this paper the SPSS will be used to analyze the data in a further step. Before that a primary data analysis will be given. SPSS is a kind of professional statistical analysis software. It takes a very important role both in social science and natural science. As Cai and Zhuang described in 2001 from the point of data statistical analysis, there are three categories. 1. The first is basic statistic. It includes Descriptive Statistic, Explore Statistic, Crosstabs’ Analysis, Linear Compounding Measurement, T-Test, One-Way ANOVA Analysis, Multiple Response Analysis, Linear Regression Analysis, Correlativity Analysis, Nonparametric Tests, etc. 2. The second is professional statistic. The scope of this field includes Discriminatory Analysis, Factor Analysis, Cluster Analysis, Range Analysis, Reliability Analysis, and otherwise. 3. The third is advanced professional statistic. Such as Logistic Regression Analysis, MANOVA Analysis, Repeated Measure Variance Analysis, Nonlinear Regression, Probit Regression, Cox Regression, Curve Estimation and so on. According to significances of different parameters, the functions used in this paper cover all of the three categories. After all required parameters are collected, a production line model will be set up by the Extend software. Extend is able to model a wide range of systems. It is a general purpose graphically oriented discrete event and continuous simulation application with an integrated authoring environment and development system. The Extend family of simulation packages includes Extend CP, Extend OR, Extend Industry, and Extend Suite. All levels of modelers can efficiently create accurate, credible, and usable models in the Extend simulation environment by the tools it provides. Extend Design facilitates every phase of the simulation project, from creating, validating, and verifying the model, to the construction of a user interface which allows others to analyze the system. For the simulation tool developers, they can use Extend Built-in, compiled language, to create reusable modeling components. All of this is done within a single, self-contained software program. (Krahl, 2003) 7
Page 1 and 2: ROYAL INSTITUTE OF TECHNOLOGY Impro
Page 3 and 4: Needs to be pointed out is that in
Page 5 and 6: TABLE OF CONTENTS ABSTRACT.........
Page 7 and 8: List of Tables Table 3.1 Descriptiv
Page 9 and 10: CHAPTER 1 INTRODUCTION OF THE RESEA
Page 11 and 12: 1.2 Present problems Haldex faces i
Page 13: CHAPTER 2 RESEARCH THEORY AND METHO
Page 17 and 18: the customers. Both of them are pri
Page 19 and 20: processing in station 5b. In the re
Page 21 and 22: After deleting the un-normal data i
Page 23 and 24: data of preassembly line accords wi
Page 25 and 26: Standard Deviation 68.49 Standard D
Page 27 and 28: Table 3.6 Values of Parameters of T
Page 29 and 30: line. In order to simplify the mode
Page 31 and 32: Table 4.3 Output Date Come From the
Page 33 and 34: Table 4.5 Current Production Volume
Page 35 and 36: Table 4.7 listed the simulation res
Page 37 and 38: 4.3.3 Increasing the operation time
Page 39 and 40: ottlenecks. Obviously, from the poi
Page 41 and 42: the first one. For the third soluti
Page 43: and unforeseen events by keeping sa
Page 46 and 47: separately and the supply chain emp
Page 48 and 49: 5.2.1 Current distribution of suppl
Page 50 and 51: Table 5.2* Delivery Information of
Page 52 and 53: ecause this only gets from experien
Page 54 and 55: 5.3.2 Inventory and suppliers’ se
Page 56 and 57: Table 5.3 Integrative Estimation of
Page 58 and 59: CHAPTER 6 INVENTORY REDUCTION AND D
Page 60 and 61: How to get the best value from a su
Page 62 and 63: calculation, 420 pallets can be suc
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each day. The maximum load of one t
Page 66 and 67:
and can also overworking during wee
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CHAPTER 7 CONCLUSION AND FUTURE WOR
Page 70 and 71:
Reference Books and papers • Ahma
Page 72 and 73:
y Chapman&Hall. • Vickery, Shawne
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Appendix A Table 1 TTF & TTR Data o
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Table 5.1 Suppliers & Suppliers’
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Appendix B Figure 1 Assembly line M
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Figure 3 Current Assembly Line Mode
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Figure 5 Assembly Line Model in Sol
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