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Construction And Application Of Lean Production Factor System

Posted on:2020-06-27Degree:MasterType:Thesis
Country:ChinaCandidate:Y W JiangFull Text:PDF
GTID:2392330590452270Subject:Industrial engineering
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Faced with the diversified development of market demand,traditional mass production method has been unable to support the long-term operation of enterprises.Different from the traditional production mode,lean production has become the most popular production management at present due to its ability of quickly responding to changes in customer demand,eliminating waste,and making the organization to be lean to meet the enterprise’s production characteristics of “high variety” and “small lot”.Most enterprises only use some of the tools and methods of lean production when applying lean production,but they do not really understand the inherent nature of lean production and cannot play the real role of lean production.In fact,lean production is a system.The system is composed of many elements.There is relationship between one element and other elements.Taking the A company of the optoelectronic display industry as an example,this paper selected sixteen lean production factors based on the previous studies,and applied the fuzzy interpretation structure model to construct the hierarchical structure diagram of lean production factors by respectively using the extraction rules of result priority,reason priority and result-reason rotation.The hierarchical structure diagram of lean production factors was divided into four levels,including layer of demand management and design,layer of lean technology application,layer of lean culture promotion and layer of lean human support.The stratified and hierarchical structure diagram of lean production factors was done.The driving force and dependence index were introduced,and the fuzzy cross-impact analysis of lean production factors was carried out.The lean production factors were divided into four categories: independent factors,dependent factors,chain-type factors and driving factors.Based on the above,the system of lean production factors was formed.The system was used to guide the implementation of lean improvement projects.Comprehensively using of value stream mapping,program analysis,stopwatch time measurement,production line balance and other methods,we found that the company’s Mini Line had high worker consumption,high WIP inventory,low balance rate,etc.Guided by the lean production factor system,this lean improvement of Mini Line was from customer needs to lean design and then lean technology applications.Comprehensively using the methods and techniques of process program analysis,therbligs analysis,cell production,human factors engineering,concurrent engineering,human-machine operation analysis and low cost intelligent automation(LCIA),the Mini Line was gradually transformed into Cell Line 2.0,which increased the rate of line balance to 100%,reduced the demand manpower from 20 to 6 and increased the per capita capacity by more than 200%.At the same time,the robot replaced part of the manual action to realize automatic loading,unloading,assembly,handling and pasting tape,which made the operator and the robot high coordinated.Cell Line 2.0 realized the man-machine collaborative operation,and reduced the difficulty and intensity of the operation.Combined with the Kanban management and the idea of continuous flow,the front-pull and back-push production system was established,which reduced the WIP inventory and the time of production delivery from 158.68 hours to 149.23 hours.And the future value stream mapping was plotted to be the improvement points of next stage.Based on the Cell Line 2.0 and guided by the lean production factor system,it is proposed that lean production requires lean culture concept and lean manpower support,so that lean production can be implemented in the long-term and achieve greater benefits.Finally,the data before and after the improvement were compared to make the improvement effect clear at a glance.
Keywords/Search Tags:lean production, lean production factors, fuzzy interpretation of structural model, value stream mapping, cell production
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