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TBEA Working Capital Performance Evaluation And Optimization

Posted on:2021-04-07Degree:MasterType:Thesis
Country:ChinaCandidate:J D ZhuFull Text:PDF
GTID:2439330620472673Subject:Accounting
Abstract/Summary:PDF Full Text Request
Special equipment manufacturing enterprises occupy a core position in China’s manufacturing industry,both in the people’s livelihood infrastructure,or high-end manufacturing development has a decisive role.However,under the leadership of China’s supply-side reform in recent years,the adjustment of enterprise capital structure and the optimization and improvement of liquidity management are imperative.In this way,how to make the working capital can play its role,improve the operation efficiency has become the key to occupy the market core position.Therefore,for enterprises,the evaluation and analysis of the excellent level of enterprise working capital,to improve the efficiency of enterprise capital operation,optimize the business structure has a very important role.In the following part,firstly,the concepts related to working capital,the research concept of the performance of working capital and other domestic and foreign references are reviewed and summarized.Then,relevant working capital theories are summarized to clarify the research direction.and introduces the development and application of BP neural network adopted in this paper.Secondly,this paper summarizes and analyzes the characteristics of the specific research industry--special equipment manufacturing industry,and explains the characteristics and problems of the working capital situation of China’s manufacturing enterprises in the current background.On this basis,based on the BP neural network’s powerful ability to learn and budget,choose and construct the performance evaluation of the relevant indicators of working capital performance evaluation model of equipment manufacturing industry of working capital,research object of this article,change especially electrician integrity of working capital performance evaluation,this is an innovative point in this paper.Then,from the perspective of supply chain,based on tbea’s position in the supply chain,we can evaluate and analyze its working capital performance in purchasing,production and marketing related to business activities.Study found that the change especially electrician working capital the overall liquid is poorer,and the upstream and downstream suppliers and distributors have stronger capital takes up the case,to a certain extent,this in the perspective of this article is the enterprise on its own technology,occupying the embodiment of the core status throughout the supply chain,but the concrete analysis,found that change especially electrician in purchasing channels of production as well as the channel of the accounts payable turnover is not efficient,the characteristics of the production process the circulation efficiency of funds shortage,marketing channel,accounts receivable turnover days of increased year by year,product sales channel flow are also worth change especially electrician.In view of these problems,by combining the characteristics of manufacturing industry and combining working capital with enterprise business,this paper puts forward reasonable Suggestions on measures that enterprises should take,and finally summarizesthe main conclusions and shortcomings of this paper.The research conclusions of this paper are as follows: firstly,although tbe’s working capital performance is above the industry average in the context of the overall operating environment of the professional equipment manufacturing industry,there are still hidden dangers and deficiencies in all aspects,which need to be paid attention to and solved.Secondly,in the future era of big data,big data research and accounting development will be increasingly closely linked.Therefore,this paper combines financial performance evaluation with the exploration of BP neural network,and also makes an exploration and prospect of future accounting informatization research.
Keywords/Search Tags:Working capital The performance evaluation, BP neural network, Supply chain management
PDF Full Text Request
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