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An evaluation of manufactures corporative performance, a DEA application

國立台灣科技大學 99 管研所博士班 企業決策分析與績效評估. An evaluation of manufactures corporative performance, a DEA application. 指 導 老 師：喻奉天 博士 Task Members – D9916903 曾麗娟 /D9916904 廖耀堂 /D9916905 陸金正 D9916906 蕭裕耀 /D9916908 周賢昌 / D991690 9 郭五鳳 D9916910 陳勝強. Outline. Abstract Motivation & Objective

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An evaluation of manufactures corporative performance, a DEA application

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  1. 國立台灣科技大學99管研所博士班 企業決策分析與績效評估 An evaluation of manufactures corporative performance, a DEA application 指 導 老 師：喻奉天 博士 Task Members – D9916903曾麗娟/D9916904廖耀堂/D9916905陸金正D9916906蕭裕耀/D9916908周賢昌/D9916909郭五鳳D9916910陳勝強

  2. Outline • Abstract • Motivation & Objective • Previous Studies • Methodology • Analysis procedure • DMU selection • Decision for input & Output • Empirical results – CCR & BCC analysis • Discussion • Conclusion

  3. Abstract • This study used data envelopment analysis (DEA) methodology to evaluate the operational performance of a range of manufacturers where located in Northern China. • 18 manufactures which are produced sport products are evaluated. • Assigned suppliers have to approach a project in 2009 and to achieve a compliance programme to meet acceptable capacities, required by the sport brand. • Brand uses their owned evaluation criteria to evaluation how those manufacturers are committed, responded and efficient to be strategy partners.

  4. Abstract • The Data Envelopment Analysis (DEA) method is used as purposed of: • to rank overall efficiency. • to solve multi-criterion problems. • The multiple inputs and outputs are used for decision making unit (DMU) to demonstrate aggregate efficiency (CCR) and technical efficiency (BCC). • The study endeavors to figure out the business efficiency on field of brand corporative by reviewing empirical results. • The outcome of DEA application will not be only a data but also a value to be effected from brand decision for making business plan development in the future.

  5. Motivation & Objective • In Asia, majority enormous manufactures are sub-contractors. They are survived by receiving buyers or brands orders to fill its designed capacitates. • For meeting long term business opportunity, manufactures must be corporative to be good factories and satisfy of brands or buyers so that to gain stable orders to balance its operation demands. • Nowadays, plenty of evaluation mechanisms brands or buyers are using to evaluate manufactures performance. Well-know commonly of that ratio analysis, weighting average and statistic analysis are applied. • The study is guide to use to evaluate manufactures acknowledge of their competitions.

  6. Previous studies

  7. Methodology – CCR application • Will scan and paste the formula of CCR

  8. Methodology – BCC application • Will scan and paste the BCC formula

  9. Analysis Procedure Define DMU Selection of Input/Output Data collection & sorting • Golany and Roll (1989) believe when using DEA to evaluate economical benefits, the evaluated projects to have the same characters of the following: • Same aims, similar jobs; • Under same market conditions; and • Same inputs/outputs that affect the evaluated projects. • An empirical application, number of DMU is ideally allowed twice the number of inputs and plus outputs. Decision of evaluation model Result & explanations Model performing

  10. DMU Selection (I) means Input / (O) means Output

  11. Decision of input and output items • Used data on Shanghai global sourcing office of a sports brand. • 18 of said strategy business suppliers data collected from outcome of 2009 which has involved brand engaged project. • Reference points: different Nationality should be referred, those factories are owned by Mainland Chinese, Taiwanese, HK-Chinese, Malaysian might come with different management senses and commitment of corporative and implementations. • Number of DMU is greater than twice the number of inputs plus outputs.

  12. Empirical results – CCR & BCC analysis • The correlation factor of inputs and outputs are looked positive, and enough to be used for determining of DEA analysis.

  13. Empirical results – CCR & BCC analysis

  14. Empirical results – CCR & BCC analysis Average of scores = 0.862669528 No. of efficient DMUs = 8 No. of inefficient DMUs = 10 No. of over iteration DMUs = 0 Average of scores = 1 No. of efficient DMUs = 9 No. of inefficient DMUs =9 No. of over iteration DMUs = 0

  15. Empirical results – CCR projection review

  16. Empirical results – CCR projection review

  17. Empirical results – CCR projection review

  18. Empirical results – CCR projection review • Project: it is to submit DMU appropriated input allocation, and to meet stabilized efficiency. • Result: • No 1, 4,5,6,11,12,13,15,16&17 are found insufficient, should review all inputs to ensure aggregate efficiency meet 1.0.

  19. Discussion • All factories operated at the high level of pure technical efficiency in 2009 (BBC data). • All aggregate efficiency factories are also technically efficient in BCC

  20. Discussion

  21. conclusion

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