1. 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 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

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 AuthorContent 高強、黃旭南、 Toshiyuki Sueyoshi (2003) Management Performance Evaluation Data Envelopment Analysis Haritha Saranga; Roger Moser (2010) Performance evaluation of purchasing and supply management using DEA approach Cook, W.D., Zhu, J., 2006Rank order data in DEA Liang, L., Yang, F., Cook, W.D., Zhu, J., 2006 DEA models for supply chain efficiency evaluation. Kao, C., 2009Efficiency decomposition in network data envelopment analysis

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

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

9. Define DMU Selection of Input/Output Data collection & sorting Decision of evaluation model Model performingResult & explanations Analysis Procedure 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.

10. DMU Selection No of Factory (I) Factory scope (men) (I) Capacities (pcs) (I) Budget (USD) (I) Expected KPI(%) (O )Actual capacities (pcs) (O) Cost spent (USD) (O ) Actual KPI (%) 1274574803125605748105021.07 248005432507300006043460062935075.83 31001172128615600605163861430075.25 437004248503000060509821500043.05 5120179674215600602788601256061.67 6192016254156006081271037035.42 7803266385156006018647725022.68 819441665896234006016658962050064.58 924230873903125602315543132044.05 10178224675923400602040702034062.1 11175080876223400606470101052022.68 125151042458625060625475135029.25 134751088104625060489647120526.78 1419610582931256079372312083.44 1512911445794156006010120561250060.24 161001237420115600601234585924056.42 171141150036415600601250307756041.67 1814161565574156006013307381500570 (I) means Input / (O) means Output

11. Decision of input and output items DMUUnitDefinitions (I) Factory scopeMen/factoryFactory size by defining of total number of employees. (I) CapacitiesPieces/year Total annual pieces of products factory can produce for all buyers in site of factory scope (I) BudgetUSD/projectInvestment of project, required by buyer its (I) Expected KPI% A given evaluation score will be expected and conducted by buyers to view the implementation of engaged project (O) Actual capacitiesPieces/year Actual annual pieces of products that factory has produced and shipped in site of factory scope (O) Cost spentUSD/projectThe cost has actual used to meet buyer requirement of project. (O) Actual KPI%A performance has evaluated by buyer and score is given 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 Statistics on Input/Output Data Factory scope (men) Capacities (pcs) Budget (USD) Expected KPI(%) Actual capacities (pcs) Cost spent (USD) Actual KPI (%) Max48005432507300006043460062935083.44 Min196162543125605748105021.07 Average14141313701.9415381.944460893303.05610696.66749.787778 SD1158.83491291714.588293.5919301053788.997577.929819.900495 Correlation Factory scope (men) Capacities (pcs) Budget (USD) Expected KPI(%) Actual capacities (pcs) Cost spent (USD) Actual KPI (%) Factory scope (men)10.454255710.8816609600.496584470.83200390.2627265 Capacities (pcs)0.454255710.2606193200.966019140.45010030.3853056 Budget (USD)0.8816610.26061932100.292920140.87411190.2339846 Expected KPI(%)0001000 Actual capacities (pcs) 0.49658450.966019140.29292014010.49228450.368006 Cost spent (USD)0.83200390.450100320.8741119400.4922844510.5692654 Actual KPI (%)0.26272650.38530560.2339846200.368005990.56926541 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 DMU DEA - CCR Score (aggregate efficiency) DEA - BCC Score (technical efficiency) Scale efficiencyRTS RTS of Projected DMU 10.4452324381 Increasing 2111Constant 3111 40.726412481 Constant 50.888745341 Decreasing 6111Constant 70.7338811931 Constant 8111 9111 1011 Constant 110.8025034681 Constant 120.7194954811 Constant 130.5489355171 Constant 14111Constant 150.8748530861 Constant 160.8685270621 Constant 170.919465441 Constant 18111Constant

14. Empirical results – CCR & BCC analysis Frequency in Reference Set (CCR) ReferenceFrequency to other DMUs 21 34 62 85 95 103 148 183 Frequency in Reference Set (BCC) ReferenceFrequency to other DMUs 10 24 30 60 85 94 103 149 183 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 No.DMUScore I/ODataProjectionDifference % 110.44523244 Factory scope (men)274121.993688-152.00631-55.48% Capacities (pcs)5748025591.9605-31888.039-55.48% Budget (USD)31251352.50313-1772.4969-56.72% Expected KPI(%)6016.5139505-43.486049-72.48% Actual capacities (pcs)574819033.638713285.639231.13% Cost spent (USD)10501144.8906594.890659.04% Actual KPI (%)21.07 00.00% 440.72641248 Factory scope (men)37001369.75631-2330.2437-62.98% Capacities (pcs)424850308616.342-116233.66-27.36% Budget (USD)3000017162.7005-12837.299-42.79% Expected KPI(%)6043.5847488-16.415251-27.36% Actual capacities (pcs)50982251566.81200584.81393.44% Cost spent (USD)15000 00.00% Actual KPI (%)43.0545.45273382.40273385.58% 550.88874534 Factory scope (men)12011067.38315-133.61685-11.13% Capacities (pcs)796742708100.74-88641.26-11.13% Budget (USD)1560013864.4273-1735.5727-11.13% Expected KPI(%)6053.3247204-6.6752796-11.13% Actual capacities (pcs)278860449192.414170332.4161.08% Cost spent (USD)12560 00.00% Actual KPI (%)61.6763.19697221.52697222.48%

16. Empirical results – CCR projection review I/ODataProjectionDifference % 770.73388119 Factory scope (men)803589.306598-213.6934-26.61% Capacities (pcs)266385195494.942-70890.058-26.61% Budget (USD)156008098.78449-7501.2155-48.08% Expected KPI(%)6044.0328716-15.967128-26.61% Actual capacities (pcs)18647112214.06593567.065501.78% Cost spent (USD)7250 00.00% Actual KPI (%)22.6855.375067532.695068144.16% 11 0.80250347 Factory scope (men)17501230.90152-519.09848-29.66% Capacities (pcs)808762649034.309-159727.69-19.75% Budget (USD)2340012945.5061-10454.494-44.68% Expected KPI(%)6038.1752946-21.824705-36.37% Actual capacities (pcs)647010 00.00% Cost spent (USD)10520 00.00% Actual KPI (%)22.6833.82603911.14603949.14% 12 0.71949548 Factory scope (men)515361.219349-153.78065-29.86% Capacities (pcs)1042458750043.82-292414.18-28.05% Budget (USD)62504496.84675-1753.1532-28.05% Expected KPI(%)6027.5072875-32.492712-54.15% Actual capacities (pcs)625475 00.00% Cost spent (USD)13503775.851632425.8516179.69% Actual KPI (%)29.25 00.00%

17. Empirical results – CCR projection review I/ODataProjectionDifference % 13 0.54893552 Factory scope (men)475260.74437-214.25563-45.11% Capacities (pcs)1088104597298.932-490805.07-45.11% Budget (USD)62503299.38243-2950.6176-47.21% Expected KPI(%)6024.3965028-35.603497-59.34% Actual capacities (pcs)489647 00.00% Cost spent (USD)12052767.296221562.2962129.65% Actual KPI (%)26.78 00.00% 15 0.87485309 Factory scope (men)12911129.43533-161.56467-12.51% Capacities (pcs)14457941264857.34-180936.66-12.51% Budget (USD)1560013647.7081-1952.2919-12.51% Expected KPI(%)6052.4911852-7.5088148-12.51% Actual capacities (pcs)1012056 00.00% Cost spent (USD)12500 00.00% Actual KPI (%)60.2461.55900351.31900352.19% 16 0.86852706 Factory scope (men)1001869.395589-131.60441-13.15% Capacities (pcs)23742011599760.07-774440.93-32.62% Budget (USD)1560010021.3988-5578.6012-35.76% Expected KPI(%)6052.1116237-7.8883763-13.15% Actual capacities (pcs)1234585 00.00% Cost spent (USD)9240 00.00% Actual KPI (%)56.42 00.00%

18. Empirical results – CCR projection review I/ODataProjectionDifference % 17 0.91946544 Factory scope (men)11411049.11007-91.889933-8.05% Capacities (pcs)15003641379532.85-120831.15-8.05% Budget (USD)1560012671.5861-2928.4139-18.77% Expected KPI(%)6041.7186857-18.281314-30.47% Actual capacities (pcs)1250307 00.00% Cost spent (USD)756010868.30943308.309443.76% Actual KPI (%)41.67 00.00% 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