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

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

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

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.

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.

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.

Previous studies

Author Content

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., 2006 Rank order data in DEA

Liang, L., Yang, F., Cook, W.D., Zhu, J., 2006

DEA models for supply chain efficiency evaluation.

Kao, C., 2009 Efficiency decomposition in network data envelopment analysis

Methodology – CCR application

Will scan and paste the formula of CCR

Methodology – BCC application

Will scan and paste the BCC formula

Define DMU Selection of Input/Output Data collection & sorting

Decision of evaluation model Model performing Result & 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 i

nputs and plus outputs.

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 (%)

1 274 57480 3125 60 5748 1050 21.07

2 4800 5432507 30000 60 4346006 29350 75.83

3 1001 1721286 15600 60 516386 14300 75.25

4 3700 424850 30000 60 50982 15000 43.05

5 1201 796742 15600 60 278860 12560 61.67

6 1920 16254 15600 60 8127 10370 35.42

7 803 266385 15600 60 18647 7250 22.68

8 1944 1665896 23400 60 1665896 20500 64.58

9 242 3087390 3125 60 2315543 1320 44.05

10 1782 246759 23400 60 204070 20340 62.1

11 1750 808762 23400 60 647010 10520 22.68

12 515 1042458 6250 60 625475 1350 29.25

13 475 1088104 6250 60 489647 1205 26.78

14 196 105829 3125 60 79372 3120 83.44

15 1291 1445794 15600 60 1012056 12500 60.24

16 1001 2374201 15600 60 1234585 9240 56.42

17 1141 1500364 15600 60 1250307 7560 41.67

18 1416 1565574 15600 60 1330738 15005 70

(I) means Input / (O) means Output

Decision of input and output items DMU Unit Definitions

(I) Factory scope Men/factory Factory size by defining of total number of employees.

(I) Capacities Pieces/year Total annual pieces of products factory can produce for all buyers in site of factory scope

(I) Budget USD/project Investment 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 capacities Pieces/year Actual annual pieces of products that factory has produced and shipped in site of factory scope

(O) Cost spent USD/project The 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.

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 (%)

Max 4800 5432507 30000 60 4346006 29350 83.44

Min 196 16254 3125 60 5748 1050 21.07

Average 1414 1313701.94 15381.9444 60 893303.056 10696.667 49.787778

SD 1158.8349 1291714.58 8293.59193 0 1053788.99 7577.9298 19.900495

Correlation

Factory scope (men)

Capacities (pcs)

Budget (USD)

Expected KPI(%)

Actual capacities (pcs)

Cost spent (USD)

Actual KPI (%)

Factory scope (men) 1 0.45425571 0.88166096 0 0.49658447 0.8320039 0.2627265

Capacities (pcs) 0.4542557 1 0.26061932 0 0.96601914 0.4501003 0.3853056

Budget (USD) 0.881661 0.26061932 1 0 0.29292014 0.8741119 0.2339846

Expected KPI(%) 0 0 0 1 0 0 0

Actual capacities (pcs) 0.4965845 0.96601914 0.29292014 0 1 0.4922845 0.368006

Cost spent (USD) 0.8320039 0.45010032 0.87411194 0 0.49228445 1 0.5692654

Actual KPI (%) 0.2627265 0.3853056 0.23398462 0 0.36800599 0.5692654 1

• The correlation factor of inputs and outputs are looked positive, and enough to be used for determining of DEA analysis.

Empirical results – CCR & BCC analysisDMU

DEA - CCR Score (aggregate efficiency)

DEA - BCC Score (technical efficiency)

Scale efficiency RTS RTS of Projected DMU

1 0.445232438 1 0.445232438 Increasing

2 1 1 1 Constant

3 1 1 1 Constant

4 0.72641248 1 0.72641248 Constant

5 0.88874534 1 0.88874534 Decreasing

6 1 1 1 Constant

7 0.733881193 1 0.733881193 Constant

8 1 1 1 Constant

9 1 1 1 Constant

10 1 1 Constant

11 0.802503468 1 0.802503468 Constant

12 0.719495481 1 0.719495481 Constant

13 0.548935517 1 0.548935517 Constant

14 1 1 1 Constant

15 0.874853086 1 0.874853086 Constant

16 0.868527062 1 0.868527062 Constant

17 0.91946544 1 0.91946544 Constant

18 1 1 1 Constant

Empirical results – CCR & BCC analysisFrequency in Reference Set (CCR)

Reference Frequency to other DMUs

2 1

3 4

6 2

8 5

9 5

10 3

14 8

18 3

Frequency in Reference Set (BCC)

Reference Frequency to other DMUs

1 0

2 4

3 0

6 0

8 5

9 4

10 3

14 9

18 3

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

Empirical results – CCR projection reviewNo. DMU Score

I/O Data Projection Difference %

1 1 0.44523244

Factory scope (men) 274 121.993688 -152.00631 -55.48%

Capacities (pcs) 57480 25591.9605 -31888.039 -55.48%

Budget (USD) 3125 1352.50313 -1772.4969 -56.72%

Expected KPI(%) 60 16.5139505 -43.486049 -72.48%

Actual capacities (pcs) 5748 19033.6387 13285.639 231.13%

Cost spent (USD) 1050 1144.89065 94.89065 9.04%

Actual KPI (%) 21.07 21.07 0 0.00%

4 4 0.72641248


Capacities (pcs) 424850 308616.342 -116233.66 -27.36%

Budget (USD) 30000 17162.7005 -12837.299 -42.79%

Expected KPI(%) 60 43.5847488 -16.415251 -27.36%


Cost spent (USD) 15000 15000 0 0.00%

Actual KPI (%) 43.05 45.4527338 2.4027338 5.58%

5 5 0.88874534


Capacities (pcs) 796742 708100.74 -88641.26 -11.13%

Budget (USD) 15600 13864.4273 -1735.5727 -11.13%

Expected KPI(%) 60 53.3247204 -6.6752796 -11.13%


Cost spent (USD) 12560 12560 0 0.00%

Actual KPI (%) 61.67 63.1969722 1.5269722 2.48%

Empirical results – CCR projection review I/O Data Projection Difference %

7 7 0.73388119


Capacities (pcs) 266385 195494.942 -70890.058 -26.61%

Budget (USD) 15600 8098.78449 -7501.2155 -48.08%

Expected KPI(%) 60 44.0328716 -15.967128 -26.61%


Cost spent (USD) 7250 7250 0 0.00%

Actual KPI (%) 22.68 55.3750675 32.695068 144.16%

11 11 0.80250347


Capacities (pcs) 808762 649034.309 -159727.69 -19.75%

Budget (USD) 23400 12945.5061 -10454.494 -44.68%

Expected KPI(%) 60 38.1752946 -21.824705 -36.37%

Actual capacities (pcs) 647010 647010 0 0.00%

Cost spent (USD) 10520 10520 0 0.00%

Actual KPI (%) 22.68 33.826039 11.146039 49.14%

12 12 0.71949548


Capacities (pcs) 1042458 750043.82 -292414.18 -28.05%

Budget (USD) 6250 4496.84675 -1753.1532 -28.05%

Expected KPI(%) 60 27.5072875 -32.492712 -54.15%


Cost spent (USD) 1350 3775.85163 2425.8516 179.69%

Actual KPI (%) 29.25 29.25 0 0.00%


13 13 0.54893552


Capacities (pcs) 1088104 597298.932 -490805.07 -45.11%

Budget (USD) 6250 3299.38243 -2950.6176 -47.21%

Expected KPI(%) 60 24.3965028 -35.603497 -59.34%


Cost spent (USD) 1205 2767.29622 1562.2962 129.65%

Actual KPI (%) 26.78 26.78 0 0.00%

15 15 0.87485309


Capacities (pcs) 1445794 1264857.34 -180936.66 -12.51%

Budget (USD) 15600 13647.7081 -1952.2919 -12.51%

Expected KPI(%) 60 52.4911852 -7.5088148 -12.51%


Cost spent (USD) 12500 12500 0 0.00%

Actual KPI (%) 60.24 61.5590035 1.3190035 2.19%

16 16 0.86852706


Capacities (pcs) 2374201 1599760.07 -774440.93 -32.62%

Budget (USD) 15600 10021.3988 -5578.6012 -35.76%

Expected KPI(%) 60 52.1116237 -7.8883763 -13.15%


Cost spent (USD) 9240 9240 0 0.00%

Actual KPI (%) 56.42 56.42 0 0.00%


17 17 0.91946544


Capacities (pcs) 1500364 1379532.85 -120831.15 -8.05%

Budget (USD) 15600 12671.5861 -2928.4139 -18.77%

Expected KPI(%) 60 41.7186857 -18.281314 -30.47%


Cost spent (USD) 7560 10868.3094 3308.3094 43.76%

Actual KPI (%) 41.67 41.67 0 0.00%

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.

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

Discussion

conclusion

Documents

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

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