Suggestion

In real situation for improving GWSM performance, we suggest that MND needs focus on the future priorities as follows:

1. Sales Promotion:

Even though GWSM services for specific customers, it still needs for attracting the people’s sighting and purchasing desire. Because of the competition by civilian’s big sales market such as Kmart, Carrefour, RT-Mart, MATSUSEI etc., customers want to compare the price, quality of products with the big market store. If GWSM does not use the fancy way to attract and maintain the customers, they will be closed very soon because no people want to walk in GWSM.

2. Enhance Quality Control Process:

Because the living standard of military already promoted in recent years, the customers do not care the little price difference but they do more care about the quality of merchandise. So GWSM needs effectively control over the suppliers’ merchandise, it can fit normal standards fresh, good looking and high quality, that we can hold the customers for a long time. If customers met one time for buying an unqualified products, they will never walk in your store again.

3. Integrated sale market conditions:

Integrating GWSM’s marketing information and avoiding duplicated investments, different area has different operating strategies. For example, GWSM in Taipei, the merchandises need sale delicate products, otherwise, it will lose the competition powers, but in low income areas, it should sale par goods, if not, GWSM will threaten the customers. Therefore, MND should integrate each GWSM conditions and share the information to improve operation efficiency.

4. Merge and deactivate the inefficient GWSM:

MND should refer to the GWSM efficiencies by above research, then; can decide which retail store should merge and deactivate because of the poor efficiency, bad location, and low competition. MND can relocated the resources and maintain the efficient stores, therefore, GWSM can survive in the future and support for military soldiers (including cadets in military academies), reservists, veterans and their dependents.

A further investigation would be the examination of performance over time (panel data) by using the Malmquist productivity change index techniques. Such an approach would allow a dynamic view of the multidimensional performance of retail stores. It is also hoped that the models and methods implemented in this study can bring about other related researches to a variety of industries.

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Appendix A: Service-Satisfaction Questionnaire

國軍福利站滿意度問卷調查

親愛的顧客您好!

國軍福利站之設立宗旨是為服務勞苦功高的三軍同胞、軍事院校學生、退 伍榮民及上述人員眷屬，為提升福利站之服務品質以提高顧客滿意度，本研究 針對福利站之硬體設施及服務品質之滿意度，設計下列問卷，敬請親愛的顧客 能以您自身的體驗填答下列問卷，俟問卷分析完畢後，即將及結果交送福利總 處，供其作為改善設施及服務品質之參考，再次感謝您的協助。

敬祝 購物愉快

交通大學管理學院管理科學系 敬上 指導教授: 楊千

研究生: 王宗誠，盧文民

Eail:jamesw0728@yahoo.com.tw

第一部分 基本資料

1. 性別：

□ 男 □ 女 2. 年齡：

□18 歲~30 歲 □30~50 歲 □50~60 歲 □60~歲以上 3. 職業：

□軍公教人員 □軍校學生 □ 榮民 □ 眷屬 □ 其他 4. 每月收入：

□ 10,000 元以下 □ 10,001~30,000 元 □ 30,001~50,000 元 □ 50,001~100,000 元 □ 100,000 元以上

5. 教育程度：

□ 小學 □ 國中 □ 高中、職 □ 大專院校 □ 研究所以上

第二部份 消費狀況

1.請問您會到福利站消費的原因

□較近□服務項目多樣化□商品的多樣化□服務度態度 □較便宜 □其他_____

2.請問您多久到福利站去消費

□每週一次□每週 2-3 次□每月一次以上 □其他_____

3.您平均每次到福利站消費的金額

□300 以下 □500 以下 □1000 以下 □2000 以下 □2000 以上 4.下列幾家販售商店,您比較喜歡到哪一家便利商店消費(請按照優先順

填寫)

______福利站 ______家樂福______大潤發______松青_____愛買 5. 大潤發與松青一天經營 24 小時對你來說有比較方便嗎?

□ 沒差 □方便 □非常方便 6. 你覺得上述超商會取代福利站嗎?

□會 □不會 □不知道 7.你滿意目前福利站整體提供的服務嗎?

□滿意 □不滿意 □沒意見 8.你覺得福利站有需要改進的地方嗎?

非常滿意

滿意

無意見

不太滿意

很不滿意

第三部份 服務滿意度

1、店員的親切度 □1 □2 □3 □4 □5 2、店員的衣著及服儀 □1 □2 □3 □4 □5 3、店員的結帳速度 □1 □2 □3 □4 □5 4、商品是否多樣化--包含熱食、飲料、零食 □1 □2 □3 □4 □5

、蔬果，生鮮及日用品等等

5、商品是否新鮮每天是否定時更新食品 □1 □2 □3 □4 □5 6、是否經常推出特惠商品 □1 □2 □3 □4 □5 7、過年過節之禮盒及禮品供應式樣是否滿意 □1 □2 □3 □4 □5 8、商品是否維持良好品質及外觀 □1 □2 □3 □4 □5 9、商品退換手續是否簡便，服務員態度是否 □1 □2 □3 □4 □5 良好

10、商品價格與大賣場比是否具競爭力 □1 □2 □3 □4 □5

設施滿意度

1、福利站是否座落在住家、辦公室的附近 □1 □2 □3 □4 □5 2、汽機車停車是否方便 □1 □2 □3 □4 □5 3、是否有接駁轉運之服務 □1 □2 □3 □4 □5 4、福利站外觀是否明顯美觀 □1 □2 □3 □4 □5 5、是否設置儲物箱或物品代管之服務 □1 □2 □3 □4 □5 6、商場面積是否足夠與舒適 □1 □2 □3 □4 □5 7、內部動線設計是否順暢舒適 □1 □2 □3 □4 □5 8、商品陳列是否整齊及以相同商品歸類陳列 □1 □2 □3 □4 □5 9、商場內光線是否充足 □1 □2 □3 □4 □5 10 整體設施及地面是否清潔舒適 □1 □2 □3 □4 □5

感謝您接受我們的問卷調查，更謝謝您的寶貴意見！

APPENDX B: Ranking Extensions to DEA Model

1. Super Efficiency (Andersen and Petersen, 1993)

Andersen and Petersen (1993) developed a new procedure for ranking efficient units.

The methodology enables an extreme efficient unit to achieve an efficiency score greater than one by removing the constraint in the multiplier model, as shown in model (a.1).

k

The dual formulation of the super-efficient model, as seen in model (a.2), computes the distance between the Pareto frontier, evaluated without unit , and the unit itself i.e. for k

{

^{1, , ,}

}

However, there are two problematic areas with this methodology. First, the super-efficient methodology can give “specialized” DMUs an excessively high ranking (Sueyoshi, 1999).

The second problem lies with an infeasibility issue, which if it occurs, means that the super-efficient technique cannot provide a complete ranking of all DMUs (Seiford and Zhu, 1999).

2. Cross-Evaluation (Doyle and Green, 1994)

The cross-evaluation matrix was first development by Sexton et al. (1986), inaugurating the subject of ranking in DEA. Indeed, as Doyle and Green (1994) argued, decision-makers do not always have a reasonable mechanism from which to choose assurance regions, thus they recommend the cross-evaluation matrix for ranking units. The basic idea is to use DEA in a peer-appraisal instead of a self-appraisal, which is calculated by the CRS (constant returns to scale) model. A peer-appraisal means that the efficiency score of a is achieved when evaluated with the optimal weights (input and output weights obtained by the output-oriented CRS model) of other . Thus, for each there are

DMU

DMUs DMU

(

ⁿ⁻¹

)

cross-efficiency scores where n represents the total number of . Averaging the cross-efficiency scores of by using the weighting scheme of other , we can compute the mean cross-efficiency score of by the following formulation:

DMUs

Here, CEM_k^Mean becomes an index for effectively differentiating between good and poor performers. Thus, the performer of the can be ranked based on mean cross-efficiency scores. Table A1 summaries a generalized CEM. The row and the

column represent the efficiency measure of DMU by the optimal weights for DMU DMUs

zth

kth k

z (E_zk).

As indicated by Baker and Talluri (1997), a limitation of the CEM evaluated from the classic DEA model is that input/output weights (optimal weights) obtained from this formulation may not be unique. This condition occurs if multiple optimum solutions exist,

because one scheme can be favorable to one DMU and not favorable to another, or vice versa.

Doyle and Green (1994) propose aggressive and benevolent formulations to solve this ambiguity. Doyle and Green not only maximize the efficiency of the target , but also take a second goal into account. This second goal, in the case of aggressive formulation, minimizes the efficiency of the composite constructed from . The outputs and inputs of a composite are obtained by summing the corresponding outputs and inputs of all the other except the target . The weights obtained from this formulation make the efficiency of the target the best that it can be, and all other are the worst. Thus, the CEM in Eq. (a4), which is evaluated from these weights, is

The aggressive formulation is generally used when relative dominance among the is to be identified. The formulation is shown below:

∑ ∑

is the weighted output of composite

, is the weighted input of composite , and

of obtained from Eq. (1). The benevolent formulation uses the same set of constraints except that the efficiency of the composite is maximized. As reported by Angulo-Meza and Lins (2002), these two formulations give very similar results, which is why only one of these formulation is used, generally the aggressive formulation.

DMUk

DMU

A DMU potentially becomes as ‘false positive’ when it is exhibiting a high efficiency score by heavily weighting on a few favorable inputs and outputs. The self-appraisal and peer-appraisal are used in computing a false positive index ( ) (Baker and Talluri, 1997).

The FPI relates to the percentage increment in efficiency that a achieves when moving from peer-appraisal to self-appraisal. This FPI is similar to the maverick index suggested by Doyle and Green (1994). It is calculated by using Eq. (a5). The higher the value of is, the more ‘false positive’ the will be. FPI is defined as:

FPI

DMU

FPIk DMU_k

(

^Mean

) (

^Mean

)

k kk k k

FPI = θ −CEM CEM , (a5)

where θ_kk is the self-appraisal efficiency of DMU_k , and CEM_k^Mean is the mean cross-efficiency score of DMU_k.

Table A1 A Generalized Cross-Efficiency Matrix

Rated DMU

Rating DMU

1 2 3 k n

1 E₁₁ E₁₂ E₁₃ E_1k E_1n

2 E ₂₁ E ₂₂ E ₂₃ E _2k E _2n

3 E ₃₁ E ₃₂ E ₃₃ E _3k E _3n

z E _z1 E _z2 E _z3 E _zk E _zn

n E _n1 E _n2 E _n3 E _nk E _nn

CEMMean E_•1 E_•2 E_•3 E_•k E_•n

3. Infeasibility of Super-Efficiency Model (Seiford and Zhu, 1999)

Seiford and Zhu (1999) presents super efficiency VRS (SE-VRS) model. The SE-VRS model is based on based on a reference technology constructed from all other DMUs. The super efficiency of DMU k is evaluated by solving the LP problem below:

* fails to recognize that the output-oriented SE-CRS model is always feasible for the trivial solution which has all variables set equal to zero. Moreover, Zhu (1996b) shows that the input-oriented SE-CRS model is infeasible if and only if a certain pattern of zero data occurs in the inputs and outputs. Figure A1 illustrates how the SE-VRS model works the infeasibility for the case of a single output and a single input case. We have three VRS frontier DMUs, A, B, and C. AB exhibits IRS and BC exhibits DRS. The SE-VRS model evaluates point B by reference to B’ and B” on section AC through output-reduction and input-increment, respectively. In an input-oriented SE-VRS model, point A is evaluated against A’. However, there is no referent DMU for point C for input variations. Therefore, the input-oriented SE-VRS model is infeasible at point C. Similarly, in an output-oriented

SE-VRS model, point C is evaluated against C’. However, there is no referent DMU for point A for output variations. Therefore, the output-oriented SE-VRS model is infeasible at point A. Note that point A is the left most end point and point B is the right most end point on this frontier.

Figure A1 Infeasibility of Super-Efficiency Model

4. A Multiple Objective Approach (Li and Reeves, 1999)

Li and Reeves (1999) present a multiple objective approach that they called Multiple Criteria DEA – MCDEA, which focuses on solving two key problems: lack of discrimination and inappropriate weighting schemes. MCDEA introduces three objective functions into a LP problem. The first objective function seeks minimization of the inefficiency of a target DMU k, measured by , such that the weighted sum of outputs is less than or equal to the weighted sum of inputs for each DMU. Thus, we can say that DMU k is not efficient its efficiency score would be

dk

k k = 1−d

θ . The second objective function aims at the minimization of the maximum deviation, for which the restriction included in the new formulation, , makes M the maximum deviation. The third objective function seeks maximization of the deviation of all DMUs. All three objective functions are based on the deviation variable. The LP problem is as follows:

)

5. Non-Oriented Super-SBM model (Tone, 2002)

In most DEA models, the best performers share the full efficient status denoted by the

In most DEA models, the best performers share the full efficient status denoted by the

在文檔中 經營績效評估之綜合管理架構研究-應用於國軍零售供應站 (頁 53-76)