Determining the optimal collaborative benchmarks in a supply chain

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Determining the optimal collaborativebenchmarks in a supply chainDer-Chiang Li a & Wen-Li Dai aa Department of Industrial and Information Management ,National Cheng Kung University , Tainan, TaiwanPublished online: 03 Jun 2009.

To cite this article: Der-Chiang Li & Wen-Li Dai (2009) Determining the optimal collaborativebenchmarks in a supply chain, International Journal of Production Research, 47:16, 4457-4471, DOI:10.1080/00207540801918588

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International Journal of Production ResearchVol. 47, No. 16, 15 August 2009, 4457–4471

Determining the optimal collaborative benchmarks in a supply chain

Der-Chiang Li* and Wen-Li Dai

Department of Industrial and Information Management,National Cheng Kung University, Tainan, Taiwan

(Received 8 August 2007; final version received 4 January 2008)

Supply chains are currently considered dynamic systems and will change withtime and with the environment. Thus, the performance of a supply chain systemwill not only be influenced by the determined measuring factors but also by theadding or withdrawal of enterprises. Faced with these unstable systems, this studyemploys the Data Envelopment Analysis and sensitivity analysis methods inorder to measure supply chain collaborative performance as well as relativeindividual company performance. This approach principally aims at finding:(1) an effective collaborative performance evaluation method that can cover themeasuring factors admitted by all chain members; (2) the robust benchmarkcompanies for closely related chain members. The results of this research clearlyshow the expected study target that will benefit chain members in performanceimprovement.

Keywords: supply chain collaboration performance; data envelopment analysis;sensitivity analysis; robust benchmarks

1. Introduction

In order for supply chain management to succeed, it must function cooperatively amongcompanies because it is basically a team-work-oriented system. By sharing informationregarding demand forecasting, production planning, and new technology development,members in a supply chain can support one another and learn from each other. Thisis generally believed to be a significant chain performance improvement mechanism.Provided this concept is true, an effective method to measure chain performance becomescritical.

Taylor (2004) suggested that supply chain performance measurement could be initiatedby examining characteristics of customer demand, internal implementation, manufacturersupply, and the information environment. Furthermore, the measurement indicatorsin this study include time, cost, efficiency, and trade results. However, in practice, it isdifficult to find a method which is objective and effective to measure supply chainperformance because in a supply chain, members often do not have agreement on themeasurement criteria. The Data Envelopment Analysis method is therefore uniquelyemployed here to systematically and objectively asses the overall chain performance andeach business’s ability in the supply chain. In addition, in order to deal with the unstable

*Corresponding author. Email: [email protected]

ISSN 0020–7543 print/ISSN 1366–588X online

� 2009 Taylor & Francis

DOI: 10.1080/00207540801918588

http://www.informaworld.com

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properties of the business environment, sensitivity analysis is used to find out therobustness of business performance.

As to an enterprise collaborative mechanism, it can usually function well only in asupply chain and in closely related businesses (Danese 2006). Through cooperativeemulation and learning among members in a chain, the mechanism produces positiveeffects in the coordination planning processes, prediction, purchasing, and goodsmending procedures as well as in the discovery of the bullwhip effect fluctuation point.Again, it is emphasised that members having similar ideas, abilities, and learningmechanisms can mature together and create innovations among customers, manufac-turers, and suppliers (Lyu 2007).

Therefore, the main purpose of this study was to find the optimal collaborativebenchmarks in businesses. The Data Envelopment Analysis method was used forperformance measurement; and the sensitivity analysis was used to find the steadybenchmarks in an industry. In addition, because the improvement attributes or thefocal points in different businesses may be different, this study classifies businesses intoclusters (eight clusters) in the experimental example in advance. By observing the results ofperformance measurements, those companies with inferior performance in a businessclass can find the best benchmark company for learning.

The remainder of this paper is organised as follows: the relevant literature is describedin the next section; Section 3 discusses the choice of the factors for performanceevaluation and sensitivity analysis. Section 4 provides the results of the experimental case.The conclusion is given in Section 5.

2. Literature review

There are a few published papers on supply chain performance measurement. The partsof basic concepts for sensitivity analysis contain references to Charnes et al. (1985) andCharnes and Neralic (1989).

2.1 Performance evaluation methods

The performance evaluation methods used were as follows (Li and Dai 2007):(1) questionnaires were used to ask for middle- or high-level executives’ suggestions,and multivariable analysis on the interview data was performed. The analysis summed upseveral important factors that influenced the performance and the relationship between thefactors. The shortcoming of using a questionnaire was the subjectivity of managers,who have different professional backgrounds, and thus different views (Bowersox et al.1999, Clossa and Mollenkopf 2004). (2) The balance score card (BSC) method was usedwith the advantage of being able to coordinate the sections in an organisation for strategicplans and goals. The shortcoming was that a structured field was restricting, makingit difficult to present the processes and results in detail (Kaplan and Norton 1992).(3) The supply chain operating reference (SCOR) model was used, which set up acommittee to provide successive self-criticism and methods for upgrading chainoperations. The shortcoming was that it was time-consuming and subjective, and itwas often difficult to reach a common understanding (Saccomano 1998, Stedman 1998).(4) The traditional performance measurement uses costs, profits, earning capacity, andproductivity and utilisation rates as the criteria to measure performance. The shortcoming

4458 D.-C. Li and W.-L. Dai

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is the inclination to analyse a short-term programme within limited aspects because a good

performance measurement method should be able to analyse a chain with wider

perspectives (Wisner et al. 2005). (5) The real performance was compared with the

standard performance. The shortcoming was that the setting of standards was very time-

consuming. Managers might also try to remedy the difference between the two

performances, making the results not reflective of the company’s true operating conditions

(Saccomano 1998, Stedman 1998).

2.2 Purpose and procedure of supply chain evaluation

Lu and Zhou (2004) pointed out that the main purpose of performance measurement

was to use a structured process to evaluate supply chain performance to help

enterprises understand their own supply chain situations and to set up a common

understanding for supply chain management. Lu and Zhou (2004) pointed out that a

supply chain evaluation procedure could be summarised as follows: (1) investigate the

chain and set up the efficiency indicators of a supply chain; (2) find the main

operational or structural questions; (3) conduct cause and effect analysis for the

problems; (4) find a way to strengthen the efficiency; (5) find a way to strengthen the

present situation.

2.3 Decision factors of supply chain performance evaluation

In understanding how enterprises improve supply chain performance, a few factors can be

considered. These factors are called drive factors. Chopra and Meindl (2004) pointed out

that the drive factors which influence supply chain performance include: (1) facilities – the

place for stocking, assembly, and production in the network. Decisions regarding these

facilities concern their locations, production capabilities, and their flexibility and have

a major impact on supply chain performance. (2) Stocking of the original materials,

materials in process, and finished products in supply chains – changes in the stock policy

could affect supply chain efficiency dramatically. (3) Transportation – the stock

movement along chain points. It influences the supply chain efficiency heavily.

(4) Information – the primary data exchange for relevant facilities, stock, transportation,

and customer analysis.

2.4 Data envelopment analysis

Gao et al. (2003) regarded the Data Envelopment Analysis method as the use of the Pareto

Optimality idea for evaluating relative efficiencies for a group of units. Charnes et al.

proposed this method in 1978, namely the CCR (Charnes, Cooper, and Rhodes) module,

using constant returns to scale; Banker et al. (1984), proposed another model using

variable remuneration, called the BCC (Banker, Cooper, and Coppet) module. Both

modules can be applied to input-oriented and output-oriented analyses. For input analysis,

the present output is fixed and inputs that make efficient outcomes are found; for output

analysis, the present input is fixed to measure the efficiency of the outputs. The production

efficiencies that both CCR and BCC modules try to measure include technological

efficiency and scale efficiency.

International Journal of Production Research 4459

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3. Collaborative performance evaluation

As supply chains are dynamic systems and will change with time and with changes in the

environment, the performance in the supply chain system will be influenced by the adding

or withdrawing of enterprises. Therefore, how to find a steady and reliable benchmark

business is important.Consequently, the goals of this study should include: (1) discovering which company is

a robust and effective benchmark; (2) determining whether a company that performs well

will change its stability after adding a new company or displacement of a company;

(3) assessing under conditions, the performance of the company of the former business was

not affected by companies adding or withdrawing; (4) determining which enterprise is the

correct and best benchmark. In order to answer these questions, this study proposes a

strategy that classifies companies first, and then uses the Data Envelopment Analysis

method to determine performance scores for every company, and follows this with a

sensitivity analysis that will find the correct robust benchmark companies. Note that the

above classifying part was not addressed in detail here (Li and Dai 2007).

3.1 Evaluating supply chain performance

The DEA method measures relative efficiency, rather than absolute efficiency, for

decision-making units, so when a decision-making unit is changed, the change of its

relative efficiency can be detected. More specifically, because the DEA calculate each

decision-making unit efficiency using a reference set (a group of referenced companies),

the change of the reference set will change the specific relative efficiency. The discussion of

this is referred to the sensitivity analysis in this study.In this study, both CCR and BCC modules are used for analysing the data because the

two models are recognised as the most influential models by researchers (Seiford 1996).

The CCR module supposes that the scale returns of production process is constant. That is

to say when the input increases; the output should also increase in the same proportion.

The BCC model supposes that the scale returns of the production process may not increase

or decrease according to the inputs. So through knowing the scale returns of each unit,

it can further improve the quality of analysis (Boussofiane et al. 1991).The formulas of CCR and BCC models are described mathematically as below:

(1) CCR model:

Suppose unit j uses i items as inputs, Xij ; and r items as outputs, Yrj, then the efficiency

of unit k is Ek (Gao et al. 2003). We use the following formula: (1), to calculate the

efficiency as:

Ek ¼Max

Psr¼1 urYrkPmi¼1 viXik

s:t:

Psr¼1 urYrjPmi¼1 viXij

� 1, j ¼ 1, . . . , n

ur, vi � "4 0, r ¼ 1, . . . , s; i ¼ 1, . . . ,m

ð1Þ

where " is a Non-Archimedean small number;where ur means the weight of rth output item;


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where vi means the weight of ith input item;where n means the decision-making unit number;where m means the input factor number;where r means the output item number.

(2) BCC model:

This model includes one extra positive or negative number, u0, to indicate the scale returns

of the production process; increase or decrease.The model calculates the efficiency using the following formula, (2):

Ek ¼Max

Psr¼1 urYrk � u0Pm

i¼1 viXik

s:t:

Psr¼1 urYrj � u0Pm

i¼1 viXij� 1, j ¼ 1, . . . , n

ur, vi � "4 0, r ¼ 1, . . . , s; i ¼ 1, . . . ,m

ð2Þ

where " is a Non-Archimedean small number;where ur means the weight of rth output item;where vi means the weight of ith input item;where n means the decision-making unit number;where m means the input factor number;where r means the output item number.

The used software is: Efficiency, Measurement, and Systems (EMS).

3.1.1 Data

A real supply chain in Taiwan supplying the PC and computer notebook industry with

parts and peripheral equipment is employed as the study object. This chain consists of

companies in classes from upper steam to lower steam as shown in Figure 1.The company data are divided into eight classes: PC, monitor, package substrate,

DRAM, power supply, thermal module, IC design, and wafer OEM industry, and the

most representative enterprises (companies making the biggest proportion of the total

production) were chosen in every class as the analysis target. A total of 50 companies are

included.This study focused on the information industry and the data were collected from

actual reports from companies in Taiwan. The data are collected from Taiwan Market

Observation Post System: http://newmops.tse.com.tw

Up stream Middle stream Low stream

IC design

DRAM

Wafer OEM

Monitor

Power supply

Package Substrate

Thermal module

PC

Figure 1. The research chain members.


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3.1.2 Selection of evaluation factors

By consulting Mollenkopf and Dapiran’s (1999) research, Clossa and Mollenkopf’s (2004)

research paper, including the influence variables and items related to logistic ability, and

Taylor’s (2004) items categorised for supply chain performance measurement, this research

analysed its influence factors from the enterprise’s practical operations and selected some

factors relating to operational efficiency and profit making. Furthermore, these factors

were divided into input and output items; among them the input items were total assets,

manpower, research and develop percentage, and research and develop cost. The output

items are average inventory turnover (times), return on total assets, average

inventory turnover days, and total assets turnover (times). Estimated item choice is

important. If the chosen item is in error, performance of the estimate may be influenced,

as Table 1 shows.According to the contents of Table 1, this research chooses 25 samples as the

evaluation item set. The results obtained using 50 samples were similar to those using

25 samples.

3.1.3 Reference set

The ‘reference set’ is the reference targets used while calculating the relative efficiency for

each unit. This is the basic design of a DEA model (Charnes et al. 1978).To explain this briefly, units’ c, d, and e in Figure 2 are the production frontiers in the

isoquant curve, which are considered efficient units. Unit b is an inefficient unit needing to

find a unit in the reference set as a learning benchmark, and c and d were selected because

the two frontiers are relatively close to this unit (Gao et al. 2003).

3.1.4 Result of DEA analysis for PC

As a partial result, the performances of the specific businesses are calculated, as shown in

Table 2.

3.2 Sensitivity analysis

To conduct sensitivity analysis, this study calculates performance using a company set

including 25 samples (old set), and adds another 25 samples later to form a set with

Table 1. Selection of evaluation items.

ItemAveragescore (%)

The chosen original item set 73.82Total assets are made into the fixed assets 73.46Delete the research and development cost 73.45Total assets are made into the fixed assetsand delete the research and development cost

73.15

Delete manpower 72.30Delete the rate of research and development 26.80


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50 samples (new set). Then we observe and compare the performance changes of these

two groups of companies in each business in order to find the robust benchmarks.

The 25 new companies added to the old set consist of firms belonging to businesses

covering eight classes. This research design aims at obtaining comprehensive sample

change sensitivity.The relevant sensitivity calculation procedure analysis is: (1) calculate the company’s

efficiencies for each of the two groups of companies for every business; (2) calculate the

total average efficiency per group of each business; (3) calculate the sensitivity for each

business using formula (3):

Sensitivity ¼score ðnewÞ � score ðoldÞ

score ðoldÞð3Þ

where new means the group of 50 companies; where old means the group of

25 companies.For example, after adding two new companies to the PC business, the performances of

the entire group of specific businesses will be influenced, as shown in Table 3, where the

average performance changed by �13.98%.

Figure 2. Production efficiency frontier curve.

Table 2. The result of DEA analysis for PC.

Sort DMUPerformancescore (%)

PC Firm1 100.00Firm2 90.75Firm3 87.19Firm4 100.00Firm5 87.50Firm6 100.00

Total average 94.24


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3.3 Benchmark determination

The ‘best benchmark’ means a stable reliable reference benchmark in one business class;the ‘reference benchmark’ means the companies in a business class which do well in the

performance evaluation but might not be the best benchmark.The steps of benchmark determination are:

Step 1: If a unit reaches a hundred per cent in both of the two groups of companies(25 and 50 groups) and belongs to the reference set in this business, then it is considered

the best benchmark.

Step 2: If there is a unit that reaches a hundred per cent in both of the two groups ofcompanies but is outside the reference set in this business, then this unit is considered

stable but not the best benchmark.

Step 3: If no unit reaches a hundred per cent in both groups of companies, but is acompany within the reference set in a business, then the one with the highest efficiency is

considered a reference benchmark, but not the best benchmark.

Step 4: If a company reference set has zero companies, this means that the performanceof this company will not be influenced by adding or withdrawing other companies and

it is therefore considered a stable company. If it is outside the reference set in this business,then the one with the highest efficiency is considered a benchmark, but not the best

reference benchmark.

4. Experiment implementation and computational results

This chapter practically exhibits the application of the DEA model to the performance andsensitivity analysis on the selected sample set. It includes a method for finding the best

benchmark for every business class and the companies capable of collaborative learning.

4.1 DEA module and sensitivity analysis

This study applied both the CCR (Charnes, Cooper, and Rhodes) and the BCC (Banker,Cooper, and Coppet) models in DEA to evaluate supply chain performance. Both models

Table 3. Business performance variation.

Sort DMU Score (new) (%) Score (old) (%) Variation (%)

PC Firm1 100.00 100.00 0.00Firm2 70.37 90.75 �20.38Firm3 77.78 87.19 �9.41Firm4 100.00 100.00 0.00Firm5 62.38 87.50 �25.12Firm6 72.20 100.00 �27.80Firm26(*) 65.79Firm27(*) 100.00

Average 81.07 94.24Sensitive �13.98


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produced consistent results. Note that they both need to use the input orientationcondition for DEA analysis and that the distance was set using the radial method.

4.1.1 CCR module

Table 4 demonstrates that the total efficiency score of the CCR module will be reduced(from 94.24% to 81.07%) because of adding the new companies, but the efficiency score ofa specific business is not completely reduced. As a result of adding new company samplesand the impact on individual business efficiency, the analysis can test the correct samplechoice and can measure the stability of the business. Table 4 demonstrates that thesensitivity score of Monitor, DRAM, and Wafer OEM are positive, and the sensitivities ofother businesses are negative.

4.1.2 BCC module

Similarly, Table 5 demonstrates that the total efficiency score of the BCC module will alsobe reduced because of adding new companies, but the specific efficiency scores ofbusinesses may not all decline. Table 5 demonstrates that the sensitivity score of Monitor,DRAM and Wafer OEM are positive, reflecting similar results to that of the CCR module.

4.2 Benchmark companies

Depending on the steps stated in Section 3.3, the selected benchmarks are as follows.The benchmark companies of the CCR module: Table 6 demonstrates that the best

benchmarks are firm1 and firm20 because both of the firms’ performance in the twogroups of data reach a hundred per cent and belong to the business reference sets in thebusinesses (firm1 is included in the PC reference set; firm20 is in the Thermal modulereference set). In addition, for the company reference sets firm4, firm43, and firm50 areall zero, so their performance will not be influenced because of adding new companiesto the set.

Using firm4 as an example, although both the performances in the two groups ofdata reach a hundred per cent, they are not inside the reference set for this business(business reference set), so they only can be referenced as a reference benchmark, and theyare not the best benchmark for these firms.

The benchmark companies of the BCC module: as shown in Table 7, the bestbenchmarks are firm1, firm3, firm4 and firm20. In addition, the company reference sets offirm6, firm11 and firm22 are all zero, so the influence of adding new companies will not besignificant for these firms.

By observing the results analysed in the above performance sensitivity analysis, onecan determine the best benchmarks. Firm1 and firm20 indicate that they are more stableenterprises by having excellent performance in both models. The reference sets aredifferent in the two models because one is using the fixed cost as one of the measuringfactors, and the other uses the variable costs. Practically, these two factors result indifferent amortisation and proportion of cost analysis.

5. Conclusions and future research

By sharing information with regard to demand forecasting, production planning, and newtechnology development, members in a supply chain can support or learn from each other.


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This is a significant chain performance improvement mechanism. However, becausesupply chains are dynamic systems and can change over time and with changes in theenvironment, the performance in the supply chain system will be influenced by addingor withdrawing enterprises. Therefore, establishing a method for finding a stable andreliable benchmark business is a significant challenge.

Table 4. CCR module sensitivity analysis.

Sort DMUScore

(new) (%)Score

(old) (%) Sort DMUScore

(new) (%)Score

(old) (%)

Panel (a)PC Firm1 100.00 100.00 Package Firm9 55.35 100.00

Firm2 70.37 90.75 substrate Firm10 54.32 59.52Firm3 77.78 87.19 Firm11 97.67 100.00Firm4 100.00 100.00 Firm31(*) 39.85Firm5 62.38 87.50 Firm32(*) 62.18Firm6 72.20 100.00 Firm33(*) 100.00Firm26(*) 65.79 Firm34(*) 100.00Firm27(*) 100.00

Average 81.07 94.24 Average 72.77 86.51Sensitivity �13.98 Sensitivity �15.88

Monitor Firm7 39.26 53.41 DRAM Firm12 16.38 16.51Firm8 29.54 42.33 Firm13 58.86 65.65Firm28(*) 51.60 Firm14 44.71 56.46Firm29(*) 74.52 Firm15 28.28 32.14Firm30(*) 69.41 Firm35(*) 3.33

Firm36(*) 100.00Firm37(*) 100.00

Average 52.87 47.87 Average 50.22 42.69Sensitivity 10.44 Sensitivity 17.65

Panel (b)Power Firm16 31.83 77.98 IC design Firm22 81.36 100.00supply Firm17 100.00 100.00 Firm23 18.43 31.96

Firm18 61.37 92.71 Firm41(*) 100.00Firm38(*) 100.00 Firm42(*) 25.80Firm39(*) 84.41 Firm43(*) 100.00

Firm44(*) 35.77Firm45(*) 27.61Firm46(*) 19.85Firm47(*) 80.21


Thermal Firm19 71.03 100.00 Wafer Firm24 19.58 30.61module Firm20 100.00 100.00 OEM Firm25 17.02 20.79

Firm21 88.71 100.00 Firm48(*) 27.18Firm40(*) 100.00 Firm49(*) 37.67

Firm50(*) 100.00

Average 89.94 100.00 Average 40.29 25.70Sensitivity �10.07 Sensitivity 56.77

All avg 64.63 73.82Sensitivity �12.46


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The Data Envelopment Analysis method is elaborative and is employed here tosystematically and objectively discover the collaborative chain performance and theindividual business’s capabilities. Further, sensitivity analysis is hybridised to discover therobustness of business performance.

Table 5. BCC module sensitivity analysis.

Sort DMUScore

(new) (%)Score

(old) (%) Sort DMUScore

(new) (%)Score

(old) (%)

Panel (a)PC Firm1 100.00 100.00 Package Firm9 57.75 100.00

Firm2 79.72 98.17 substrate Firm10 54.37 60.14Firm3 100.00 100.00 Firm11 100.00 100.00Firm4 100.00 100.00 Firm31(*) 48.82Firm5 71.36 93.37 Firm32(*) 65.10Firm6 100.00 100.00 Firm33(*) 100.00Firm26(*) 67.96 Firm34(*) 100.00Firm27(*) 100.00


Monitor Firm7 41.91 54.33 DRAM Firm12 16.65 17.46Firm8 30.10 44.48 Firm13 59.84 65.71Firm28(*) 51.63 Firm14 46.34 57.19Firm29(*) 75.16 Firm15 28.33 40.00Firm30(*) 80.51 Firm35(*) 10.47

Firm36(*) 100.00Firm37(*) 100.00

Average 55.86 49.41 Average 51.66 45.09Sensitivity 13.07 Sensitivity 14.57

Panel (b)Power Firm16 45.64 80.50 IC design Firm22 100.00 100.00supply Firm17 100.00 100.00 Firm23 21.25 100.00

Firm18 63.58 92.89 Firm41(*) 100.00Firm38(*) 100.00 Firm42(*) 28.24Firm39(*) 92.35 Firm43(*) 100.00

Firm44(*) 35.79Firm45(*) 32.06Firm46(*) 29.31Firm47(*) 88.14


Thermal Firm19 71.55 100.00 Wafer OEM Firm24 19.65 37.34module Firm20 100.00 100.00 Firm25 18.20 21.81

Firm21 92.38 100.00 Firm48(*) 28.49Firm40(*) 100.00 Firm49(*) 42.41

Firm50(*) 100.00

Average 90.98 100.00 Average 41.75 29.58Sensitivity �9.02 Sensitivity 41.17

All avg 68.13 78.54Sensitivity �13.25


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Table 6. Benchmarks of CCR module.

Sort DMU Score (new) (%) Score (old) (%) Reference setBusiness

benchmarks

Panel (a)PC Firm1 100.00 100.00 19 1(best),4

Firm2 70.37 90.75 1,26Firm3 77.78 87.19 1,26Firm4 100.00 100.00 0Firm5 62.38 87.50 8,26Firm6 72.20 100.00 1,26Firm26(*) 65.79 1,29,39Firm27(*) 100.00 12

Monitor Firm7 39.26 53.41 8,26 29Firm8 29.54 42.33 1,8,26Firm28(*) 51.60 27,34Firm29(*) 74.52 1,8,26Firm30(*) 69.41 8,26

Package Firm9 55.35 100.00 8,26 11,33,34substrate Firm10 54.32 59.52 8,26,31,34,36

Firm11 97.67 100.00 8,26,34,39Firm31(*) 39.85 26,2,34,39Firm32(*) 62.18 26,31,34,36Firm33(*) 100.00 2Firm34(*) 100.00 7

DRAM Firm12 16.38 16.51 1,31,34,36 36,37Firm13 58.86 65.65 1,26,31,34,36Firm14 44.71 56.46 1,19,26,31,36Firm15 28.28 32.14 1,31,34,36Firm35(*) 3.33 1,20,26,27,29Firm36(*) 100.00 29Firm37(*) 100.00 13

Panel (b)Power supply Firm16 31.83 77.98 1,8,26,34,39 17,38

Firm17 100.00 100.00 9Firm18 61.37 92.71 1,20,26,27,29Firm38(*) 100.00 9Firm39(*) 84.41 1,20,26,27,39

Thermal Firm19 71.03 100.00 20,26,27,29 20(best),40module Firm20 100.00 100.00 13

Firm21 88.71 100.00 20,26,27Firm40(*) 100.00 9

IC design Firm22 81.36 100.00 26,27,29 41,43Firm23 18.43 31.96 1,26,27,34Firm41(*) 100.00 6Firm42(*) 25.80 26,27,29Firm43(*) 100.00 0Firm44(*) 35.77 27,34Firm45(*) 27.61 20,26,27,29Firm46(*) 19.85 1,20,27,29Firm47(*) 80.21 26,29,39

Wafer OEM Firm24 19.58 30.61 8,26 50Firm25 17.02 20.79 1,8,19,26Firm48(*) 27.18 1,31,34,36Firm49(*) 37.67 1,8,26Firm50(*) 100.00 0


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Table 7. Benchmarks of BCC module.

Sort DMU Score (new) (%) Score (old) (%) Reference setBusiness

benchmarks

Panel (a)PC Firm1 100.00 100.00 8 1(best),3(best),

4(best), 6,27Firm2 79.72 98.17 1,3,4,26Firm3 100.00 100.00 1Firm4 100.00 100.00 2Firm5 71.36 93.37 8,19,26Firm6 100.00 100.00 0Firm26(*) 67.96 1,26,29,39Firm27(*) 100.00 10

Monitor Firm7 41.91 54.33 8,19,26 30Firm8 30.10 44.48 1,8,26Firm28(*) 51.63 27,31,34,36Firm29(*) 75.16 1,4,8,26Firm30(*) 80.51 19,26,27

Package Firm9 57.75 100.00 8,19,26 11,33,34substrate Firm10 54.37 60.14 8,26,27,31,34,36

Firm11 100.00 100.00 0Firm31(*) 48.82 1,26,39Firm32(*) 65.10 19,26,27,31,36Firm33(*) 100.00 5Firm34(*) 100.00 8

DRAM Firm12 16.65 17.46 26,31,34,36 36,37Firm13 59.84 65.71 26,31,34,36Firm14 46.34 57.19 8,26,31,36Firm15 28.33 40.00 26,31,34,36Firm35(*) 10.47 20,26Firm36(*) 100.00 28Firm37(*) 100.00 12

Panel (b)Power supply Firm16 45.64 80.50 1,26,39,41,50 17,38

Firm17 100.00 100.00 8Firm18 63.58 92.89 20,26,27,29Firm38(*) 100.00 10Firm39(*) 92.35 20,26,34

Thermal Firm19 71.55 100.00 20,26,27,29 20(best),40module Firm20 100.00 100.00 9

Firm21 92.38 100.00 20,26,27,34Firm40(*) 100.00 10

IC design Firm22 100.00 100.00 0 22,41,43Firm23 21.25 100.00 20,26,27,29Firm41(*) 100.00 4Firm42(*) 28.24 20,26,27,29Firm43(*) 100.00 2Firm44(*) 35.79 27,31,34,36Firm45(*) 32.06 27,29Firm46(*) 29.31 27,29Firm47(*) 88.14 26,29,39,41

Wafer OEM Firm24 19.65 37.34 1,8,26 50Firm25 18.20 21.81 8,26,31,36Firm48(*) 28.49 20,26,34Firm49(*) 42.41 1,8,26Firm50(*) 100.00 1


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This research concludes that if a company is within the reference set in each businessand has steady performance (both in new and old groups), then it is considered as thebest benchmark. Additionally, this study found that if the company reference set has zerocompanies, this means that the performance of this company will not be influenced by

adding or withdrawing other companies. If it is outside the reference set in a business, thenthe business with the highest efficiency is considered a reference benchmark, but not thebest benchmark.

This main value of this study lies in proposing one objective approach thatestablishes the stable and capable benchmarks. Due to the restrictions in time and cost,it was impossible to study all of the companies in the information industry. However,this is a suggestion for future study. Researchers can also consider other related researchsubjects as future studies, such as a study on an industry other than information industryfor purpose of comparison. As to the classification of companies, other than classifyingcompanies by product types it is worth applying Support Vector machines or Neural

networks as an objective and quantitative classification.

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Documents

Determining the optimal collaborative benchmarks in a supply chain