LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW

KARYA ILMIAH : JURNAL ILMIAH

................

Judul Jurnal Ilmiah (Artikel) : Optimisation of supplier selection with Taguchi loss function, analytic network

process, and multi-choice goal programming

Jumlah Penulis : 3 orang

Status Pengusul : penulis pertama/utama

Identitas Jurnal Ilmiah : a. Nama Jurnal : International Journal of Agile Systems

and Management

b. Nomor ISSN : 17419174, 17419182

c. Volume, nomor, bulan tahun : 7(2): 155-178., Juni 2014

d. Penerbit : Inderscience

e. DOI artikel (jika ada) : DOI: 10.1504/IJASM.2014.061454

f. Alamat web jurnal :

JURNAL

ARTIKEL

: https://www.inderscienceonline.com/doi/abs/10.1504/IJASM.2014.061454 : http://eprints.undip.ac.id/64709/

g. Terindeks di Scopus/Scimagojr/SJR=0,88 (2017) dan Q1.

Kategori Publikasi Jurnal Ilmiah : Jurnal Ilmiah Internasional

(beri pada kategori yang tepat) Jurnal Ilmiah Nasional Terakreditasi

Jurnal Ilmiah Nasional Tidak Terakreditasi

Hasil Penilaian Peer Review :

Komponen

Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Yang

Diperoleh Reviewer 1 Reviewer 2

a. Kelengkapan unsur isi jurnal(10%) 2,11 2,42 2,26

b. Ruang lingkup dan kedalaman

pembahasan (30%) 6,32 6,06

6,19

c. Kecukupan dan kemutahiran

data/informasi dan metodologi

(30%) 5,26

6,06

5,66

d. Kelengkapan unsur dan kualitas

penerbit (30%) 6,32 5,45

5,89

Total = (100%) 20,00 20,00 20,00

Nilai Pengusul = (60%)*20,00 = 12

Semarang,

Reviewer 1

Prof.Ir. Togar M. Simatupang, M.Tech.,Ph.D.

NIP. 196812311993031015

Unit kerja: Sekolah Bisnis dan Manajemen

(SBM) Insititut Teknologi Bandung

Reviewer 2

Prof. Ir. I Nyoman M.Eng.Ph.D. NIP.

196912311994121076 Unit

kerja :Teknik Industri ITS

LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW

KARYA ILMIAH : JURNAL ILMIAH

................................

Judul Jurnal Ilmiah (Artikel) : “Optimisation of supplier selection with Taguchi loss function, analytic network

process, and multi-choice goal programming”

Jumlah Penulis : 3 orang

Status Pengusul : penulis pertama/utama

Identitas Jurnal Ilmiah : a. Nama Jurnal : International Journal of Agile Systems

and Management

b. Nomor ISSN : 17419174, 17419182

c. Volume, nomor, bulan tahun : 7(2): 155-178., Juni 2014

d. Penerbit : Inderscience

e. DOI artikel (jika ada) : DOI: 10.1504/IJASM.2014.061454

f. Alamat web jurnal :

JURNAL : https://www.inderscienceonline.com/doi/abs/10.1504/IJASM.2014.061454

ARTIKEL : http://eprints.undip.ac.id/64709/

g. Terindeks di Scopus/Scimagojr/SJR=0,88 (2017) dan Q1.

Kategori Publikasi Jurnal Ilmiah : Jurnal Ilmiah Internasional

(beri pada kategori yang tepat) Jurnal Ilmiah Nasional Terakreditasi

Jurnal Ilmiah Nasional Tidak Terakreditasi

Hasil Penilaian Peer Review :

Komponen

Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Akhir

Yang

Diperoleh

Internasional

Nasional

Terakreditasi

Nasional

Tidak

Terakreditasi

a. Kelengkapan unsur isi jurnal (10%) 4,00 2,11b. Ruang lingkup dan kedalaman

pembahasan (30%)

12,00 6,32

c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

12,00 5,26

d. Kelengkapan unsur dan kualitas

terbitan/jurnal (30%)

12,00 6,32

Total = (100%) 40,00

Nilai Pengusul = 60%*20= 12,00

Catatan Penilaian artikel oleh Reviewer: 1. Kesesuaian dan kelengkapan unsur isi jurnal: Struktur artikel telah sesuai dengan petunjuk dan sudah memuat abstract, introduction, Criterion

for supplier selection, Integrated proposed model for supplier selection, Selecting the best supplier: an illustrative problem, dan Conclusions. Unsur-

unsur paper sudah memasukkan aspek penting yang ditetapkan oleh jurnal seperti cara penulisan referensi dan format. 2. Ruang lingkup dan kedalaman pembahasan: Lingkup penelitian merupakan bidang Teknik Industri terutama sistem produksi dan hubungannya

dengan pemasok atau vendor. Model yang digunakan cukup lengkap yang terdiri dari fungsi kerugian Taguchi, ANP (analyic network process), dan

pemograman tujuan jamak. Penulis menggunakan gabungan model tersebut untuk mendapatkan model yang terintegrasi yang dibutuhkan olehsebuah perusahaan dalam memilih vendor.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi: Model yang dikembangkan ditunjukkan kemampuannya melalui ilustrasi analisis

numerik. Perlu suatu pendekatan empiris dari dunia industri untuk mengetahui kecocokan variabel dengan ketersediaan data. Model ditunjukkan lebih baik dari model yang sudah ada. Referensi yang digunakan cukup mutakhir.

4. Kelengkapan unsur dan kualitas terbitan: Paper telah diterbitkan baik dalam bentuk daring maupun cetakan. Situs (lihat

http://www.inderscience.com/jhome.php?jcode=IJASM) secara resmi dilengkapi dengan ISSN, petunjuk penulisan, edtorial board, pengindeks, danlain-lain. Jurnal bereputasi sangat baik dan terindeks SCOPUS dengan kategori Q1.

Semarang, 19 September 2018

Reviewer 1

Prof. Ir. Togar M. Simatupang, Ph.D.

NIP. 196812311993031015

Unit kerja : Sekolah Bisnis dan Manajemen (SBM)

Insititut Teknologi Bandung (ITB)

20,00

LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW

KARYA ILMIAH : JURNAL ILMIAH

................................

Judul Jurnal Ilmiah (Artikel) : Optimisation of supplier selection with Taguchi loss function, analytic network

process, and multi-choice goal programming

Jumlah Penulis : 3 orang

Status Pengusul : penulis pertama/utama

Identitas Jurnal Ilmiah : a. Nama Jurnal : International Journal of Agile Systems

and Management

b. Nomor ISSN : 17419174, 17419182

c. Volume, nomor, bulan tahun : 7(2): 155-178., Juni 2014

d. Penerbit : Inderscience e. DOI artikel (jika ada) : DOI: 10.1504/IJASM.2014.061454

f. Alamat web jurnal :

JURNAL

ARTIKEL

: https://www.inderscienceonline.com/doi/abs/10.1504/IJASM.2014.061454

: http://eprints.undip.ac.id/64709/g. Terindeks di Scopus/Scimagojr/SJR=0,88 (2017) dan Q1.

Kategori Publikasi Jurnal Ilmiah : Jurnal Ilmiah Internasional

(beri pada kategori yang tepat) Jurnal Ilmiah Nasional Terakreditasi

Jurnal Ilmiah Nasional Tidak Terakreditasi

Hasil Penilaian Peer Review :

Komponen

Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Akhir

Yang

Diperoleh

Internasional

Nasional

Terakreditasi

Nasional

Tidak

Terakreditasi

a. Kelengkapan unsur isi jurnal (10%) 4,00 2,42b. Ruang lingkup dan kedalaman

pembahasan (30%)

12,00 6,06

c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

12,00 6,06

d. Kelengkapan unsur dan kualitasterbitan/jurnal (30%)

12,00 8,45

Total = (100%) 40,00

Nilai Pengusul = 60%*20 = 12

Catatan Penilaian artikel oleh Reviewer : 1. Kesesuaian dan kelengkapan unsur isi jurnal: Bidangnya in line dengan bidang penulis. Isi artikel lengkap sebagaimana artikel jurnal pada

umumnya.

2. Ruang lingkup dan kedalaman pembahasan: Cukup dalam pembahasanya.

.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi: Menggunakan kombinasi dari beberapa metode klasik (Taguchi, ANP, GP).

4. Kelengkapan unsur dan kualitas terbitan: Jurnal memenuhi kaidah jurnal ilmiah pada uumnya, reputasi jurnal maupun penerbitnya belum terlalubagus (walaupun Q1 di Scimago, tapi h-index baru 17). Jurnal yang secara internasional diakui top di bidang Teknik Industri tidak lebih dari 30 danpenerbitnya terbatas (Elsevier, Taylor & Francis, Wiley, Springer, beberapa di Emerald, dan INFORMS). Sebagai contoh, European Journal ofOperational Research, acceptance ratenya tidak lebih dari 20%, h-index nya 211.

Surabaya, 23 September 2018

Reviewer 2

Prof. Ir. I Nyoman M.Eng.Ph.D.NIP. 196912311994121076 Unit kerja : Teknik Industri ITS

20,00

Scopus

Document details

References (38)

30 of 38

Optimisation of supplier selection with Taguchi loss function, analytic networkprocess, and multi-choice goal programming (Article)

, ,

Department of Industrial Engineering, Diponegoro University, Semarang, 50275, IndonesiaSchool of Business IT and Logistics, College of Business, RMIT University, Melbourne, VIC 3001, Australia

AbstractThis study proposed an integrated method of the Taguchi loss function, ANP, and MCGP to select the best supplier.This study proposed six criteria and 19 of decision sub-criteria as qualitative and quantitative factors to select the bestsupplier. In the proposed method, each sub-criterion is assigned an importance weight through ANP analysis. Thenthe 19 of sub-criteria are incorporated into the Taguchi loss functions to estimate the total loss. In the final step,importance weight and estimated total loss of suppliers are incorporated into MCGP model to identify the bestsupplier. Copyright © 2014 Inderscience Enterprises Ltd.

SciVal Topic Prominence

Topic:

Prominence percentile: 99.242

Author keywordsAnalytic network process ANP MCGP Multi-choice goal programming Supplier selection Taguchi loss function

Ayag, Z.Evaluating simulation software alternatives through ANP(2011) Proceeding of the 2nd International Conference on Industrial Engineering and OperationsManagement (IEOM 2011).  .22-24 January, Kuala Lumpur, Malaysia

 

Chang, C.-T.

(Open Access)

(2008) Applied Mathematical Modelling, 32 (12), pp. 2587-2595.  .doi: 10.1016/j.apm.2007.09.008

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Supplier selection | Decision making | Sustainable supplier

ISSN: 17419174Source Type: JournalOriginal language: English

DOI: 10.1504/IJASM.2014.061454Document Type: ArticlePublisher: Inderscience Enterprises Ltd.

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REVIEW PROCESS

RESUME KRONOLOGIS KORESPONDENSI DARI SUPPLIER SELECTION WITH TAGUCHI

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IJASM_56705 Submission Acknowledgement

From: Inderscience Submissions System (submissions@inderscience.com)

To: ariessusanty@yahoo.com

Date: Saturday, May 4, 2013, 12:18 PM GMT+7

Dear Dr Aries Susanty,

Thank you for submitting your article entitled "Optimisation of Supplier Selection with Taguchi Loss Function,Analytic Network Process (ANP), and Multi-Choice Goal Programming (MCGP)" (Submission code: IJASM-56705)for the International Journal of Agile Systems and Management (IJASM).

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pp. IJASM Editor

Inderscience Publishers Ltd. submissions@inderscience.com

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Refereeing Process: Editor comments IJASM-56705

From: Online Submissions (submissions@indersciencemail.com)

To: ariessusanty@yahoo.com; ferry.jie@rmit.edu.au; ardliezard@gmail.com; john.mo@rmit.edu.au

Date: Sunday, October 13, 2013, 1:01 PM GMT+7

Dear Author(s),

We have received the review reports for your paper "Optimisation of Supplier Selection with Taguchi Loss Function,Analytic Network Process (ANP), and Multi-Choice Goal Programming (MCGP)".

We require now that you implement in your submission the following recommendations made by the reviewers:

Reviewer A Comments:

================== Originality of the work : Marginal

Engineering relevance : Marginal Scientific relevance : Poor Completeness of the work : Marginal Acknowledgement of the work of others by references : Marginal Organization of the manuscript : Acceptable Clarity in writing, tables, graphs and illustrations : Acceptable Likelihood of passing the "test of time" : Poor Is the technical treatment plausible and free of technical errors : Yes

Have you checked the equations? : No Are you aware of prior publication or presentation of this work? : No Is the manuscript free of commercialism? : Yes Is the paper too long? : No RECOMMENDATION : Acceptable with major revisions

Changes which must be made before publication: Major Revision is Essential;

English is to be revised and improved with native speaker from abstract to conclusion. Abstract does not convey the exact theme of the paper. The author(s) claim that they have proposed Taguchi Loss

Function, ANP, and MCGP but it is already available in the literature. I have seen many papers and they havepublished the same notion.

I can see many repetition of the same work from the submitted paper. I can see from literature study that, manyworks have been carried out in similar to the existing paper. How far author(s) logic is better than other and there isno comparison with respect to others in all aspects. For instance,

1. Zhu, C., Wang, Z., and Shan, X. (2011) Evaluation of Urban Rail Network Based on Analytic Network Process.ICTE 2011: pp. 31-36. doi: 10.1061/41184(419)6 2. Farah Yasmin, Fuzzy Theory Concept Applied in Analytic Network Process, IJARCSSE, Vol.3, ,Iss.5, May-2013,pp.832-837.

3. Proposal solicitation & supplier selection. 4. Pi-Fang Hsu and Min-Hua Kuo, Applying the ANP Model for Selecting the Optimal Full-service Advertising

Agency, International Journal of Operations Research Vol.8, No. 4, 48-58 (2011) 5. Chiang Ku Fan and Tien Chun Chen The Risk Management Strategy of Applying Cloud Computing, (IJACSA)International Journal of Advanced Computer Science and Applications,Vol. 3, No. 9, 2012.

6. Supplier Selection Methods for Small Scale Manufacturing Industry: A Review, International Journal of Scienceand Research (IJSR), India Online ISSN: 2319-7064 7. Farzad Tahriri, Mohammad Rasid Osman, Aidy Ali and Rosnah Mohd Yusuff, A REVIEW OF SUPPLIERSELECTION METHODS IN MANU-FACTURING INDUSTRIES, J. Sci. Technol. 15(3):201-208, Received: Sept 24,2007; Revised: Jun 2, 2008; Accepted: Jul 29, 2008.

8. TAGUCHI LOSS FUNCTION AS OPTIMISED MODEL FOR SUPPLIER SELECTION AND EVALUATION,International Journal of Advanced Engineering Technology, International Journal of Advanced EngineeringTechnology E-ISSN 0976-3945;IJAET/Vol.III/ Issue I/January-March, 2012/268-270

9. Chin-Nung Liao, Supplier selection project using an integrated Delphi, AHP and Taguchi loss function, ProbStatForum, Volume 03, July 2010, Pages 118-134

10. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management, Expert Systems with Applications, Volume 38, Issue 9, September 2011, Pages 10803–10811.

08/03/2019 Yahoo Mail - Refereeing Process: Editor comments IJASM-56705

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11. Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goalprogramming, Computers & Industrial Engineering, Volume 58, Issue 4, May 2010, Pages 571–577.

12. COMBINING ANALYTICAL HIERARCHY PROCESS AND TOPSIS APPROACHES FOR SUPPLIERSELECTION IN A CABLE COMPANY, Journal of Business, Economics & Finance (2013), Vol.2(2).

13. Nuray Girginer, USING ANP FOR COURSEWARE DEVELOPMENT PLATFORM SELECTION The contribution from the paper is very limited, so significant improvement of the quality is necessary.

Care has been taken to ensure the quality of the presentation & research directions. It is significant to point out that, I do not see any unique contributions with sufficient editorial, novelty dealings

including new concepts, approaches. All notions are already available and existing from literature review. Reviewer B Comments:

================== Originality of the work : Marginal

Engineering relevance : Acceptable Scientific relevance : Acceptable Completeness of the work : Marginal Acknowledgement of the work of others by references : Acceptable Organization of the manuscript : Good Clarity in writing, tables, graphs and illustrations : Acceptable Likelihood of passing the "test of time" : Marginal Is the technical treatment plausible and free of technical errors : Yes

Have you checked the equations? : No Are you aware of prior publication or presentation of this work? : No Is the manuscript free of commercialism? : Yes Is the paper too long? : No RECOMMENDATION : Acceptable with major revisions

Suggestions which would improve the quality of the paper but are not essential for publication: I will leave it up to the author do decide if the following suggested changes are essential for publication or not.

Section 1 Introduction 1. "...have been developed since 1966".

What is so special about 1966?

2. "There are several methods that can be used by the company to select their supplier, i.e. ...". Perhaps better to use ""e.g." instead of "i.e." as the list you give are only examples, not the full list of possibilities.

3. "Usually when a company sets out to develop ... ... and to discuss their different applications". OK - then why is your method better than the example decision support methods you list above such as the muchless complex 'decision-matrix' method? Why should I go to through all the complexity of your proposed methodwhen I could use the much simpler Decision-Matrix method and perhaps obtain a result that is good enough andmuch more transparent? I believe it is important to explain this. When would a supplier be upgraded or downgradedin rank using your method that would not be captured by using other perhaps less complex methods? 4. "... intangible constrains regarding ..."

Spelling 5. "Thus the existence of of interdependencies among the criteria will influence the suppliers overall ranking."

"This may be true but is the influence actually significant? Has this been tested? 6. "Based on the limitation of AHP ... is more suitable than AHP."

No evidence is provided in the paper that use of AHP for this problem is a practical or significant limitation for thisdecision making problem over the use of ANP.

7. "In order to improve the study which is conducted by Liao and Kao (2010) ..." At the moment your method is only a potential improvement. No evidence is given that it actually is an improvement.

Section 3.1 Analytic Network Process

8. "The ANP provides a solution for problems that cannot be structured hierarchically." One justification then for using ANP over AHP is that the factors in the problem cannot be structured in a hierarchy?Have you tried, and if so what were the actual problems encountered in creating this hieararchy that meant ANP wasrequired?

Section 4.2 Formulating ....

9. "IDR" Suggest explain early this is a currency. I had to look it up to find out what it was.

10. "The value of k (average loss coefficient) can be calcuted from ..." The explanation of the calculation of the k values is a bit thin. Suggest showing the completion numerical calculation

of the variable k from eqns (5) or (6) for at least example to aid reader understanding. 11. "114,0003.91"

???? Is this 114,003 or something else?

08/03/2019 Yahoo Mail - Refereeing Process: Editor comments IJASM-56705

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12. Table 4 Suggest round up all numbers to 0 decimal places. The current two decimal places makes the presented data hard

to read. 13. Paragraph: "Different with five sub-criteria ... is a discount which is given by each supplier."

Don't understand the meaning or point of this paragraph. Section 5 Conclusion

14. "...combination of several different methods with different strengths suited to meet the company's specificselection needs."

Yes, but under what criteria or conditions would a company choose to use your method over other methods?

15. "In this study, a sensitivity analysis has not been done in the selection of the best supplier." Why not? It would probably not take long to do as you obviously have all the model set up. This is critical to the

usefulness of the method. Consider: how sensitive is obtained solution for example to changes/errors in theparameters estimated in the Taguchi Loss Functions for the factors and suppliers? Is the solution obtained robust toerrors in the parameter estimations? If a 5% change in one qualitative parameter estimation changes the supplierranking then the solution is probably not considered robust and the use of a more complex method such as this maynot be justified. I would suggest you reconsider the present non-performance of a sensitivity analysis for theexample decision problem presented in this paper. Changes which must be made before publication: This paper is very much the same content as Liao and Kao (2010) with AHP swapped out for ANP. At present thejustification presented in this review paper for using ANP instead of AHP is very thin. Just saying that ANP is betterthan AHP for this paper does not necessarily make it so. I would like to see more evidence or comparison betweenthe results using either method and evidence of a benefit of this paper over Liao and Kao (2010). I don't see this atpresent. Reviewer C Comments:

================== Originality of the work : Poor

Engineering relevance : Poor Scientific relevance : Poor Completeness of the work : Poor Acknowledgement of the work of others by references : Marginal Organization of the manuscript : Marginal Clarity in writing, tables, graphs and illustrations : Marginal Likelihood of passing the "test of time" : Marginal Is the technical treatment plausible and free of technical errors : No

Have you checked the equations? : Yes Are you aware of prior publication or presentation of this work? : Yes Is the manuscript free of commercialism? : No Is the paper too long? : Yes RECOMMENDATION : NOT ACCEPTABLE

Suggestions which would improve the quality of the paper but are not essential for publication: Not Applicable.

Changes which must be made before publication: Not Applicable.

NOTE: Please send an email to the editor to acknowledge the reception of this email notification. The editor needsto make sure that messages reach the authors and don't delay the review process.

- - - - - - - - - - - - - - - - - - - - -

Instructions

1) To help the reviewer(s) verify that you have made the required corrections, please append a summary of themodifications made at the beginning of your revised manuscript.

2) Append figures, images and tables at the end of your revised manuscript.

3) To upload your revised version, please:

Login via http://www.inderscience.com/ospeers/login.php if you do not remember your username or password, you can recover it viahttp://www.inderscience.com/forgotpw.php)

Then point your browser to

08/03/2019 Yahoo Mail - Refereeing Process: Editor comments IJASM-56705

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http://www.inderscience.com/ospeers/admin/author/articlestatus.php?id=56705 and scroll-down to find the input box"Author's revised version of file". Click on 'Browse...' to select the revised document to be submitted and click 'Upload'.

4) Click on "Editor/Author Comments" to access the referee(s) comments and possible annotated files.

If you have problems uploading the file with your revised manuscript please contact submissions@inderscience.comindicating the submission ID of your article.

- - - - - - - - - - - - - - - - - - - - -

IMPORTANT: If we do not receive your revised manuscript within 3 months your manuscript will be considered as anew submission and will be sent to a new round of reviews.

Your prompt attention is much appreciated.

pp. Prof. John Mo

Int. J. of Agile Systems and Management (IJASM) submissions@inderscience.com

Dear Author(s),

We have received the review reports for your paper "Optimisation of Supplier Selection with Taguchi

Loss Function, Analytic Network Process (ANP), and Multi-Choice Goal Programming (MCGP)".

We require now that you implement in your submission the following recommendations made by

the reviewers:

Reviewer A Comments:

==================

Originality of the work : Marginal

Engineering relevance : Marginal

Scientific relevance : Poor

Completeness of the work : Marginal

Acknowledgement of the work of others by references : Marginal

Organization of the manuscript : Acceptable

Clarity in writing, tables, graphs and illustrations : Acceptable

Likelihood of passing the "test of time" : Poor

Is the technical treatment plausible and free of technical errors : Yes

Have you checked the equations? : No

Are you aware of prior publication or presentation of this work? : No

Is the manuscript free of commercialism? : Yes

Is the paper too long? : No

RECOMMENDATION : Acceptable with major revisions

Changes which must be made before publication:

Major Revision is Essential;

English is to be revised and improved with native speaker from abstract to conclusion.

Abstract does not convey the exact theme of the paper. The author(s) claim that they have proposed

Taguchi Loss Function, ANP, and MCGP but it is already available in the literature. I have seen many

papers and they have published the same notion.

I can see many repetition of the same work from the submitted paper. I can see from literature

study that, many works have been carried out in similar to the existing paper. How far author(s) logic

is better than other and there is no comparison with respect to others in all aspects. For instance,

1. Zhu, C., Wang, Z., and Shan, X. (2011) Evaluation of Urban Rail Network Based on Analytic

Network Process. ICTE 2011: pp. 31-36. doi: 10.1061/41184(419)6

2. Farah Yasmin, Fuzzy Theory Concept Applied in Analytic Network Process, IJARCSSE, Vol.3, ,Iss.5,

May-2013,pp.832-837.

3. Proposal solicitation & supplier selection.

4. Pi-Fang Hsu and Min-Hua Kuo, Applying the ANP Model for Selecting the Optimal Full-service

Advertising Agency, International Journal of Operations Research Vol.8, No. 4, 48-58 (2011)

5. Chiang Ku Fan and Tien Chun Chen The Risk Management Strategy of Applying Cloud Computing,

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 3, No. 9, 2012.

6. Supplier Selection Methods for Small Scale Manufacturing Industry: A Review, International

Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

7. Farzad Tahriri, Mohammad Rasid Osman, Aidy Ali and Rosnah Mohd Yusuff, A REVIEW OF

SUPPLIER SELECTION METHODS IN MANU-FACTURING INDUSTRIES, J. Sci. Technol. 15(3):201-208,

Received: Sept 24, 2007; Revised: Jun 2, 2008; Accepted: Jul 29, 2008.

8. TAGUCHI LOSS FUNCTION AS OPTIMISED MODEL FOR SUPPLIER SELECTION AND EVALUATION,

International Journal of Advanced Engineering Technology, International Journal of Advanced

Engineering Technology

E-ISSN 0976-3945;IJAET/Vol.III/ Issue I/January-March, 2012/268-270

9. Chin-Nung Liao, Supplier selection project using an integrated Delphi, AHP and Taguchi loss

function, ProbStat Forum, Volume 03, July 2010, Pages 118-134

10. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply

chain management, Expert Systems with Applications, Volume 38, Issue 9, September 2011, Pages

10803–10811.

11. Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-

choice goal programming, Computers & Industrial Engineering, Volume 58, Issue 4, May 2010, Pages

571–577.

12. COMBINING ANALYTICAL HIERARCHY PROCESS AND TOPSIS APPROACHES FOR SUPPLIER

SELECTION IN A CABLE COMPANY, Journal of Business, Economics & Finance (2013), Vol.2(2).

13. Nuray Girginer, USING ANP FOR COURSEWARE DEVELOPMENT PLATFORM SELECTION

The contribution from the paper is very limited, so significant improvement of the quality is

necessary.

Care has been taken to ensure the quality of the presentation & research directions.

It is significant to point out that, I do not see any unique contributions with sufficient editorial,

novelty dealings including new concepts, approaches. All notions are already available and existing

from literature review.

Reviewer B Comments:

==================

Originality of the work : Marginal

Engineering relevance : Acceptable

Scientific relevance : Acceptable

Completeness of the work : Marginal

Acknowledgement of the work of others by references : Acceptable

Organization of the manuscript : Good

Clarity in writing, tables, graphs and illustrations : Acceptable

Likelihood of passing the "test of time" : Marginal

Is the technical treatment plausible and free of technical errors : Yes

Have you checked the equations? : No

Are you aware of prior publication or presentation of this work? : No

Is the manuscript free of commercialism? : Yes

Is the paper too long? : No

RECOMMENDATION : Acceptable with major revisions

Suggestions which would improve the quality of the paper but are not essential for publication:

I will leave it up to the author do decide if the following suggested changes are essential for

publication or not.

Section 1 Introduction

1. "...have been developed since 1966".

What is so special about 1966?

2. "There are several methods that can be used by the company to select their supplier, i.e. ...".

Perhaps better to use ""e.g." instead of "i.e." as the list you give are only examples, not the full list of

possibilities.

3. "Usually when a company sets out to develop ... ... and to discuss their different applications".

OK - then why is your method better than the example decision support methods you list above such

as the much less complex 'decision-matrix' method? Why should I go to through all the complexity of

your proposed method when I could use the much simpler Decision-Matrix method and perhaps

obtain a result that is good enough and much more transparent? I believe it is important to explain

this. When would a supplier be upgraded or downgraded in rank using your method that would not

be captured by using other perhaps less complex methods?

4. "... intangible constrains regarding ..."

Spelling

5. "Thus the existence of of interdependencies among the criteria will influence the suppliers overall

ranking."

"This may be true but is the influence actually significant? Has this been tested?

6. "Based on the limitation of AHP ... is more suitable than AHP."

No evidence is provided in the paper that use of AHP for this problem is a practical or significant

limitation for this decision making problem over the use of ANP.

7. "In order to improve the study which is conducted by Liao and Kao (2010) ..."

At the moment your method is only a potential improvement. No evidence is given that it actually is

an improvement.

Section 3.1 Analytic Network Process

8. "The ANP provides a solution for problems that cannot be structured hierarchically."

One justification then for using ANP over AHP is that the factors in the problem cannot be structured

in a hierarchy? Have you tried, and if so what were the actual problems encountered in creating this

hieararchy that meant ANP was required?

Section 4.2 Formulating ....

9. "IDR"

Suggest explain early this is a currency. I had to look it up to find out what it was.

10. "The value of k (average loss coefficient) can be calcuted from ..."

The explanation of the calculation of the k values is a bit thin. Suggest showing the completion

numerical calculation of the variable k from eqns (5) or (6) for at least example to aid reader

understanding.

11. "114,0003.91"

???? Is this 114,003 or something else?

12. Table 4

Suggest round up all numbers to 0 decimal places. The current two decimal places makes the

presented data hard to read.

13. Paragraph: "Different with five sub-criteria ... is a discount which is given by each supplier."

Don't understand the meaning or point of this paragraph.

Section 5 Conclusion

14. "...combination of several different methods with different strengths suited to meet the

company's specific selection needs."

Yes, but under what criteria or conditions would a company choose to use your method over other

methods?

15. "In this study, a sensitivity analysis has not been done in the selection of the best supplier."

Why not? It would probably not take long to do as you obviously have all the model set up. This is

critical to the usefulness of the method. Consider: how sensitive is obtained solution for example to

changes/errors in the parameters estimated in the Taguchi Loss Functions for the factors and

suppliers? Is the solution obtained robust to errors in the parameter estimations? If a 5% change in

one qualitative parameter estimation changes the supplier ranking then the solution is probably not

considered robust and the use of a more complex method such as this may not be justified. I would

suggest you reconsider the present non-performance of a sensitivity analysis for the example

decision problem presented in this paper.

Changes which must be made before publication:

This paper is very much the same content as Liao and Kao (2010) with AHP swapped out for ANP. At

present the justification presented in this review paper for using ANP instead of AHP is very thin. Just

saying that ANP is better than AHP for this paper does not necessarily make it so. I would like to see

more evidence or comparison between the results using either method and evidence of a benefit of

this paper over Liao and Kao (2010). I don't see this at present.

Reviewer C Comments:

==================

Originality of the work : Poor

Engineering relevance : Poor

Scientific relevance : Poor

Completeness of the work : Poor

Acknowledgement of the work of others by references : Marginal

Organization of the manuscript : Marginal

Clarity in writing, tables, graphs and illustrations : Marginal

Likelihood of passing the "test of time" : Marginal

Is the technical treatment plausible and free of technical errors : No

Have you checked the equations? : Yes

Are you aware of prior publication or presentation of this work? : Yes

Is the manuscript free of commercialism? : No

Is the paper too long? : Yes

RECOMMENDATION : NOT ACCEPTABLE

Suggestions which would improve the quality of the paper but are not essential for publication:

Not Applicable.

Changes which must be made before publication:

Not Applicable.

NOTE: Please send an email to the editor to acknowledge the reception of this email notification.

The editor needs to make sure that messages reach the authors and don't delay the review process.

- - - - - - - - - - - - - - - - - - - - -

Instructions

1) To help the reviewer(s) verify that you have made the required corrections, please append a

summary of the modifications made at the beginning of your revised manuscript.

2) Append figures, images and tables at the end of your revised manuscript.

3) To upload your revised version, please:

Login via http://www.inderscience.com/ospeers/login.php

if you do not remember your username or password, you can recover it via

http://www.inderscience.com/forgotpw.php)

Then point your browser to

http://www.inderscience.com/ospeers/admin/author/articlestatus.php?id=56705 and scroll-down

to find the input box "Author's revised version of file".

Click on 'Browse...' to select the revised document to be submitted and click 'Upload'.

4) Click on "Editor/Author Comments" to access the referee(s) comments and possible annotated

files.

If you have problems uploading the file with your revised manuscript please contact

submissions@inderscience.com indicating the submission ID of your article.

- - - - - - - - - - - - - - - - - - - - -

IMPORTANT: If we do not receive your revised manuscript within 3 months your manuscript will be

considered as a new submission and will be sent to a new round of reviews.

Your prompt attention is much appreciated.

pp. Prof. John Mo

Int. J. of Agile Systems and Management (IJASM)

submissions@inderscience.com

Answer for Reviewer’s Comment

1. "...have been developed since 1966". What is so special about 1966?

It would like to said that the factor or criteria for supplier selection have been developed a long time ago (since1966) and the most popular criterion of supplier selection is quality, the second popular is delivery, and the third one is price/cost.

2. "There are several methods that can be used by the company to select their supplier, i.e. ...". Perhaps better to use ""e.g." instead of "i.e." as the list you give are only examples, not the full list of possibilities.

I have replaced i.e. with e.g.

3. "Usually when a company sets out to develop ... ... and to discuss their different applications". OK - then why is your method better than the example decision support methods you list above such as the much less complex 'decision-matrix' method? Why should I go to through all the complexity of your proposed method when I could use the much simpler Decision-Matrix method and perhaps obtain a result that is good enough and much more transparent? I believe it is important to explain this. When would a supplier be upgraded or downgraded in rank using your method that would not be captured by using other perhaps less complex methods?

This study proposed an integrated method of the taguchi Loss Function, ANP, and MCGP to select the best supplier. As I have mentioned in this paragraph, the result of a combination of several different methods with different strengths usually suite to meet the company’s specific selection needs. ANP is used to calculate the importance weight of each criterion which have interdependence each other. Taguchi Loss Function is applied to assess the loss of each selection criteria. Finally, based on the tangible and intangible constrains regarding the suppliers, a MCGP model is formulated and solved to identify the best supplier the quality losses are identified using Taguchi’s loss function. Using a combination of three different methods (ANP, Taguchi Lost Fuction, an MCGP) would cause the company to choose the best supplier with the lowest value on the loss of the most important criteria. It can’t be captured if only using one method alone. The supplier may change if the selected criteria considered important for the company is changed or if the value of quality loss is changed or both of them are changed

4. . "... intangible constrains regarding ..." Spelling t

I have fixed it with ‘........intangible constraints regarding...”

5. "Thus the existence of of interdependencies among the criteria will influence the suppliers overall ranking." "This may be true but is the influence actually significant? Has this been tested?

As I have mentioned in this paper the interdependencies among the criteria have been tested by Kasirian & Yusuff (2009). Result of the study conducted by Kasirian & Yusuff (2009) showed that interdependencies existed among the six criteria for supplier selection

(Cost, Quality, Delivery Reliability, Flexibility and Responsiveness, Professionalism, and Long-Term Relationship). For example, total cost of supply chain, value added productivity, and warranty cost had influences on cost of goods sold. Besides Kasirian & Yusuff (2009), the interdependencies between criteria for supplier selection have been proven Surazi (2012). According to his research, there is a significant positive relationship between high technology quality product/service and price and the relationship has an influence on supplier selection.There is also a significant positive relationship between high technology quality products/services and supplier delivery performance and the relationship has an influence on supplier selection. The price factor has a significant positive relationship with delivery performance and this relationship influences supplier selection. (this explanation has been added to the papper)

6. "Based on the limitation of AHP ... is more suitable than AHP." No evidence is provided in the paper that use of AHP for this problem is a practical or significant limitation for this decision making problem over the use of ANP.

It is true, that there were no empircal evidence in this paper that use AHP for this problem is a practical or significant limitation for this decision making problem over the use of ANP. It is because I am not use AHP for solve the same problem. But I have explained in this paper why AHP have significant limit for the decision making in the supplier selection. AHP suffers a significant limitation in assuming independence among various decision-making criteria, for example, cost may be influenced by other criteria like quality and time (Sarkis & Tarulli, 2002). In this case, the interdependence among decision crierion have been proven by Kasirian & Yusuff (2009) dan Surazi (2012).

7. "In order to improve the study which is conducted by Liao and Kao (2010) ..." At the moment your method is only a potential improvement. No evidence is given that it actually is an improvement.

Yes, It is true because I am not use the same case study with Liou and Kao (2010) but I am sure that my study would make some potential improvement since my study have consider the the interdependencies between criterion for supplier selection.

8. "The ANP provides a solution for problems that cannot be structured hierarchically." One justification then for using ANP over AHP is that the factors in the problem cannot be structured in a hierarchy? Have

I haven’t tried. The actual problems : there is interdependencies existed among the criteria for supplier selection so we use ANP to solve the problem

you tried, and if so what were the actual problems encountered in creating this hieararchy that meant ANP was required?

9. "IDR" Suggest explain early this is a currency. I had to look it up to find out what it was.

The explanation of IDR has been added to the paper. Please see footnote 1. “ The rupiah (Rp) is the official currency of Indonesia. Issued and controlled by the Bank of Indonesia, the ISO 4217 currency code for the Indonesian rupiah is IDR”

10. "The value of k (average loss coefficient) can be calcuted from ..." The explanation of the calculation of the k values is a bit thin. Suggest showing the completion numerical calculation of the variable k from eqns (5) or (6) for at least example to aid reader understanding.

“The example of numerical calculation of the variable k from Eq. (5) or (6) for defect price can be described as follows. The value of target of defects for delivered ginger is 0% and the upper specification limit of defect parts is 0.8% .Company has calculated the costs incurred for control of defective products is 1,500,000 IDR. Based on this condition, the value of A is (average quality loss) IDR 1,500,000, the value

the customer’s tolerance) is 0.008, and value of k is 23,437,500,000 IDR.” (this explanation has been added to the papper)

11. "114,0003.91" ???? Is this 114,003 or something else?

I have fixed it with ‘........intangible constraints regarding...”

12. Table 4 Suggest round up all numbers to 0 decimal places. The current two decimal places makes the presented data hard to read.

I have rounded up all numbers in table 4 to 0 decimal places

13. Paragraph: "Different with five sub-criteria ... is a discount which is given by each supplier." Don't understand the meaning or point of this paragraph.

I have changed the sentence, may be better understood. “Different from five sub-criteria above, this study didn’t calculate the value of L or Taguchi Loss Function for sub-criteria production capacity and quantity discount. In this case, the company does not depend on only one supplier; the company has a policy to buy from several suppliers to minimize the risk of the inability of suppliers to meet the quantity demanded. The assumption is the quantity demanded will fullfilled from several suppliers and there is no lose because the quantity is not fulfilled. So, for capacity of production, value that incorporate into the MCGP is a number of the real capacity of each supplier. Associated with the quantity discount, the company does not lose anything if discount is not given by the supplier and the company also does not lose

anything if the discount is given. So, for capacity of production, value that incorporate into the MCGP is a number of discounts which is given by each supplier

14. "...combination of several different methods with different strengths suited to meet the company's specific selection needs." Yes, but under what criteria or conditions would a company choose to use your method over other methods?

Under the criteria that method can solve the supplier selection wich have interdependencies existed among the criteria for that selection and the company can choose the best supplier with the lowest value on the loss of the most important criteria.

15. "In this study, a sensitivity analysis has not been done in the selection of the best supplier." Why not? It would probably not take long to do as you obviously have all the model set up. This is critical to the usefulness of the method. Consider: how sensitive is obtained solution for example to changes/errors in the parameters estimated in the Taguchi Loss Functions for the factors and suppliers? Is the solution obtained robust to errors in the parameter estimations? If a 5% change in one qualitative parameter estimation changes the supplier ranking then the solution is probably not considered robust and the use of a more complex method such as this may not be justified. I would suggest you reconsider the present non-performance of a sensitivity analysis for the example decision problem presented in this paper.

Sensitivity analysis has been added: The current study presented exhibit limitations that should be considered for further research. In this study, a sensitivity analysis has not been done in all of the criteria for the selection of the best supplier. Research with the same theme can be done by adding a sensitivity analysis on other criteria used for supplier selection, except the price, in order to know the level of sensitivity of the optimal solution which has been obtained if there is a change in one or more parameter, except the increased or decreased of the price.

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Int. J. Agile Systems and Management, Vol. X, No. Y, xxxx 1

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Optimisation of supplier selection with Taguchi loss function, analytic network process, and multi-choice goal programming

Aries Susanty* and Ardian Bagus Putranto Department of Industrial Engineering, Diponegoro University, Semarang, 50275, Indonesia Fax: 62- 024-7460052 E-mail: ariessusanty@yahoo.com E-mail: ardliezard@gmail.com *Corresponding author

Ferry Jie School of Business IT and Logistics, RMIT University, Melbourne E-mail: ferry.jie@rmit.edu.au

Abstract: This study proposed an integrated method of the Taguchi loss function, ANP, and MCGP to select the best supplier. This study proposed six criteria and 19 of decision sub-criteria as qualitative and quantitative factors to select the best supplier. In the proposed method, each sub-criterion is assigned an importance weight through ANP analysis. Then the 19 of sub-criteria are incorporated into the Taguchi loss functions to estimate the total loss. In the final step, importance weight and estimated total loss of suppliers are incorporated into MCGP model to identify the best supplier.

Keywords: supplier selection; Taguchi lost function; analytic network process; ANP; multi-choice goal programming; MCGP.

Reference to this paper should be made as follows: Susanty, A., Putranto, A.B. and Jie, F. (xxxx) ‘Optimisation of supplier selection with Taguchi loss function, analytic network process (ANP), and multi-choice goal programming (MCGP)’, Int. J. Agile Systems and Management, Vol. X, No. Y, pp.000–000.

Biographical notes: Aries Susanty is a Lecturer in the Department of Industrial Engineering, Diponegoro University. She obtained her Doctoral in Industrial Engineering from the Bandung Institute of Technology. Her research interests include supply chain modelling, supply chain policy, procurement, and logistics strategy. She has also interests in the field of management and organisation.

Ardian Bagus Putranto is a graduate from the Department of Industrial Engineering, Diponogoro University and currently, he works as a private sector worker.

Comment [t1]: Author: Please confirm if A. Susanty is the corresponding author.

Comment [t2]: Author: Please provide full mailing address.

2 A. Susanty et al.

Ferry Jie is a Full Time Academic Staff at School of Business IT and Logistics, RMIT University. He is currently a Deputy Programme Director, Master of Supply Chain and Logistics Management. He has been in academic career for more than eight years. He has a great value in teaching, research, leadership and community engagement. He has a strong commitment to improving the quality of working life at RMIT in particularly College of Business. In addition, he can make a fully commitment to consult with academic staff within college and to represent their interests once the issues are addressed.

This paper is a revised and expanded version of a paper entitled [title] presented at [name, location and date of conference].

1 Introduction

Supplier selection is the process by which the buyer identifies, evaluates, and contracts with suppliers (Reinecke et al., 2007). Supplier selection is one of the most crucial components of the purchasing function for a company (Lopez, 2007) because qualified and reliable supplier is a key element and a good source for a buyer in reducing production and material costs. The number of suppliers to be selected depends on the sourcing strategy that a company follows. If the company is in favour of single sourcing, a single supplier is to be selected. If, on the other hand, the company follows a multiple sourcing strategy, then more than one supplier is selected. In relation to sourcing strategy, the minimum order quantity and a supplier’s capability may affect the supplier selection process (Sonmez, 2006).

Supplier selection is a multiple criteria decision-making (MCDM) problem which is affected by several conflicting factors that can be qualitative and quantitative. The factors or criteria for supplier selection have been developed since 1966. The most popular criterion of supplier selection is quality, time of delivery, and price/cost. These three usual criteria of supplier selection followed by others which are manufacturing capability, service, management, technology, research and development, finance, flexibility, reputation, relationship, risk, safety and environment (Ho et al., 2010). Related to several criteria which can be used to select the best supplier, a purchasing manager must analyse the trade-off between these of several criteria with appropriate method. There are several methods that can be used by the company to select their supplier, e.g., analytical hierarchic process (AHP), analytic network process (ANP), techniques for order preference by similarity to an ideal solution (TOPSIS), data envelopment analysis (DEA), case-based reasoning (CBR), decision-matrix method, and multi-choice goal programming (MCGP). It is important to understand why a company chooses one method (or a combination of different methods) over another. Several well-known selection methods have been developed and classified by numerous scholars over the years. Certain methods have been popular selection choices for years, while other methods have only emerged recently. Usually when a company sets out to develop or choose a supplier selection method, the result is a combination of several different methods with different strengths suited to meet the company’s specific selection needs. Therefore, it is important to explore a range of different selection methods and to discuss their different applications (Tahriri et al., 2008).

Comment [t3]: Author: If a previous version of your paper has originally been presented at a conference please complete the statement to this effect or delete if not applicable.

Optimisation of supplier selection 3

In their research, Liao and Kao (2010) proposed analytical hierarchy process (AHP), Taguchi loss function, and MCGP as an integrated model to solve the supplier selection problems. AHP is used to calculate the importance weight of each criterion. Taguchi Loss Function is applied to assess the loss of each selection criteria. Finally, based on the tangible and intangible constraints regarding the suppliers, a MCGP model is formulated and solved to identify the best supplier (Liao and Kao, 2010). Using the AHP method to calculate the importance weight of each criterion for supplier selection is not appropriate, since the criterion which is used to select the supplier are not independent but interrelated or interdependencies with each other; for example, cost may be influenced by other criteria like quality and time (Sarkis and Tarulli, 2002). Interdepencies among criterion which is used to select supplier have been proven by Kasirian and Yusuff (2009). Result of the study conducted by Kasirian and Yusuff (2009) showed that interdependencies existed among the six criteria for supplier selection (cost, quality, delivery reliability, flexibility and responsiveness, professionalism, and long-term relationship). For example, total cost of supply chain, value added productivity, and warranty cost had influenced on cost of goods sold (COGS). Besides Kasirian and Yusuff (2009), the interdependencies between criteria for supplier selection have been proven by Surazi (2012). According to his research, there is a significant positive relationship between high technology quality product/service and price and this relationship has an influence on supplier selection. There is also a significant positive relationship between high technology quality products/services and supplier delivery performance and this relationship has an influence on supplier selection. The price factor has a significant positive relationship with delivery performance and this relationship influences supplier selection.

Thus, the existence of interdependencies among the criteria will influence the suppliers’ overall ranking. Based on the limitation of AHP, using ANP to calculate the importance weight of each criterion for supplier selection is more suitable than AHP. ANP can be used to determine the priorities of the suppliers while considering interdependencies. ANP provides a general framework to deal with decisions without making assumptions about the independence of higher-level elements from lower level elements and about the independence of the elements within a level. Therefore, ANP is represented by a network without the need to specify levels as in a hierarchy (Saaty, 1999).

In order to improve the study which is conducted by Liao and Kao (2010), this study proposed an ANP, Taguchi Loss Function, and MCGP as an integrated model to solve the supplier selection problems. This study is organised as follows. Section 2 proposes some criterion for supplier selection. Section 3 reviews the ANP method, Taguchi loss Functions method, and MCGP method. Section 4 applies the integrated method to the supplier selection problem in the traditional medicine manufacturing company with a numerical example. Finally, Section 5 provides the conclusion of the study. Using a combination of three different methods (ANP, Taguchi lost function, MCGP) would cause the company to choose the best supplier with the lowest value on the loss of the most important criteria. It can’t be done if only using one method only. The supplier may change if the selected criteria considered important for the company is changed or if the value of quality loss is changed or both of them are changed.

Comment [t4]: Author: Please provide full reference or delete from the text if not required.

4 A. Susanty et al.

2 Criterion for supplier selection

To establish the criteria for supplier selection, relevant literature review was conducted. Supplier selection criteria are generally chosen based on the relevant field of work. This study uses six criteria for supplier selection, namely quality, price, delivery, services, organisational factor, and environmental factor.

• Quality

Definition of quality is given by so many authors in the field of quality management. Deming (1986) defines quality as meeting or exceeding customer satisfaction with the product or service. According to Dickson (1966) and Weber et al. (1991), quality is part of the most important criteria in supplier selection. Beside Dickson (1996) and Weber et al. (1991), almost all of the authors in the supplier selection process agree that quality is part of the most important criteria (Gallego, 2011). It is because the success of the buying organisation is highly dependent on how well the suppliers perform. It is also important that the supplier and the buyer have the same idea of what satisfactory quality is (Leenders and Fearon, 2010). They need to agree on: the basic requirements of the transaction, the way in which the requirements are to be realised, how to check that the requirements are fulfilled and the measures to be taken when the expectations are not met (Van Weele, 2010).

In this study, quality criteria consist of three sub-criteria namely percentage target of defect parts, return rate, and quality-related certificates. The third sub-criteria is an important sub-criteria to be considered by the buyer as the supplier certification programme may lead to lax market monitoring as buyers and sellers continue to move toward the long-term relational contracts (Teli et al., 2013).

• Price

Quality in itself is not sufficient to ensure that the suppliers can avoid extra costs while offering the right quality. Purchase price is also a significant factor for the purchasing organisation. In 1998, 92% of buyers responding to a purchasing magazine survey cited negotiating price as one of their top responsibilities. Nearly as many respondents said price remains a key criterion they use to select a supplier (Kotler and Keller, 2002).

In this study, price criteria consist of two sub-criteria namely offering price and quantity discount. Offering price refers to the comparison between the sale price offered by the supplier with a purchase price demanded by the company, whereas, discount price refers to price reductions provided by the supplier after negotiations.

• Delivery

Performance delivery describes the efficiency rate of business operations when preparing and delivering an order to a customer (Gallego, 2011).

In this study, delivery criteria consist of three sub-criteria, namely lead time, on time delivery rate, and delivery flexibility. Lead time defined as the time it takes from the moment an order is placed until it arrives. In related to performance of suppliers, lead time refers to the capability of the supplier to meet specification limits of delivery delay which allowed by the company. On time delivery rate referring to the

Comment [t5]: Author: Please confirm the year of publication (whether Leenders and Fearon, 2010 or 1997). Reference entry: Leenders, M. and Fearon, H. (1997) Purchasing & Supply Management, Irwin, Chicago.

Optimisation of supplier selection 5

frequency of occurrence of delay in delivery of goods by the supplier; whereas delivery flexibility refers to the capability of the supplier to always fulfil the order from the company.

• Service

Buyers and suppliers of manufactured products appear to agree that service is increasing in importance related to product and price issues. A manufacturer, even the best manufacturer in the world, who can’t respond to customers’ needs, is not going to survive. The effort by manufacturers to be more customers focused must be matched by buying organisations ensuring that their suppliers are giving them the level of service that is required and at reasonable cost (Donaldson, 1994). He defined service as all those activities provided by the seller that enhance or augment the product and have value for the buyer, thus increasing customer satisfaction and encouraging patronage and loyalty between the parties, is increasing in importance to buyers.

In this study, service criteria consist of three sub-criteria, namely warranty, legal procedure, and communication. Warranty refers to the extent to which the supplier is able to provide guarantees if the goods delivered are not in accordance with the specifications. Legality procedure refers to the extent to which the supplier is able to carry out cooperation in the procurement of goods in accordance with established procedures, whereas communication refers to the extent to which the supplier is able to provide ease in communication between the suppliers of the company; suppliers can act quickly in response to complaints and suggestions. The ease of communication and negotiability with the suppliers decide the long-term relation between the supplier and manufacturer. Since languages, business customs, ethics and communication devices vary from country to country, good suppliers should be best communicators; good message in good time (Mwikali and Kavale, 2012).

• Organisational factor (supplier’s organisation)

To actually see if an adequate level of quality of supplier is achievable, the buyer may have to look deeply into the supplier’s organisation to ensure the supplier is capable and competent to meet the buyer’s specifications. In this study supplier’s organisation criteria consists of five sub-criteria, namely historical performance, technical capability, financial capability, resource availability, and production capacity. Historical performance refers to the performance shown by suppliers in the past. Technical capability refers to the extent to which the supplier is capable to create and respond to emerging technologies, and affects the level of supplier involvement in the customer’s product development. Financial capability refers to refers to the equity, financial position, financial management and operation abilities available from the supplier, as reflected by their net assets and their asset/liability ratio. Resource availability refers to the extent to which the supplier is capable to provide resources which is required for the supply of goods to the company; whereas production capacity refers to the amount of product that can be generated by a supplier in a given period by using current resources

6 A. Susanty et al.

• Environmental factor

Due to the increasing realisation of the importance of integrating environmental factors into assessing supplier’s performance, a number of researchers have begun to identify some possible environmental criteria and sub-criteria. Lamming and Hampson in 1996 have attempted to provide provisional guidelines for evaluating suppliers according to environmental management issues. These researchers concluded that there was no coordinated response to dealing with the environment and each of the companies in the study had used a different approach. Noci in 1997 suggested a preliminary framework that identifies measures for assessing environmental performance but little emphasis was placed on environmental cost data (Humphreys et al., 2003). For the supplier selection decision, environmental factors may be introduced from various perspectives, e.g., a company level perspective versus a regional location perspective. Environmental factors can be categorised into multiple categories such as environmental performance and environmental practices adopted by organisations. Environmental practices may refer to policies and procedures, such as monitoring discharges and periodical audits; while environmental performance is in reference to resource consumption and pollution production (Gauthier, 2005).

In this study, environmental factor criteria consist of three sub-criteria, namely pollution control, environment-related certificates, and green process planning. Pollution control refers to the extent to which the supplier is capable to control wastes generated from the process of production. Environment-related certificates refers to the extent to which the supplier has a certificate in the field of management of the environment, such as ISO 14000, whereas green process planning refers to the level of green process planning of the supplier so activity of production which is conducted by supplier do not give negative impact to the environment.

3 Integrated proposed model for supplier selection

In the proposed method, each sub-criterion (which belongs to six criteria) is assigned an importance weight via ANP analysis. Then the 19 of sub-criteria are incorporated into the Taguchi loss functions to estimate the total loss. In the final step, importance weight and estimated total loss of suppliers are incorporated into MCGP model to identify the best supplier.

3.1 Analytic network process

AHP is one of the widely used approaches to handle such a multicriteria decision-making problem (Saaty, 1980). However, a significant limitation of AHP is the assumption of independence and structured hierarchically among various criteria. In the real problem, many decisions cannot be structured hierarchically when the interaction of higher level elements with lower level elements and their dependency should be taken into account. The ANP provides a solution for problems which cannot be structured hierarchically. Not only does the importance of the criteria determine the importance of the alternatives, as in a hierarchy, the importance of the alternatives themselves determine the importance of

Comment [t6]: Author: Please provide full reference or delete from the text if not required.

Comment [t7]: Author: Please provide full reference or delete from the text if not required.

Optimisation of supplier selection 7

the criteria (Saaty, 1996). The structural differences between a network and a hierarchy can be seen in Figure 1 (Kasirian et al., 2010; Görener, 2012).

Figure 1 Structural difference between (a) a hierarchy and (b) a network

(a) (b)

The process of ANP comprises four major steps (Saaty, 1996; Hsu and Kuo, 2011; Ayag, 2011; Fan and Chen, 2012).

• Step 1: Model construction and problem structuring.

The problem should be stated clearly and decomposed into a rational system like a network. The structure can be obtained by the opinion of decision makers through brainstorming or other appropriate methods. Figure 1(b) shows an example of the network format.

• Step 2: Pairwise comparisons matrices and priority vectors.

The ANP decision elements at each component are compared pairwise with respect to their control criterion, and the components themselves are also compared pairwise with respect to their contribution to the goal.

Saaty (1980) has suggested a scale of 1 to 9 when comparing two components. A score of 1 represents the criteria have same importance or indifference where a score of 9 indicates complete dominance to the comparison criteria in a pairwise comparison matrix. If criteria have some level of weaker impact than its comparison criteria the range of the scores will be from 1 to 1/9, where 1 indicates indifference and 1/9 represents an extreme importance by one criterion (row component in the matrix) compared to the other criteria (column component in the matrix). Thus, the value a12 for = 2, whereas a21 = 1/2.When scoring is conducted for a pair, a reciprocal value is automatically assigned to the reverse comparison within the

8 A. Susanty et al.

matrix. That is, aij is a matrix value assigned to the relationship of ith element to jth element, then denotes aij = 1/aji, Once all the pairwise comparisons are complete, the relative importance weight for each component is determined. Given that A is the pairwise comparison matrix; the weights can be determined by equation (1).

maxA w λ w⋅ = ⋅ (1)

where A denotes the matrix of pairwise comparison, w represents the eigenvector, and λmax is the largest eigenvalue of A (Saaty, 1980). Saaty (1980) provides several algorithms for approximating w. In this study a two-stage algorithm to solve for the largest eigenvalue: the first one is the construction of the network (Step 3), and the second one is the calculation of the priorities of the elements (Step 4).

• Step 3: Supermatrix formation.

The supermatrix concept resembles the Markov chain process (Saaty, 1996). To obtain global priorities in a system involving interdependent influences, the local priority vectors are entered into the appropriate columns of a matrix, known as a supermatrix. Consequently, a supermatrix is actually a partitioned matrix, where each matrix segment represents a relationship between two nodes (components) in a system (Meade and Sarkis, 1998). Let the components of a decision system be Ck; k = 1… n, where each component k has mk elements, denoted by ek1, ek2,… ,ekmk. The local priority vectors derived in Step 2 are grouped and located in appropriate positions in a supermatrix based on the flow of influence from one component to another, or from a component to itself, as in the loop. The standard form of a supermatrix resembles that in (2) (Saaty, 1996).

1 1

11

12

1 1 1

12 11 1 1 1

121 11 1 1

1

2

11 1 1

1

2 1

.. .. .. n

k

n

k n

m k km n nm

k n

m

kk

k n

km

n

n

n k nk nn

nm

ee

C C Ce e e e e e eeC W W W

ew eC

W W We

Cee W W W

e

= (2)

If the criteria are interrelated, the (2, 2) entry of Wn given by W22 would indicate the interdependency, and the supermatrix would be (Saaty, 1996).

Optimisation of supplier selection 9

21 22

32

0 0 00

0w w w

w I= (3)

Notably, any zero in the supermatrix can be replaced by a matrix if there is an interrelationship among the in a component or between two components. Since interdependence generally exists among clusters in a network, the columns of a supermatrix usually total more than one. The supermatrix must be transformed first to make it stochastic; that is, each column of the matrix sums to unity. Saaty (1996) recommended determining the relative importance of the clusters in the supermatrix with the column cluster (block) as the controlling component (Meade and Sarkis, 1998). That is, the row components with non-zero entries for their blocks in that column block are compared according to their impact on the component of that column block (Saaty, 1996). Through pairwise comparison matrix of the row components with respect to the column component, an eigenvector can be obtained. This process obtains an eigenvector for each column block. For each column block, the first entry of the respective eigenvector is multiplied by all the elements in the first block of that column, the second entry is multiplied by all the elements in the second block of that column and so on. The block in each column of the supermatrix is thus weighted, and the result is termed the weighted supermatrix, which is stochastic.

Raising a matrix to powers gives the long-term relative influences of elements on each other. To achieve convergence of the importance weights, the weighted supermatrix is raised to the power of 2k + 1; where k is an arbitrarily large number, and this new matrix is called the limit supermatrix (Saaty, 1996). The limit supermatrix has the same form as the weighted supermatrix, but all the columns of the limit supermatrix are the same. Normalising each block of this supermatrix can obtain the final priorities of all the elements in the matrix.

• Step 4: Selection of best alternatives.

If the supermatrix formed in Step 3 covers the whole network, the priority weights of alternatives can be found in the column of alternatives in the normalised supermatrix. On the other hand, if a supermatrix only comprises interrelated components, additional calculations must be performed to obtain the overall priorities of the alternatives. The alternative with the largest overall priority should be the one selected. This study applies the first method, and a supermatrix that covers the whole network. In this study, the fourth step is not performed because the ANP method is only used to determine the importance weight of each sub-criterion, while the selection of suppliers is done by using a method MCGP.

3.2 Taguchi loss functions

Taguchi methods were developed by Genichi Taguchi and impact of Taguchi upon North American product design and manufacturing processes was first observed in November 1981. This introduced the concept of loss function as a measure of quality. Taguchi defines quality in a negative manner as “the loss imparted to society from the time the product is shipped” (Mishra and Gangele, 2012). Taguchi proposed a narrower view of

10 A. Susanty et al.

characteristic acceptability to indicate that any deviation from a characteristic’s target value results in a loss and a higher quality measurement is one that will result in minimal variation from the target value. For example, the loss is zero when the characteristic’s measurement is the same as the target value. The loss can be measured using a quadratic function and action is taken to systematically reduce the variation from the target value (Kethley and Waller, 2002).

Taguchi’s loss function is classified into three types of functions: nominal-is-best characteristics, smaller-is-better characteristics and large-is-better characteristics. The proper function depends on the magnitude of variation and the variation is allowed in both directions from the target value. This target can be the centre within two-sided specification limits, called the two-sided equal or nominal-is-best loss function (see Figure 2) and it can be formulated in equation (4) (Liao and Kao, 2010)

2( ) ( )L y k y m= − (4)

where L(y) is the loss associated with a particular value of equality character y; m is the nominal value of the specification; k is the average loss coefficient, and its value is a constant depending on the cost at the specification limits and the width (e.g., m ± Δ) of the specification; where Δ is the customer’s tolerance. In addition, the other two loss functions include the one-sided minimum and the one-sided maximum specification limit functions, called the smaller-is-better and the larger-is-better loss functions (see Figures 3 and 4) which are formulated in equations (5) and (6) respectively (Liao and Kao, 2010).

Figure 2 Nominal-is-best loss function

Figure 3 Smaller-is-better loss function

Optimisation of supplier selection 11

Figure 4 Higher-is-better loss function

2 2( ) .( ) , / ΔL y k y k A= = (5)

2 2( ) / , ΔL y k y k A= = (6)

where A is average quality loss; and all other variable are the same as those in the nominal-is-best loss function

3.3 Multi-choice goal programming

According to Chang in 2007, a MCGP problem can be stated in the following mode (Liao and Kao, 2010):

11

( ) or or orn

i i n i imi

Min w If X g g g=

−∑ …… (7)

. . ( , )s t X F F is a feasible set X is unrestricted in sign∈

where F is a feasible set, X is an element of F, fi(X) is the linear function of the ith goal, gij (i = 1,2,,......,n and j = 1,2,.......,m) is the jth aspiration level of the ith goal, gij–1 ≤ gij ≤ gij+1.

According to Chang in 2008, the MCGP can be reformulated as the following two alternative MCGP-achievement functions. The first case: ‘the more the better’ is formulated as (Liao & Kao, 2010):

( ) ( )1

n

i i ii ii

Min w d d e e+ − + −

=

⎡ ⎤+ + +⎣ ⎦∑ α

s.t.

( ) 1, 2i i Iif X d d y i n+ −− + = = …… (8)

,max 1,2i i iiy e e g i n+ −− + = = …… (9)

,min ,maxi i ig y g≤ ≤

, , , 0 1,2,i ii id d e e i n+ − + − ≥ = ……

Comment [t8]: Author: Please provide full reference or delete from the text if not required.

12 A. Susanty et al.

( , )X F F is a feasible set X is unrestricted in sign∈

where id + and id − are the positive and negative deviation attached to the ith goal |fi(X) – yi| in equation (8); ie+ and ie− are the positive and negative deviation attached to |yi – gi,max| in equation (9); αi is the weight attached to the sum of the deviation of |yi – gi,max|; all other variables are defined as in MCGP.

The second case: ‘the less the better’ is formulated as (Liao and Kao, 2010):

( ) ( )1

n

i i ii ii

Min w d d e e+ − + −

=

⎡ ⎤+ + +⎣ ⎦∑ α

s.t.

( ) 1,2,i i iif X d d y i n+ −− + = = …… (10)

,min 1,2,i i iiy e e g i n+ −− + = = …… (11)

,min ,maxi i ig y g≤ ≤

, , , 0 1,2,i ii id d e e i n+ − + − ≥ = ……

( , )X F F is a feasible set X is unrestricted in sign∈

where id + and id − are the positive and negative deviation attached to the ith goal |fi(X) – yi| in equation (10); ie+ and ie− are the positive and negative deviation attached to |yi – gi,max| in equation (11); αi is the weight attached to the sum of the deviation of |yi – gi,max; all other variables are defined as in MCGP. In this study, the weight attached to the sum of the deviation of |yi – gi,max| and |yi – gi,min| or αi will be calculated using ANP.

4 Selecting the best supplier: an illustrative problem

The integrated proposed model for supplier selection (Taguchi loss function, ANP, and MCGP) applied to the select the best suppliers of PT.X. PT. X is one of the major companies in Indonesia which produce the herbs (traditional medicine). Six suppliers (suppliers A, B, C, D, E, and F) are identified and 19 of decision sub-criteria (percentage target of defect parts, return rate, quality-related certificates, offering price, quantity discount, lead time, on time delivery rate, delivery flexibility, warranty, legal procedure, communication, historical performance, technical capability, financial capability, resource availability, production capacity, pollution control, environment-related certificates, and green process planning) from six criteria are determined to select those alternatives of supplier. This study begins with assigning a importance weight of each sub-criterion via ANP analysis, then, formulating the Taguchi loss functions to estimate the total loss of each supplier, and finally, this study incorporating the results (importance weight and estimated total loss) into the MCGP model.

Optimisation of supplier selection 13

4.1 Assigning a importance weight of each sub-criterion via ANP analysis

The ANP model structured for six criteria and 19 sub-criteria for supplier selection can be seen in Figure 5.

Figure 5 ANP model structured for six criteria and 19 sub-criteria for supplier selection (see online version for colours)

According to the ANP model structured for the supplier selection at PT.X, the next step is to perform pairwise comparisons between cluster, criteria, sub-criteria and alternatives as per linkage provided in ANP model. To determine the importance of the decision criteria with respect to the overall objective, the questionnaire which consists of pairwise comparison between each criterion is made. The scale used for this subjective judgment is 1 to 9 and the decision maker (which is two experts from the Procurement Department) asked to determine which criterion contributes more to the objective of supplier selection at PT. X and the selection of the best supplier alternatives. The sample question may be – with respect to the goal of selecting the best supplier for the procurement of ginger as raw material for herbal, what is the relative importance of quality to price. If the answer is (1/9) means the decision maker believes price is – extremely important- relative to quality. Once all the pairwise comparisons are complete, the priority weight vector (w) is computed, the consistency ratio (CR) of the pairwise comparison matrix is calculated, the weighted supermatrix is constructed, and the global priority weight is calculated.

From the ANP computation, the CR of the pairwise comparison and the importance weight for each sub-criterion can be seen as in Table 1 and Table 2.

The CR introduced by Saaty for the AHP is used to test the consistency of the pairwise comparison in the ANP. If the value of CR is less than 0.1, this indicates the pairwise comparison matrix achieves satisfactory consistency. As the value of CR of all clusters is less than 0.1 (see Table 1), the judgments are acceptable.

14 A. Susanty et al.

Table 1 The CR of the pairwise comparison

Respondent Cluster Consistency ratio Respondent Cluster Consistency

ratio 1 Goal 0.0541 2 Goal 0.0442

Quality 0.0624 Quality 0.0624 Price 0.0000 Price 0.0000

Environmental factor

0.0370 Environmental factor

0.0624

Services 0.0370 Services 0.0280 Delivery 0.0516 Delivery 0.0280

Organisational factor

0.0548 Organisational factor

0.0529

Table 2 The importance weight for each sub-criterion

No. Sub-criteria Normalised

Importance weightExpert 1 Expert 2

1 Percentage target of defect parts 0.2264 0.2370 0.232 2 Offering price 0.2245 0.1716 0.198 3 Quality-related certificates 0.0838 0.0883 0.086 4 Delivery flexibility 0.0664 0.0692 0.068 5 Return rate 0.0408 0.0724 0.057 6 Lead time 0.0591 0.0533 0.056 7 Warranty 0.0621 0.0373 0.050 8 Communication 0.0468 0.0540 0.050 9 Green process planning 0.0462 0.0496 0.048 10 Quantity discount 0.0306 0.0234 0.027 11 Technical capability 0.0172 0.0271 0.022 12 Environment-related certificates 0.0191 0.0203 0.020 13 Financial capability 0.0179 0.0209 0.019 14 On time delivery rate 0.0169 0.0152 0.016 15 Resource availability 0.0153 0.0148 0.015 16 Legal procedure 0.0063 0.0239 0.015 17 Production capacity 0.0125 0.0117 0.012 18 Historical performance 0.0061 0.0074 0.007 19 Pollution control 0.0020 0.0026 0.002

From the result of computation of the importance weight for each sub-criterion, we can conclude that the priorities of the sub-criteria reveal the importance of percentage target of defect parts, which are followed by offering price, quality-related certificates, delivery flexibility, return rate, lead time, warranty, communication, green process planning, quantity discount, technical capability, environment-related certificates, financial capability, on time delivery rate, resource availability, legal procedure, production capacity, historical performance, and pollution control.

Optimisation of supplier selection 15

4.2 Formulating the Taguchi loss functions to estimate the total loss

These 19 sub-criteria used to select the suppliers can be divided into two, namely quantitative sub-criteria and qualitative sub-criteria. Belong to quantitative sub-criteria are the percentage target of defect parts, offering price, return rate, lead time, and on time delivery rate, production capacity, and quantity discount. Belong to quantitative sub-criteria are quality-related certificates, delivery flexibility, warranty, communication, green process planning, technical capability, environment-related certificates, financial capability, resource availability, legal procedure, historical performance, and pollution control.

Concerning the quantitative sub-criteria, the company sets the percentage of defect parts at zero and the upper specification limit could be set to 0.8% to indicate the allowable deviation from the target value. Zero loss will occur for 0% defective parts and 100% loss will occur at the specification limit of 2% defective parts. For offering price, the loss will be zero for supplier who offers the lowest price (60,000 IDR1/kg) and the specification limit is 58% more than the lowest price required by the company (95,000 IDR/kg). For return rate, the loss will be occur for 0% return rate and 100% loss will be occur at the specification limit of 8% return rate (return rate occurs once in 12 times shipping). For lead time (delivery time), the company sets the specification limit of delivery delay as five working days, meaning that 100% loss occurs if the supplier’s delivery time delay is five working days. For on time delivery rate, the loss will be zero for a supplier who never late on delivery product or 100% on time delivery rate and 100% loss will occur at the specification limit of 70% on time delivery rate. The specification limit and range value of quantitative sub-criterion are presented in Table 3. Table 3 The specification limit and range value of quantitative sub-criteria

Target value Specification limit Range Percentage of defect parts 0% 0.8% 0–0.8% Offering price 0% lowest 58% higher 0–58% Return rate 0% 8% 0–8% Lead time 0 5 0–5 On time delivery rate 100% 70% 100–70%

For calculating the value of k (average loss coefficient) from equation (5) or equation (6), gives a value of 23,437,500,000 IDR, 35,000 IDR, 50,000,000 IDR, 20,000 IDR and 122,2222,222 IDR for average cost due for losses caused by defect parts, offering price, return rate, lead time (delivery time), and on time delivery rate. The example of numerical calculation of the variable k from equation (5) or equation (6) for defect price can be described as follows. The value of target of defects fordeliveredgingeris0% and the upper specification limit of defect parts is 0.8% .Company has calculated the costs incurred for control of defective products is 1,500,000 IDR. Based on this condition, the value of A is (average quality loss) IDR 1,500,000, the value of Δ (the customer’s tolerance) is 0.008, and value of k is 23,437,500,0002 IDR.

Then, for calculating loss caused by a particular sub-criteria (value of L (y)) for each supplier, we need to multiply the value of k (average loss coefficient) by the performance of each supplier related to each sub-criterion for some period of observation. Example, for calculating loss caused by a sub-criteria percentage of defect parts for a supplier A,

16 A. Susanty et al.

we must multiply the average loss coefficient caused by sub-criteria percentage of defect parts with performance of supplier A related to percentage of defect parts for some period. So, the value of loss caused by a sub-criteria percentage of defect parts for a supplier A equal to:

2 2 2

2

1.500.000 0.0024 0.0027 0.0028 114,003.(0.008) 12

L IDR+ + += × =

………

where 0.0024, 0.0027, until 0.0028 are the percentage of defective parts from supplier A during one year. Table 4 summarises the result of the Taguchi loss function (value of L) for the each of the quantitative sub-criterion (defect parts, offering price, return rate, lead time (delivery time), and on time delivery rate). Table 4 Supplier characteristic Taguchi loss

Supplier Percentage of defect parts (IDR)

Offering price (IDR)

Return rate (IDR)*)

Lead time (IDR)

On time delivery rate (IDR)

A 247,275 114,004 - 613,333 1,662,887 B 248,740 106,133 - 650,000 2,078,609 C 238,183 803,985 - 653,333 2,494,331 D 237,536 913,184 - 650,000 2,078,609 E 241,053 624,219 - 591,667 1,039,305 F 241,441 838,711 - 631,667 1,870,748

Note: *All suppliers have 0% return rate during the observation period.

Different from five sub-criteria above, this study didn’t calculate the value of L or Taguchi Loss Function for sub-criteria production capacity and quantity discount. In this case, the company does not depend on only one supplier; the company has a policy to buy from several suppliers to minimise the risk of the inability of suppliers to meet the quantity demanded. The assumption is the quantity demanded will fulfilled from several suppliers and there is no lose because the quantity is not fulfilled. So, for capacity of production, value that incorporate into the MCGP is a number of the real capacity of each supplier. Associated with the quantity discount, the company does not lose anything if discount is not given by the supplier and the company also does not lose anything if the discount is given. So, for capacity of production, value that incorporate into the MCGP is a number of discounts which is given by each supplier.

Concerning the qualitative sub-criteria, Monczka and Trecha (1988) proposed a service factor rating (SFR) to measure performance factors that are difficult to quantify from a cost point of view, but they are important to the supplier’s competition. In this study, factors that are difficult to quantify from a cost point of view can be described as follows quality-related certificates, delivery flexibility, warranty, communication, green process planning, technical capability, environment-related certificates, financial capability, resource availability, legal procedure, production capacity, historical performance, and pollution control. These factors are qualitative sub-criteria. In practice, these performance factors can be rated by the appropriate personnel from purchasing, quality control, manufacturing or product engineering departments. For a given supplier, then, his ratings on all factors are summed, and then averaged to gain a total service rating (Pi and Low, 2005). In this study, two experts from Procurement Department are

Optimisation of supplier selection 17

asked to rate this factors (qualitative sub-criteria) and the result can be seen in Table 5. For service satisfaction, the company set the specification limit of the supplier’s service factor percentage of qualitative sub-criteria at 70%. At this time, the loss will be 100%. Also zero loss will occur if the supplier’s service factor percentage of qualitative sub-criteria is 100%. Table 5 Value of SFR of each qualitative sub-criterion

Sub-criteria Average SFR of supplier

A B C D E F Quality-related certificates 0.982 0.964 0.827 0.794 0.766 0.823 Delivery flexibility 0.935 0.914 0.924 0.890 0.929 0.931 Warranty 0.886 0.896 0.882 0.789 0.755 0.949 Communication 0.927 0.929 0.820 0.946 0.760 0.871 Green process planning 0.717 0.875 0.665 0.920 0.869 0.796 Technical capability 0.914 0.906 0.786 0.912 0.830 0.957 Environment-related certificates 0.781 0.839 0.749 0.886 0.915 0.826 Financial capability 0.819 0.820 0.953 0.986 0.903 0.935 Resource availability 0.841 0.945 0.944 0.940 0.962 0.828 Legal procedure 0.952 0.967 0.88 0.741 0.897 0.778 Historical performance 0.968 0.823 0.767 0.935 0.912 0.835 Pollution control 0.784 0.778 0.833 0.863 0.948 0.706

4.3 Incorporating the results (importance the weight and estimated the total loss) into the MCGP model

Finally, we incorporate the results (e.g., importance weight and estimated total loss) into the MCGP model as formulated in Table 6 to identify the best supplier. Then, based on the MCGP model formulation, the supplier’s selection problem can be solved using LINGO (Schrage, 2002). The results show that supplier B is the best selection, followed by supplier F, supplier D, supplier A, supplier E, and supplier C.

4.4 Sensitivity analysis

In this section the sensitivity analysis is conducted only to see the effect of a change of price in selecting the best supplier. In this case we use two conditions. The first scenario assumed that the price is increased by 5% and the second scenario assumed that the price is decreased by 5%. Although the value of loss function has been changed, the sensitivity analysis shows the stability of the derived results of supplier selection. Supplier B is still the best, followed by supplier F, supplier D, supplier A, supplier E, and supplier C. The result of sensitivity analysis on price can be seen in Figure 6.

18 A. Susanty et al.

Table 6 MCGP formulation

()

()

()

()

()

()

()

()

()

()

()

()

11

22

33

44

55

66

77

88

99

1010

1111

1212

13

0.19

80.

027

0.23

20.

057

0.08

6

004

80.

002

0.02

0.00

7

0.01

20.

0229

0.01

90.

015

Min

zd

ed

ed

ed

ed

e

.d

ed

ed

ed

e

de

de

de

d

−−

−−

−−

−−

−−

−−

−−

−−

−−

−−

−−

−−

−

=+

++

++

++

++

++

++

++

++

++

++

++

+(

)(

)(

)(

)(

)(

)(

)

13

1414

1515

1616

1717

1818

1919

0.05

0.05

0.01

50.

068

0.05

6

0.01

6

e

de

de

de

de

de

de

−

−−

−−

−−

−−

−−

−−

+

++

++

++

++

++

++

12

34

56

11

124

7274

.64

2487

39.5

723

8183

2375

3624

1052

.71

2411

44.3

6x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r offe

ring

pric

e go

al, t

he le

ss

the

bette

r

11

123

7536

.36

ye

e+

−−

+=

Fo

r |y 1

–g1,

min

|

s.t.

123

7536

.36

2487

39.5

7y

≤≤

Fo

r bou

nd o

f y1

12

34

56

22

21

1.2

21.

82

1.5

xx

xx

xx

dd

y+

−+

++

++

−+

=

For q

uant

ity d

isco

unt g

oal,

the

mor

e th

e be

tter

22

22

ye

e+

−−

+=

Fo

r |y 2

–g2,

max

|

21

2y

≤≤

Fo

r bou

nd o

f y2

12

34

56

33

311

4003

.91

1061

32.8

180

3984

.38

9131

83.5

962

4218

.75

8387

10.9

4x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r per

cent

age

of d

efec

t par

ts

goal

, the

less

the

bette

r

33

310

6132

.81

ye

e+

−−

+=

Fo

r |y 3

–g3,

min

|

310

6132

.81

9131

83.5

9y

≤≤

Fo

r bou

nd o

f y3

12

34

56

44

40

00

00

0x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r ret

urn

rate

goa

l, th

e le

ss th

e be

tter

44

40

ye

e+

−−

+=

Fo

r |y 4

–g4,

min

|

12

34

56

55

598

9683

7977

82x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r qua

lity–

rela

ted

certi

ficat

es

goal

, the

mor

e th

e be

tter

55

510

0y

ee

+−

−+

=

For |

y 5–g

5,m

ax|

570

100

y≤

≤

For b

ound

of y

5

Optimisation of supplier selection 19

Table 6 MCGP formulation (continued)

12

34

56

66

672

8866

9287

80x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r gre

en p

roce

ss p

lann

ing

goal

, the

mor

e th

e be

tter

66

610

0y

ee

+−

−+

=

For |

y 6–g

6,m

ax|

670

100

y≤

≤

For b

ound

of y

6

12

34

56

77

778

7883

8695

71x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r pol

lutio

n co

ntro

l goa

l, th

e m

ore

the

bette

r

77

710

0y

ee

+−

−+

=

For |

y 7–g

7,m

ax|

770

100

y≤

≤

For b

ound

of y

7

12

34

56

88

878

8475

8992

83x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r env

ironm

ent–

rela

ted

certi

ficat

es g

oal,

the

mor

e th

e be

tter

88

810

0y

ee

+−

−+

=

For |

y 8–g

8,m

ax|

870

100

y≤

≤

For b

ound

of y

8

12

34

56

99

998

8277

9491

83x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r his

toric

al p

erfo

rman

ce g

oal,

the

mor

e th

e be

tter

99

910

0y

ee

+−

−+

=

For |

y 9–g

9,m

ax|

970

100

y≤

≤

For b

ound

of y

9

12

34

56

1010

1010

5012

0017

0085

013

0070

0x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r pro

duct

ion

capa

city

goa

l, th

e m

ore

the

bette

r

1010

1017

00y

ee

+−

−+

=

For |

y 10–

g 10,

max

|

1070

017

00y

≤≤

Fo

r bou

nd o

f y10

12

34

56

1111

1191

9179

9183

96x

xx

xx

xd

dy

+−

++

++

+−

+=

Fo

r tec

hnic

al c

apab

ility

goa

l, th

e m

ore

the

bette

r

1111

1110

0y

ee

+−

−+

=

For |

y 11–

g 11,

max

|

1170

010

0y

≤≤

Fo

r bou

nd o

f y11

20 A. Susanty et al.

Table 6 MCGP formulation (continued)

12

34

56

1212

1282

8295

9990

94x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r fin

anci

al c

apab

ility

goa

l, th

e m

ore

the

bette

r

1212

1210

0y

ee

+−

−+

=

For |

y 12–

g 12,

max

|

1270

010

0y

≤≤

Fo

r bou

nd o

f y12

12

34

56

1313

1384

9594

9496

83x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r res

ourc

e av

aila

bilit

y go

al,

the

mor

e th

e be

tter

1313

1310

0y

ee

+−

−+

=

For |

y 13–

g 13,

max

|

1370

010

0y

≤≤

Fo

r bou

nd o

f y13

12

34

56

1414

1489

9088

7975

95x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r war

rant

y go

al, t

he m

ore

the

bette

r

1414

1410

0y

ee

+−

−+

=

For |

y 14–

g 14,

max

|

1470

010

0y

≤≤

Fo

r bou

nd o

f y14

12

34

56

1515

1593

9382

9576

87x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r com

mun

icat

ion

goal

, the

m

ore

the

bette

r

1515

1510

0y

ee

+−

−+

=

For |

y 15–

g 15,

max

|

1570

010

0y

≤≤

Fo

r bou

nd o

f y15

12

34

56

1616

1695

9788

7490

78x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r leg

al p

roce

dure

goa

l, th

e m

ore

the

bette

r

1616

1610

0y

ee

+−

−+

=

For |

y 16–

g 16,

max

|

1670

010

0y

≤≤

Fo

r bou

nd o

f y16

12

34

56

1717

1793

9192

8993

93x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r del

iver

y fle

xibi

lity

goal

, the

m

ore

the

bette

r

1717

177

100

ee

+−

−+

=

For |

y 17–

g 17,

max

|

1770

010

0y

≤≤

Fo

r bou

nd o

f y17

Optimisation of supplier selection 21

Table 6 MCGP formulation (continued)

12

34

56

1818

1891

3333

.33

6500

0065

3333

.33

6500

0059

1666

.67

6316

66.6

7x

xx

xx

xd

dy

++

++

++

+−

+=

Fo

r lea

d tim

e (d

eliv

ery

time)

go

al, t

he le

ss th

e be

tter

1818

1859

1666

.67

ye

e+

−−

+=

Fo

r |y 1

8–g 1

8,m

in|

1859

1666

.67

1633

33.3

3y

≤≤

Fo

r bou

nd o

f y18

12

34

56

1919

1916

6288

7.38

2078

609.

2224

9433

1.07

2078

609.

2210

3930

4.61

1870

748.

30x

xx

xx

xd

dy

++

++

++

+−

+=

For o

n tim

e de

liver

y ra

te g

oal,

the

less

the

bette

r

1919

1910

3930

4.61

ye

e+

−−

+=

Fo

r |y 1

9–g 1

9,m

in|

1910

3930

4.61

2494

331.

07y

≤≤

Fo

r bou

nd o

f y19

12

34

56

1;x

xx

xx

x+

++

++

=

10;

20;

30;

40;

50;

60;

xx

xx

xx

≥≥

≥≥

≥≥

10;

20;

30;

40;

50;

60;

70;

80;

90;

100;

dp

dp

dp

dp

dp

dp

dp

dp

dp

dp

≥≥

≥≥

≥≥

≥≥

≥≥

10;

110;

130;

140;

150;

160;

170;

180;

190;

dp

dp

dp

dp

dp

dp

dp

dp

dp

≥≥

≥≥

≥≥

≥≥

≥

10;

20;

30;

40;

50;

60;

70;

80;

90;

100;

dm

dm

dm

dm

dm

dm

dm

dm

dm

dm

≥≥

≥≥

≥≥

≥≥

≥≥

10;

110;

130;

140;

150;

160;

170;

180;

190;

dm

dm

dm

dm

dm

dm

dm

dm

dm

≥≥

≥≥

≥≥

≥≥

≥

22 A. Susanty et al.

Figure 6 The result of sensitivity analysis on price (see online version for colours)

5 Conclusions

The supplier selection processes are very important to organisations nowadays since choosing the one that fits best the company’s needs can bring significant savings and the quality of suppliers can also affect to company’s performance. The supplier selection processes can vary across companies depending on many factors and there are several methods that can be used by the company to select their supplier. Usually when a company sets out to develop or choose a supplier selection method, the result is a combination of several different methods with different strengths suited to meet the company’s specific selection needs. Related to a combination of several different methods for supplier selection, this study proposed an integrated method of the Taguchi Loss Function, ANP, and MCGP to select the best supplier of PT. X. First, this study begins with assigning a importance weight of each sub-criterion via ANP analysis. Second, formulating the Taguchi Loss Functions to estimate the total loss of each supplier, and finally, incorporating the results (importance weight and estimated total loss) into the MCGP model. Differences with the previous study that conducted by Liao and Kao (2010), there is an additional advantage of the integrated method proposed in this study. The integrated method proposed in this study not only allows decision makers to set multiple aspiration levels for the decision criteria; with the ANP method, the decision makers can also determine the importance weight of each criterion which is not independent but interrelated or interdependence with each other.

The current study presented exhibit limitations that should be considered for further research. In this study, a sensitivity analysis has not been done in all of the criteria for the selection of the best supplier. Research with the same theme can be done by adding a sensitivity analysis on other criteria used for supplier selection, except the price, in order to know the level of sensitivity of the optimal solution which has been obtained if there is a change in one or more parameter, except the increased or decreased of the price.

Optimisation of supplier selection 23

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Humphreys, P.K., Wong, Y.K. and Chan, F.T.S. (2003) ‘Integrating environmental criteria into supplier selection process’, Journal of Materials Processing Technology, Vol. 138, Nos. 1–3, pp.349–356.

Kasirian, M., Yusuff, R.M. and Ismail, M.Y. (2010) ‘Application of AHP and ANP in supplier selection process-a case in an automotive company’, International Journal of Management Science and Engineering Management, Vol. 5, No. 2, pp.125–135.

Kasirian, M.N. and Yusuff, R.M. (2009) ‘Determining interdependencies among supplier selection criteria’, European Journal of Scientific Research, Vol. 35, No. 1, pp.76–84.

Kethley, R.B. and Waller, T.A. (2002) ‘Improving customer service in the real estate industry: a property selection model using Taguchi loss’, Total Quality Management, Vol. 13, pp.739–748.

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Comment [t10]: Author: Please confirm the year of publication (whether 2010 or 1997).

Comment [t11]: Author: Please provide the issue number.

24 A. Susanty et al.

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AMENDMENTS TO PROOF

JOURNAL: Int. J. Agile Systems and Management

AUTHORS NAME: Aries Susanty, Ardian Bagus Putranto, Ferry Jie

PAPER TITLE: Optimisation of supplier selection with Taguchi loss function, analytic network process, and

multi-choice goal programming

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confirm if A.

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2 Author School of Business IT

and Logistics,

RMIT University,

Melbourne Please

provide full

mailing address.

The full miling address:

School of Business IT

and Logistics

College of Business

RMIT University

Melbourne VIC 3001,

Australia

Fax: 61-3-99255850

3 Abstract This paper is a revised

and expanded version of

a paper entitled [title]

presented at [name,

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conference please

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4 Introduction 3 17 Surazi (2012) Please

provide full reference

or delete from the text

if not required

Add:

Surazi and Rahim (2012)

Surazi and Rahim, A.

(2012) ‘A supplier

selection criteria within

the Malaysian

telecommunications

industry’, Proceeding of

Brunel Business School

Doctoral Symposium,

27–28 March, London,

United Kingdom.

5 Criterion for

supplier

1 14 Leenders and Fearon,

2010 Please confirm

the year of publication

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publication is 1997

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selection

(quality)

(whether Leenders and

Fearon, 2010 or 1997).

Reference entry:

Leenders, M. and

Fearon, H. (1997)

Purchasing &

Supply Management,

Irwin, Chicago

Leenders and Fearon,

1997

Leenders, M. and Fearon,

H. (1997) Purchasing &

Supply Management,

Irwin, Chicago

6 Criterion for

supplier

selection

(environmental

factor)

1 3 Lamming and Hampson

in 1996 Please

provide full

reference or delete

from the text if not

required

Add in references

Lamming, R. and

Hampson, J. (1996) ‘The

environment as a supply

chain management issue’,

British Journal of

Management, Vol. 7,

No.1, pp. S45-S62.

7 Criterion for

supplier

selection

(environmental

factor)

1 7 Noci in 1997Please

provide full reference

or delete from the text

if not required.

Add in references

Noci, G. (1997)

‘Designing green vendor

rating systems for the

assessment of a supplier

environmental

performance’, European

Journal of Purchasing

and Supply Management,

Vol. 3, No. 1, pp.103-114

8 References Chang in 2008Please

provide full reference

or delete from the text

if not required.

Add in references

Chang, C. T. (2008)

‘Revised multi-choice

goal programming’,

Applied Mathematical

Modelling, Vol. 32,

No.12, pp. 2587–2595.

9 References Kethley, R.B. and

Waller, T.A. (2002)

‘Improving customer

service in the real estate

industry: a

property selection

model using Taguchi

loss’, Total Quality

Management, Vol. 13,

pp.739–748 Please

provide the issue

number

Add issue number

Kethley, R.B. and

Waller, T.A. (2002)

‘Improving customer

service in the real estate

industry: a

property selection model

using Taguchi loss’,

Total Quality

Page

No.

Section Paragraph Line

No.

Delete Add/amend

Management, Vol. 13,

No. 6, pp.739–748

10 References Liao, C.N. and Kao,

H.P. (2010) ‘Supplier

selection model using

Taguchi loss function,

analytical

hierarchy process and

multi-choice goal

programming’,

Computers & Industrial

Engineering, Vol. 58,

pp.571–577 Please

provide the issue

number

Add issue number

Liao, C.N. and Kao, H.P.

(2010) ‘Supplier

selection model using

Taguchi loss function,

analytical

hierarchy process and

multi-choice goal

programming’,

Computers & Industrial

Engineering, Vol. 58,

No. 1, pp.571–577

11 References Reinecke, N., Spiller, P.

and Ungerman, D.

(2007) ‘The talent factor

in purchasing’, The

McKinsey Quarterly,

Vol. 1, pp.6–9 Please

provide the issue

number

Add issue number (“1”

is not a volume but an

issue number)

Reinecke, N., Spiller, P.

and Ungerman, D. (2007)

‘The talent factor in

purchasing’, The

McKinsey Quarterly, No.

1, pp.6–9

12 References Schrage, L. (2002)

LINGO Release 8.0,

LINDO System, Inc.,

Chicago --> Please

provide the

place of publication

Add place of

publication

Schrage, L. (2002)

LINGO Release 8.0,

LINDO System, Inc.,

Chicago

13 References Weber, C.A., Current,

J.R. and Benton, W.C.

(1991) ‘Vendor

selection criteria and

methods’, European

Journal of Operational

Research, Vol. 50, pp.2–

18 Please provide

the issue number

Add issue number:

Weber, C.A., Current,

J.R. and Benton, W.C.

(1991) ‘Vendor selection

criteria and methods’,

European Journal of

Operational Research,

Vol. 50, No. 1, pp.2–18

Add New References

Surazi and Rahim, A. (2012) ‘A supplier selection criteria within the Malaysian telecommunications

industry’, Proceeding of Brunel Business School Doctoral Symposium, 27–28 March, London,

United Kingdom.

Lamming, R. and Hampson, J. (1996) ‘The environment as a supply chain management issue’,

British Journal of Management, Vol. 7, No.1, pp. S45-S62.

Noci, G. (1997) ‘Designing green vendor rating systems for the assessment of a supplier

environmental performance’, European Journal of Purchasing and Supply Management, Vol. 3,

No. 1, pp.103-114

Chang, C. T. (2008) ‘Revised multi-choice goal programming’, Applied Mathematical

Modelling, Vol. 32, No.12, pp. 2587–2595

Change year of publication

Leenders and Fearon, 1997

Leenders, M. and Fearon, H. (1997) Purchasing & Supply Management, Irwin, Chicago

Add place of publication

Schrage, L. (2002) LINGO Release 8.0, LINDO System, Inc., Chicago

Add issue number

Kethley, R.B. and Waller, T.A. (2002) ‘Improving customer service in the real estate industry: a

property selection model using Taguchi loss’, Total Quality Management, Vol. 13, No. 6, pp.739–

748

Liao, C.N. and Kao, H.P. (2010) ‘Supplier selection model using Taguchi loss function, analytical

hierarchy process and multi-choice goal programming’, Computers & Industrial Engineering, Vol.

58, No. 1, pp.571–577

Reinecke, N., Spiller, P. and Ungerman, D. (2007) ‘The talent factor in purchasing’, The McKinsey

Quarterly, No. 1, pp.6–9

Reinecke, N., Spiller, P. and Ungerman, D. (2007) ‘The talent factor in purchasing’, The McKinsey

Quarterly, No. 1, pp.6–9

Weber, C.A., Current, J.R. and Benton, W.C. (1991) ‘Vendor selection criteria and methods’,

European Journal of Operational Research, Vol. 50, No. 1, pp.2–18

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