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Optimization of supplier selection problem by combined customer trust and resilience engineering under uncertainty

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Abstract

Trust between suppliers and producers would increase productivity and quality. Incorporation of resilience engineering has also proven to enhance efficiency. This study presents an integrated customer trust and resilience engineering algorithm for optimum supplier selection in a real auto parts manufacturer. The proposed algorithm is composed of standard questionnaires, fuzzy mathematical programming, statistical methods, and verification and validation mechanism. Resilience engineering (flexibility, adaptability and redundancy) and customer trust (integrity, benevolence, ability and predictability) are considered as outputs and cost and delivery time are considered as inputs to select best suppliers. Fuzzy mathematics is used to achieve improved results due to existence of data subjectivity and uncertainty. Moreover, fuzzy data envelopment analysis (fuzzy DEA) is designed and applied for various alpha cuts. The results show integration of customer trust and resilience engineering will increase total efficiency. The result identifies the most important factors in supplier selection problem with respect to trust and resilience engineering. The best supplier can also be selected. Predictability and redundancy have the highest impact on total efficiency, respectively. This is the first study that simultaneously considers resilience engineering, customer trust, cost and delivery time to select optimum supplier. Second, it uses a robust algorithm to achieve such objective. Third, it is equipped with verification and validation mechanism. Fourth, it is a practical approach for decision makers.

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References

  • Agha Mohammad Ali Kermani M, Aliahmadi A, Salamat VR, Barzinpour F, Hadiyan E (2015) Supplier selection in a single-echelon supply chain with horizontal competition using Imperialist competitive algorithm. Int J Comput Integr Manuf 28(6):628–638

    Article  Google Scholar 

  • Aghai S, Mollaverdi N, Sabbagh MS (2014) A fuzzy multi-objective programming model for supplier selection with volume discount and risk criteria. Int J Adv Manuf Technol 71(5–8):1483–1492

    Article  Google Scholar 

  • Azadeh A, Alem SM (2010) A flexible deterministic, stochastic and fuzzy data envelopment analysis approach for supply chain risk and vendor selection problem: simulation analysis. Expert Syst Appl 37(12):7438–7448

    Article  Google Scholar 

  • Azadeh A, Atrchin N, Salehi V, Shojaei H (2014a) Modelling and improvement of supply chain with imprecise transportation delays and resilience factors. Int J Logist Res Appl 17(4):269–282

    Article  Google Scholar 

  • Azadeh A, Salehi V, Ashjari B, Saberi M (2014b) Performance evaluation of integrated resilience engineering factors by data envelopment analysis: the case of a petrochemical plant. Process Saf Environ Prot 92(3):231–241

    Article  Google Scholar 

  • Azadeh A, Rezaei-Malek M, Evazabadian F, Sheikhalishahi M (2015) Improved design of CMS by considering operators decision-making styles. Int J Prod Res 53(11):3276–3287

    Article  Google Scholar 

  • Carvalho H, Barroso AP, Machado VH, Azevedo S, Cruz-Machado V (2012) Supply chain redesign for resilience using simulation. Comput Ind Eng 62(1):329–341

    Article  Google Scholar 

  • Chai J, Liu JNK, Ngai EWT (2013) Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst Appl 40(10):3872–3885

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  MATH  MathSciNet  Google Scholar 

  • Chen MC, Yang T, Yen CT (2007) Investigating the value of information sharing in multi-echelon supply chains. Qual Quant 41(3):497–511

    Article  Google Scholar 

  • Christopher M, Peck H (2004) Building the resilient supply chain. Int J Logist Manag 15(2):1–13

    Article  Google Scholar 

  • Costella MF, Saurin TA, de Macedo Guimarães LB (2009) A method for assessing health and safety management systems from the resilience engineering perspective. Saf Sci 47(8):1056–1067

    Article  Google Scholar 

  • De Boer L, Labro E, Morlacchi P (2001) A review of methods supporting supplier selection. Eur J Purch Supply Manag 7(2):75–89

    Article  Google Scholar 

  • Deane JK, Craighead CW, Ragsdale CT (2009) Mitigating environmental and density risk in global sourcing. Int J Phys Distrib Logist Manag 39(10):861–883

    Article  Google Scholar 

  • Gefen D, Straub DW (2004) Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega 32(6):407–424

    Article  Google Scholar 

  • Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27(1):51–90

    Article  Google Scholar 

  • Haiyan YI, Huaizhen YE (2008) Build principle and frame design for resilience supply chain. Commer Times 13:17–19

    Google Scholar 

  • Jafari Songhori M, Tavana M, Azadeh A, Khakbaz MH (2011) A supplier selection and order allocation model with multiple transportation alternatives. Int J Adv Manuf Technol 52(1–4):365–376

    Article  Google Scholar 

  • Jafarian Moghaddam AR, Ghoseiri K (2011) Fuzzy dynamic multi-objective data envelopment analysis model. Expert Syst Appl 38(1):850–855

    Article  Google Scholar 

  • Juttner U, Maklan S (2011) Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Manag Int J 16(4):246–259

    Article  Google Scholar 

  • Kamalahmadi M, Mellat-Parast M (2016) Developing a resilient supply chain through supplier flexibility and reliability assessment. Int J Prod Res 54(1):302–321

    Article  Google Scholar 

  • Ku CY, Chang CT, Ho HP (2010) Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming. Qual Quant 44(4):623–640

    Article  Google Scholar 

  • Lu Y, Zhao L, Wang B (2010) From virtual community members to C2C e-commerce buyers: trust in virtual communities and its effect on consumers’ purchase intention. Electron Commer Res Appl 9(4):346–360

    Article  Google Scholar 

  • Mayer RC, Davis JH, Schoorman FD (1995) An integration model of organizational trust. Acad Manag Rev 20(3):709–734

    Google Scholar 

  • McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-Commerce: an integrative typology. Inf Syst Res 13(3):334–359

    Article  Google Scholar 

  • Nair A, Jayaram J, Das A (2015) Strategic purchasing participation, supplier selection, supplier evaluation and purchasing performance. Int J Prod Res 53(20):6263–6278

    Article  Google Scholar 

  • Rajesh R, Ravi V (2015) Supplier selection in resilient supply chains: a grey relational analysis approach. J Clean Prod 86(1):343–359

    Article  Google Scholar 

  • Tanrikulu Z, Celilbatur N (2013) Trust factors affecting e-Ticket purchasing. Proced Soc Behav Sci 73(27):115–119

    Article  Google Scholar 

  • Toloo M (2014) Selecting and full ranking suppliers with imprecise data: a new DEA method. Int J Adv Manuf Technol 74(5–8):1141–1148

    Article  Google Scholar 

  • Visani F, Barbieri P, Di Lascio FML, Raffoni A, Vigo D (2016) Supplier’s total cost of ownership evaluation: a data envelopment analysis approach. Omega 61:141–154

    Article  Google Scholar 

  • Vugrin ED, Warren DE, Ehlan MA (2011) A resilience assessment framework for infrastructure and economic systems: quantitative and qualitative resilience analysis of petrochemical supply chains to a hurricane. Am Inst Chem Eng Process Saf Prog 30(3):280–290

    Article  Google Scholar 

  • Wu DD, Zhang Y, Wu D, Olson DL (2010) Fuzzy multi-objective programming for supplier selection and risk modeling: a possibility approach. Eur J Oper Res 200(3):774–787

    Article  MATH  Google Scholar 

  • Yeung JHY, Selen W, Zhang M, Huo B (2009) The effects of trust and coercive power on supplier integration. Int J Prod Econ 120(1):66–78

    Article  Google Scholar 

  • Zhao Y, Cavusgil ST (2006) The effect of supplier’s market orientation on manufacturer’s trust. Ind Mark Manag 35(4):405–414

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful for the valuable comments and suggestions by the respected reviewers, which have enhanced the strength and significance of this work. This study was supported by a grant from the Iran National Science Foundation [grant number 95848814]. The authors are grateful for the financial support provided by the Iran National Science Foundation.

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Correspondence to A. Azadeh.

Appendices

Appendix 1: Design of questionnaire

1. Delivery time:

1.1. Is the supplier able to deliver the ordered products on time?

1.2. In times of crisis, is the supplier able to deliver the extra requested products on time?

2. Cost:

2.1. Does the price of the supplier’s products enjoy acceptable stability under the fluctuations in market?

2.2. Does the supplier have sufficient funding to compensate for the belated financial settlement on our part?

3. Ability:

3.1. Is the supplier technologically able to meet our needs in case of any changes in demands?

3.2. Is the supplier able to deliver more than the amount of ordered products, if necessary?

4. Integrity:

4.1. Has the supplier been loyal to the fulfillment of their obligations with respect to their history of cooperation with us?

4.2. Has the supplier been honest regarding the desired quality of the delivered products?

5. Benevolence:

5.1. Does the supplier work for the advantage of our organization regardless of profit-based purposes?

5.2. Are the supplier assign importance to the goals of our organization?

6. Predictability:

6.1. Is the performance of the supplier predictable and does it follow an acceptable performance?

6.2. Is it possible to predict that the past successes of the supplier guarantee their future success?

7. Flexibility:

7.1. Does the supplier have the flexibility to change the prices due to existent competition in the market?

7.2. Is the supplier able to quickly guarantee their final product under the fluctuations of their raw material quality?

8. Redundancy:

8.1. Is the supplier able to improve and increase their production capacity?

8.2. Has the supplier the possibility of product delivery through the warehouse, if an unexpected event occurs?

9. Adaptability:

9.1. Does the supplier have the required compatibility with the changes in customers’ tastes?

9.2. In the event of any change in the market, does the supplier have the necessary compatibility with these changes regarding the delivery of their after-sales service?

Appendix 2: Raw data

See Table 9.

Table 9 The preliminary data of the suppliers (SP)

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Azadeh, A., Siadatian, R., Rezaei-Malek, M. et al. Optimization of supplier selection problem by combined customer trust and resilience engineering under uncertainty. Int J Syst Assur Eng Manag 8 (Suppl 2), 1553–1566 (2017). https://doi.org/10.1007/s13198-017-0628-2

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  • DOI: https://doi.org/10.1007/s13198-017-0628-2

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