Advertisement

Decision Making for Determining the Implementation Level of RFID Projects

  • Emre Cevikcan
  • Alp Ustundag
Chapter

Abstract

Each physical item is given an identity and tracked in the supply chain in an automated and timely manner via RFID technology. Tracking physical assets, inventory, and personnel with automated systems improve performance of the system in terms of cost and process flow. It is an important decision for a company to invest RFID technology. Consecutively, it should be decided at which level RFID technology will be implemented in business processes. The aim of this chapter is to select the most appropriate RFID implementation level. The related decision is made among the alternatives of item, box, and pallet levels. Three main criteria are determined to evaluate the alternatives, namely cost, benefit, and implementation aspect. The related criteria are situated in hierarchical structure. Meanwhile, the alternatives are assessed for each criterion with verbal rating categories which have equivalent numerical values. Therefore, Analytic Hierarchical Process (AHP) rating model is developed for determining the implementation level of RFID Projects. The results indicate that the model is practical and validated for real life decision-making problems. The results indicate that item level outperforms other alternatives with respect to the judgements of an information technology expert.

Keywords

Pairwise Comparison Matrix Implementation Level Analytic Hierarchy Process Infrastructure Cost MCDM Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Chianga CM, Laib CM (2002) A study on the comprehensive indicator of indoor environment assessment for occupants’ health in Taiwan. Build Environ 37(4):387–392CrossRefGoogle Scholar
  2. Chin KS, Pun KS, Xu Y, Chan JSF (2002) An AHP based study of critical factors for TQM implementation in Shanghai manufacturing industries. Technovation 22(11):707–715Google Scholar
  3. Gaukler GM, Seifert RW, Hausman WH (2007) Item-level RFID in the retail supply chain. Prod Oper Manag 16(1):65–76CrossRefGoogle Scholar
  4. Hafeez K, Zhang Y, Malak N (2002) Determining key capabilities of a firm using analytic hierarchy process. Int J Prod Econ 76(1):39–51Google Scholar
  5. Ho W, Higson HE, Dey PK, Xu X, Bahsoon R (2009) Measuring performance of virtual learning environment system in higher education. Qual Ass Edu 17(1):6–29CrossRefGoogle Scholar
  6. Kahraman C, Demirel NC, Demirel T (2007) Prioritization of e-Government strategies using a SWOTAHP analysis: the case of Turkey. Eur J Inform Syst 16(3):284–298CrossRefGoogle Scholar
  7. Korpela J, Lehmusvaara A (1999) A customer oriented approach to warehouse network evaluation and design. Int J Prod Econ 59:135–146CrossRefGoogle Scholar
  8. Lin MC, Wang CC, Chen MS, Chang CA (2008) Using AHP and TOPSIS approaches in customer-driven product design process. Comput Ind 59(1):17–31CrossRefGoogle Scholar
  9. Ossadnik W, Lange O (1999) AHP-based evaluation of AHP-Software. Eur J Oper Res 118(3):578–588MATHCrossRefGoogle Scholar
  10. Ramanathan R (2001) A note on the use of the analytic hierarchy process for environmental impact assessment. J Environ Manag 63(1):27–35CrossRefGoogle Scholar
  11. Saaty T (1999) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, PittsburghGoogle Scholar
  12. Suwignjo P, Bititci US, Carrie AS (2000) Quantitative models for performance measurement system. Int J Prod Econ 64(3):231–241CrossRefGoogle Scholar
  13. Triantaphyllou E, Shu B, Nieto Sanchez S, Ray T (1998) Multi-criteria decision making: an operations research approach, encyclopedia of electrical and electronics engineering, vol. 15. Wiley, New York, pp 175–186Google Scholar
  14. Troutt MD, Tadisina SK (1992) The analytic hierarchy process as a model base for a merit salary recommendation system. Math Comput Mod 16(5):23–38CrossRefGoogle Scholar
  15. Unal C, Mucella GG (2009) Selection of ERP suppliers using AHP tools in the clothing industry. Int Jof Cloth Sci Tech 21(4):239–251CrossRefGoogle Scholar
  16. Ustundag A, Cevikcan E, Cebi S (2007) Evaluating AUTO-ID systems using fuzzy analytic hierarchical process. Proceedings of 10th joint conference on information sciences. Marriott Salt Lake City Center, Salt Lake City, Utah, 18–24 July 2007, pp. 1023–1028Google Scholar
  17. Vaidya OS, Kumar S (2006) Analytic hierarchy process: An overview of applications. Eur J Oper Res 169(1):1–29MathSciNetMATHCrossRefGoogle Scholar
  18. Vidal LA, Sahin E, Martelli N, Berhoune M, Bonan B (2010) Applying AHP to select drugs to be produced by anticipation in a chemotherapy compounding unit. Expert Syst Appl 37(2):1528–1534CrossRefGoogle Scholar
  19. Weiwu W, Jun K (1994) Highway transportation comprehensive evaluation. Comput Ind Eng 27(1–4):257–259CrossRefGoogle Scholar
  20. Yedla S, Suresh RM (2003) Multi-criteria approach for the selection of alternative options for environmentally sustainable transport system in Delhi. Transport Res A-Pol 37(8):717–729Google Scholar
  21. Yurdakula M, Tansel Y (2003) AHP approach in the credit evaluation of the manufacturing firms in Turkey. Int J Prod Econ 88(3):269–289CrossRefGoogle Scholar
  22. Zhang L (2010) Comparison of classical analytic hierarchy process (AHP) approach and fuzzy AHP approach in multiple-criteria decision making for commercial vehicle information systems and networks (CVISN) project. Msc Dissertation, University of Nebraska-Lincoln, LincolnGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Industrial EngineeringFaculty of Management, Istanbul Technical UniversityMackaTurkey

Personalised recommendations