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Fuzzy Multi Criteria Decision Making Approach for Performance Measurement of Advanced Manufacturing Systems


Affiliations
1 Department of Technical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran, Islamic Republic of
2 Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran, Islamic Republic of
3 Roudbar Branch, Islamic Azad University, Roudbar, Iran, Islamic Republic of
4 Zahedshahr Branch, Islamic Azad University, Zahedshahr, Iran, Islamic Republic of
 

In today's competitive environment, a right performance measurement system of manufacturing firms plays a critical role in achieving competitive advantages. To overcome disadvantages of traditional performance measurement and to achieve competitive advantages goals, this paper attempts to present a new approach based on fuzzy analytic hierarchy process for performance measurement of advanced manufacturing systems under activity based costing (ABC) system. The proposed decision method aggregates the experts' judgments for the ABC criteria weights, and the measuring performance of companies which applied advanced manufacturing systems. The proposed approach is applied to measure performance of advanced manufacturing systems as an experiment and results are provided. Also, the proposed approach can effectively handle complex, ambiguity and fuzzy environment involved in measuring performance of advanced manufacturing systems.

Keywords

Fuzzy Set, Multi Criteria Decision Making, Performance Measurement, Manufacturing System
User

  • Abdel-Kader MG and Dugdale D (2001) Evaluating investments in advanced manufacturing technology: A fuzzy set theory approach. The Br. Accounting Rev. 33(4), 455–489.
  • Askarany D, Yazdifar H and Askary S (2010) Supply chain management, activity-based costing and organisational factors. Int.J. Production Econ. 127(2), 238-248.
  • Azadeh A, Nazari-Shirkouhi S, Hatami-Shirkouhi L and Ansarinejad A (2011) A unique fuzzy multi-criteria decision making: computer simulation approach for productive operators’ assignment in cellular manufacturing systems with uncertainty and vagueness. The Intl. J. Adv. Manuf. Technol. 56(1-4), 329-343.
  • Azadeh A, Shirkouhi SN and Rezaie K (2010) A robust decision-making methodology for evaluation and selection of simulation software package. The Intl. J. Adv. Manuf. Technol. 47(1), 381–393.
  • Banker RD, Bardhan IR and Chen TY (2008) The role of manufacturing practices in mediating the impact of activitybased costing on plant performance. Accounting, Organizations & Soc. 33(1), 1–19.
  • Bayazit O (2005) Use of AHP in decision-making for flexible manufacturing systems. J. Manuf. Technol. Management. 16(7), 808–819.
  • Beaumont N, Schroder R and Sohal A (2002) Do foreignowned firms manage advanced manufacturing technology better? Intl. J. Operations & Production Management. 22(7), 759–771.
  • Berrah L, Mauris G and Montmain J (2008) Monitoring the improvement of an overall industrial performance based on a Choquet integral aggregation. Omega. 36(3), 340–351.
  • Berrah L, Mauris G and Vernadat F (2004) Information aggregation in industrial performance measurement: rationales, issues and definitions. Intl. J. Production Res. 42(20), 4271–4293.
  • Beskese A, Kahraman C and Irani Z (2004) Quantification of flexibility in advanced manufacturing systems using fuzzy concept. Intl. J. Production Econ. 89(1), 45–56.
  • Boucher TO, Gogus O and Wicks EM (1997) A comparison between two multiattribute decision methodologies used in capital investment decision analysis. The Engg. Economist, 42(3), 179–202.
  • Boyle TA (2006) Towards best management practices for implementing manufacturing flexibility. J. Manuf. Technol. Management. 17(1), 6–21.
  • Brown S (2000), Manufacturing the Future: strategic resonance for enlightened manufacturing. Prentice-Hall, Harlow.
  • Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets & Systems. 17(3), 233–247.
  • Buyukozkan G, Çifçi G and Guleryuz S (2011) Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Systems with Appl. 38 (8), 9407- 9424.
  • Chan FTS, Chan HK, Chan MH and Humphreys PK (2006) An integrated fuzzy approach for the selection of manufacturing technologies. The Intl. J. Adv. Manuf. Technol. 27(7-8), 747-758.
  • Chuang M, Yang YS and Lin CT (2009) Production technology selection: Deploying market requirements, competitive and operational strategies, and manufacturing attributes. Intl. J. Computer Integrated Manuf. 22(4), 345– 355.
  • Chuu SJ (2009) Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Computers & Industrial Engg. 57(3), 1033–1042.
  • Cooper R and Kaplan RS (1988) Measure costs right: Make the right decision. Harvard Bus. Rev. 66, 96–103.
  • Dangayach GS and Deshmukh SG (2005) Advanced manufacturing technology implementation: evidence from Indian small and medium enterprises (SMEs). J. Manuf.Technol. Management, 16(5), 483–496.
  • Duran O and Aguilo J (2008) Computer-aided manufacturing-tool selection based on a Fuzzy-AHP approach. Expert Systems with Appl. 34, 1787–94.
  • Glad E, Becker H, Partridge M and Perren L (1996) Activity-based costing and management. Wiley. NY.
  • Iranmanesh H, Shirkouhi SN and Skandari MR (2008) Risk evaluation of information technology projects based on fuzzy analytic hierarchal process. Intl. J. Computer & Info. Sci. & Engg. 2(1), 38-44.
  • Juran JM and Gyrna FM (1980) Quality planning and analysis. McGraw-Hill, NY.
  • Kahraman C, Tolga E and Ulukan Z (2000) Justification of manufacturing technologies using fuzzy benefit/cost ratio analysis. Intl. J. Production Econ. 66(1), 45–52.
  • Kaplan RS (1991) New systems for measurement and control. The Engg. Economist. 36(3), 201–218.
  • Karsak EE and Tolga E (2001) Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. Int. J. Production Econ. 69(49), 64.
  • Kaufmann A and Gupta MM (1988) Fuzzy mathematical models in engineering and management science. Publisher? NY.
  • Kim G, Park CS and Yoon KP (1997) Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. Intl. J. Production Econ. 50(1), 23-33.
  • Kolli S, Wilhelm MR, Parsaei HR and Liles DH (1992) A classification scheme for traditional and non-traditional approaches to the economic justification of advanced automated manufacturing systems. Manuf. Res. & Technol. 14, 165–187.
  • Liou TS and Wang MJ (1992) Ranking fuzzy numbers with integral value. Fuzzy Sets & Systems. 50(3), 247–255.
  • Park CS and Kim GT (1995) An economic evaluation model for advanced manufacturing systems using activitybased costing. J. Manuf. Systems, 14(6), 439–451.
  • Perego A and Rangone A (1998) A reference framework for the application of MADM fuzzy techniques to selecting AMTS. Intl. J. Product. Res. 36(2), 437–458.
  • Raafat F (2002) A comprehensive bibliography on justification of advanced manufacturing systems. Intl. J. Product. Econ. 79(3), 197–208.
  • Rehman AU and Subash Babu A (2009) Evaluation of reconfigured manufacturing systems: an AHP framework. Intl. J. Productivity & Quality Management. 4(2), 228–246.
  • Rezaie K, Byat M and Shirkouhi SN (2009b) Evaluating effective factors of implementing knowledge management based on FAHP method. In: Modelling & Simulation, 2009. AMS'09. Third Asia Intl. Conf. on. pp: 398–403.
  • Rezaie K, Nazari-Shirkouhi and Alem SM (2009a) Evaluating and selecting flexible manufacturing systems by integrating data envelopment analysis and analytical hierarchy process model. 2009 Third Asia Intl. Conf. on Modelling & Simulation. pp: 460–464.
  • Rezaie K, Nazari-Shirkouhi, Alem SM, Hatami-Shirkouhi L (2010) Using data envelopment analysis and analytical hierarchy process model to evaluate flexible manufacturing systems. Aus. J. Basic & Appl. Sci. 4(12), 6461–6469.
  • Romero F (2010) The social dimension of the integration of manufacturing systems: the role of institutions. Intl. J. Computer Integrated Manuf. 23(8), 806–818.
  • Saaty TL (1980) The analytic hierarchy process. McGraw- Hill, NY.
  • Santos SP, Belton V and Howick S (2002) Adding value to performance measurement by using system dynamics and multi-criteria analysis. Intl. J. Operations & Production Management. 22(11), 1246-72.
  • Stam A and Kuula M (1991) Selecting a flexible manufacturing system using multiple criteria analysis. Intl. J. Production Res. 29(4), 803–820.
  • Sullivan WG, Wicks EM, Luxhoj JT and Woods BM (2003) Engg. Economy. Prentice Hall.
  • Swink M and Nair A (2007) Capturing the competitive advantages of AMT: Design-manufacturing integration as a complementary asset. J. Operations Management. 25(3), 736–754.
  • Udo GJ and Ehie IC (1996) Critical success factors for advanced manufacturing systems. Computers & Industrial Engg. 31(1-2), 91-94.
  • Wabalickis RN (1988) Justification of FMS with the analytic hierarchy process. J. Manuf. Systems. 7(3), 175-182.
  • Wang X, Chan HK, Yee RWY and Diaz-Rainey I (2011) A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. Intl. J. Production Econ. In press.
  • Yang CL, Chuang SP and Huang RH (2009) Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Systems with Appl. 36(8), 11369–11377.

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  • Fuzzy Multi Criteria Decision Making Approach for Performance Measurement of Advanced Manufacturing Systems

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Authors

A. Rezazadeh
Department of Technical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran, Islamic Republic of
A. Mohammadzadeh
Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran, Islamic Republic of
M. Ghadamyari
Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran, Islamic Republic of
S. Nazari-Shirkouhi
Roudbar Branch, Islamic Azad University, Roudbar, Iran, Islamic Republic of
M. R. Dalvand
Zahedshahr Branch, Islamic Azad University, Zahedshahr, Iran, Islamic Republic of

Abstract


In today's competitive environment, a right performance measurement system of manufacturing firms plays a critical role in achieving competitive advantages. To overcome disadvantages of traditional performance measurement and to achieve competitive advantages goals, this paper attempts to present a new approach based on fuzzy analytic hierarchy process for performance measurement of advanced manufacturing systems under activity based costing (ABC) system. The proposed decision method aggregates the experts' judgments for the ABC criteria weights, and the measuring performance of companies which applied advanced manufacturing systems. The proposed approach is applied to measure performance of advanced manufacturing systems as an experiment and results are provided. Also, the proposed approach can effectively handle complex, ambiguity and fuzzy environment involved in measuring performance of advanced manufacturing systems.

Keywords


Fuzzy Set, Multi Criteria Decision Making, Performance Measurement, Manufacturing System

References





DOI: https://doi.org/10.17485/ijst%2F2011%2Fv4i10%2F30175