Abstract
The purpose of this study is to develop the decision model to help decision makers with their technology selection. Evaluation and selection of a new technology is a multi-criteria decision-making process encompassing various tangible and intangible factors. Thus, these factors should be identified for inclusion in the evaluation process. A thorough analysis of the impact, both positive and negative, of such factors on the organization is also required in the evaluation technique. Therefore, the first step in the development of a decision model for evaluation of technology alternatives is the identification of the pertinent factors. To accomplish this, both risks and benefits of implementing a new technology are identified for inclusion in the evaluation process. Once pertinent risks and benefits are identified, a mechanism for analysis of these factors is developed. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss function is used to measure performance of each technology candidate with respect to the risk and benefit categories. Appropriate Taguchi loss functions are formulated based on the target value and the specification limits set by the decision maker. These loss functions are then used to calculate Taguchi loss scores for each technology alternative. Analytic hierarchy process (AHP) is used to determine the relative importance of the risks and benefits to the decision maker. The weighted loss scores are calculated for each technology alternative by using the relative importance as the weights. The composite weighted loss scores are then calculated and used for ranking of the technology alternatives. The technology with the smallest composite loss score is recommended for adoption. The proposed model provides guidelines for managers to make an informed decision regarding technology selection. In addition, combining Taguchi loss function and AHP provides a novel approach for ranking of the potential technology alternatives for implementation purposes.
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References
Georgakellos DA (2005) Technology selection from alternatives: a scoring model for screening candidates in equipment purchasing. Int J Innov Technol Manag 2(1):1–18
Small M (2007) Planning, justifying, and installing advanced manufacturing technology: a managerial framework. J Manuf Technol Manag 18(5):513–537
Ordoobadi S, Xue Y, Shanteau J (2005) Similarity-based reasoning using proverbs in managing technological innovations for small manufacturers. Int J Innov Technol Manag 2(4):433–449
Chan FTS, Chan MH, Lau H, Lp RWL (2001) Investment techniques for advanced manufacturing technology: a literature review. Integr Manuf Syst 12(1):35–47
Chan FTS, Chan MH, Tang NKH (2000) Evaluation methodologies for technology selection. J Mater Process Technol 107:330–337
Newman DG, Lavelle JP, Eschenbach TG (2000) Engineering economic analysis. Engineering, Austin
Sullivan WG, Bontadelli JA, Wicks EM (2000) Engineering economy. Prentice-Hall, Upper Saddle River
Canada JR, Sullivan WG (1990) Persistent pitfalls and applicable approaches for justification of advanced manufacturing technologies. Eng Costs Prod Econ 18:247–253
Ordoobadi S, Mulvaney N (2001) Development of a Justification tool for advanced manufacturing technologies: system-wide benefit analysis. J Eng Technol Manag 18:157–184
Thomas AJ, Barton R, John EG (2008) Advanced manufacturing technology implementation: a review of benefits and a model for change. Int J Product Perform Manag 57(2):156–176
Raafat F (2002) A comprehensive bibliography on justification of advanced manufacturing systems. Int J Prod Econ 79:197–208
Albayrakoglu M (1996) Justification of new manufacturing technology: a strategic approach using the analytic hierarchy process. Prod Invent Manag J 37(1):71–76
Dhavale D (1995) Justifying manufacturing cells. Manuf Eng 115(6):31–37
Arslan MD, Catay B, Budak E (2004) A decision support system for machine tool selection. J Manuf Technol Manag 15(1):101–109
Kulak O, Kahraman C, Oztaysi B, Tanyas M (2005) Multi-attribute information technology project selection using fuzzy axiomatic design. J Enterp Inf Manag 18(3):275–288
Thomaidis NS, Nikitakos N, Dounias G (2006) The evaluation of information technology projects: a fuzzy multicriteria decision-making approach. Int J Inf Technol Decis Mak 15(1):89–122
Lefley F (2004) An assessment of various approaches for evaluating project strategic benefits: recommending the strategic index. Manag Decis 42(7):850–862
O’Brien C, Smith SJE (1993) Design of the decision process for strategic investment in advanced manufacturing systems. Int J Prod Econ 30(31):309–322
Sullivan WG (1986) Models IE’s can use to include strategic, non-monetary factors in automation decisions. Ind Eng 18(3):42–50
Kahraman C, Cebi S, Kaya I (2010) Selection among renewable energy alternatives using fuzzy axiomatic design: the case of Turkey. J Univ Comput Sci 16(1):82–102
Ordoobadi S (2012) Application of ANP methodology in evaluation of advanced manufacturing technologies. J Manuf Technol Manag 23(2):229–252
Ordoobadi S (2009) Evaluation of advanced manufacturing technologies using Taguchi loss functions. J Manuf Technol Manag 20(3):367–384
Liang T-P, Huang C-W, Yeh Y-H, Lin B (2007) Adoption of mobile technology in business: a fit-viability model. Ind Manag Data Sys 107(8):1154–1169
Christenson CM (1997) The innovator’s dilemma when new technologies cause great firm to fail. Harvard Business School, Boston
Jones P, Clarke-Hill C, Hillier D, Comfort D (2005) The benefits, challenges and impacts of RFID technology for retailers in the UK. Mark Intell Plan 23(4):395–402
Woodside AG (1996) Theory of rejecting superior, new technologies. J Bus Ind Mark 11(3):25–43
Dey PK, Kinch J, Ogunlana SO (2007) Managing risk in software development projects: a case study. Ind Manag Data Syst 107(2):284–303
Maguire S (2002) Identifying risks during information system development: managing the process. Inf Manag Comput Secur 10(3):126–134
Vehovar V, Lesjak D (2007) Characteristics and impacts of ICT investments: perceptions among managers. Ind Manag Data Syst 107(4):537–550
Au AK, Enderwick P (2000) A cognitive model on attitude towards technology adoption. J Manag Psychol 15(4):266–282
Ryan SD, Harrison DA (2000) Considering social subsystem costs and benefits in information technology investment decisions: a view from the field on anticipated payoffs. J Manag Inf Syst 16(4):11–40
Ordoobadi S (2007) Opportunity costs of risks in evaluation of advanced technologies. Int J Innov Technol Manag 4(3):305–321
Ordoobadi S (2011) Inclusion of intangible risks in evaluation of technology alternatives. Int J Adv Manuf Technol 54(1–4):413–420
Cannon A, Reyes PM, Frazier GV, Prater E (2008) RFID in the contemporary supply chain: multiple perspectives on its benefits and risks. Int J Oper Prod Manag 28(5):433–454
Small MH, Chen IJ (1997) Economic and strategic justification of AMT inferences from industrial practices. Int J Prod Econ 49:65–75
Noble JL (1990) A new approach for justifying computer-integrated manufacturing. Cost Manag 3(4):14–19
Son YK, Park CS (1987) Economic measure of productivity, quality, and flexibility in advanced manufacturing systems. J Manuf Syst 6(3):193–207
Parsaei HR, Karwowski W, Wilhelm MR, Walsh AJ (1988) A methodology for economic justification of flexible manufacturing systems. Comput Ind Eng 15(1–4):117–122
Downing T (1989) Eight new ways to evaluate automation. Mech Eng 111(7):82–86
Handfield R, Pagell M (1995) An analysis of the diffusion of flexible manufacturing systems. Int J Prod Econ 39:243–253
Kakati M, Dhar UR (1991) Investment justification in flexible manufacturing systems. Eng Costs Prod Econ 21(3):203–209
Noaker PM (1994) Can you justify change? Manuf Eng 113(6):30–35
Primrose PL and Leonard R (1987) Performing investment appraisals for advanced manufacturing technology. Cost Manag 34–42
Proctor MD, Canada JR (1992) Past and present methods of manufacturing investment evaluation: a review. Eng Econ 38(1):45–58
Swann K, O’Keefe WD (1990) Advanced manufacturing technology: investment decision process. Part 2. Manag Decis 28(3):27–34
Machuca JAD, Gil MJA, Gonza’lez SG, Machuca MAD, Jime’nez AR (1995) Operations management: strategic issues in the manufacturing and services production processes. McGraw-Hill, Madrid
Burcher P, Lee G (1997) The challenge of investing in advanced manufacturing technologies: a study of British manufacturers. Research report. Aston Business School, Birmingham
Lin G, Nagalingam S (2000) CIM justification and optimization. Taylor & Francis, London
Hofmann C, Orr S (2005) Advanced manufacturing technology adoption: the German experience. Technovation 25(5):711–724
Raymond L (2005) Operations management and advanced manufacturing technologies in SMEs: a contingency approach. J Manuf Technol Manag 16(8):936–955
Monge CAM, Rao SS, Gonzalez ME, Sohal AS (2006) Performance measurement of AMT: a cross-regional study. Benchmark Int J 13(1/2):135–146
Love PED, Ghoneim A, Irani Z (2004) Information technology evaluation: classifying indirect costs using the structured case method. J Enterp Inf 17(4):312–325
Steward R, Mohamed S (2002) IT/IS projects selection using multi-criteria utility theory. Logist Inf Manag 15(4):254–270
Barreau D (2001) The hidden costs of implementing and maintaining information systems. The bottom line: managing library finances. MCB Univ Press 14(4):207–212
Irani Z, Love PED (2001) The propagation of technology management taxonomies for evaluating investments in information systems. J Manag Inf Syst 17(3):161–177
Lewis WG, Pun KF, Lalla TRM (2006) Exploring soft and hard factors for TQM implementation in small and medium-sized enterprises. Int J Product Perform Meas 55(7):539–544
Saaty TL (1995) Decision making for leaders: the analytic hierarchy process for decisions in complex world. RWS, Pittsburgh
Bayazit O (2006) Use of analytic network process in vendor selection decisions. Benchmark Int J 13(5):566–579
Kirytopoulos K, Leopoulos V, Voulgaridou D (2008) Supplier selection in pharmaceutical industry: an analytic network process approach. Benchmark Int J 15(4):494–516
Taguchi G, Hsiang TC (1989) Quality engineering in production systems. McGraw-Hill, New York
Besterfield DH, Besterfield-Michna C, Besterfield GH, Besterfield-Sacre M (2003) Total quality management. Pearson Education, Upper Saddle River
Ealey LA (1994) Quality by design: Taguchi methods and U.S. industry. ASI, Dearborn
Liao C-N (2010) Supplier selection project using an integrated Delphi, AHP and Taguchi loss function. ProbStat Forum 03:118–134
Magdalena R (2012) Supplier selection for food industry: a combination of Taguchi loss function and fuzzy analytical hierarchy process. In: Proceedings of the 3rd International Conference on Technology and Operations Management
Nukala S and Gupta S (2007) A fuzzy mathematical programming approach for supplier selection in a closed-loop supply chain network. In: Proceedings of the POMS-Dallas Meeting
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Ordoobadi, S.M. Application of AHP and Taguchi loss functions in evaluation of advanced manufacturing technologies. Int J Adv Manuf Technol 67, 2593–2605 (2013). https://doi.org/10.1007/s00170-012-4676-0
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DOI: https://doi.org/10.1007/s00170-012-4676-0