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Neural network application in predicting advanced manufacturing technology implementation performance

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Abstract

Advanced Manufacturing Technology (AMT) adoption can be complex, costly, and risky. Companies need to assess and evaluate their current conditions with that of AMT requirements to identify the gaps and predict their performance. Such an approach will facilitate companies not only in their investment decisions, but on the actions needed to improve performance. The lack of such an approach prompted this study to develop an Artificial Neural Network (ANN) classification and prediction model that can assist companies especially Small and Medium size Enterprises (SMEs) in evaluating AMT implementation. Data were collected from a survey of 140 SMEs. Using cluster analysis, the companies were classified into three groups based on their performance. Then, a feed-forward NN was developed and trained with back-propagation algorithm. The results showed that the model can classify companies with 72% accuracy rate into the three clusters. This model is suitable to evaluate AMTs implementation outcomes and predict company performance as high, low, or poor in technology adoption.

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References

  1. Dalton DR, Todor WD, Spendolini MJ, Fielding GJ, Porter LW (1980) Organization structure and performance: a critical review. Acad Manag Rev 5(1):49–64

    Google Scholar 

  2. Bessant J (1994) Towards total integrated manufacturing. Int J Prod Econ 34(3):237–251

    Article  Google Scholar 

  3. Boyer KK, Ward PT, Leong GK (1996) Approaches to the factory of the future an empirical taxonomy. J Oper Manag 14:297–313

    Article  Google Scholar 

  4. Dıaz MS, Machucaa JAD, MıJ Álvarez-Gil (2003) A view of developing patterns of investment in amt through empirical taxonomies: new evidence. J Oper Manag 21:577–606

    Article  Google Scholar 

  5. Jonsson P (2000) An empirical taxonomy of advanced manufacturing technology. Int J Oper Prod Manag 20(12):1446–1474

    Article  MathSciNet  Google Scholar 

  6. Chung W, Swink M (2009) Patterns of advanced manufacturing technology utilization and manufacturing capabilities. Prod Oper Manag 18(5):533–545

    Article  Google Scholar 

  7. Sambasivarao KV, Deshmukh SG (1995) Selection and implementation of advanced manufacturing technologies classification and literature review of issues. Int J Oper Prod Manag 10:43–62

    Article  Google Scholar 

  8. Pagell M, Handfield RD, Barber AE (2000) Effects of operational employee skills on advanced manufacturing technology performance. Prod Oper Manag 9(3):222–238

    Article  Google Scholar 

  9. Zhou H, Leong GK, Jonsson P, Sum C-C (2009) A comparative study of advanced manufacturing technology and manufacturing infrastructure investments in Singapore and Sweden. Int J Prod Econ 120(1):42–53

    Article  Google Scholar 

  10. Dasa A, Narasimhan R (2001) Process-technology fit and its implications for manufacturing performance. J Oper Manag 19:521–540

    Article  Google Scholar 

  11. McDermott CM, Stock GN (1999) Organizational culture and advanced manufacturing technology implementation. J Oper Manag 17:521–533

    Article  Google Scholar 

  12. Efstathiades A, Tassou S, Antoniou A (2002) Strategic planning, transfer and implementation of advanced manufacturing technologies (amt) development of an integrated process plan. Technovation 22(4):201–212

    Article  Google Scholar 

  13. Chung CA (1996) Human issues influencing the successful implementation of advanced manufacturing technology. J Eng Technol Manag 13:283–299

    Article  Google Scholar 

  14. Swamidass PM, Kotha S (1998) Explaining manufacturing technology use, firm size and performance using a multidimensional view of technology. J Oper Manag 17:23–37

    Article  Google Scholar 

  15. Boyer KK, Leong GK, Ward PT, Krajewski LJ (1997) Unlocking the potential of advanced manufacturing technologies. J Oper Manag 15:331–347

    Article  Google Scholar 

  16. Koc T, Bozdag E (2009) The impact of amt practices on firm performance in manufacturing SMES. Robot Comput Integr Manuf 25(2):303–313

    Article  Google Scholar 

  17. Heine ML, Grover V, Malhotra MK (2003) The relationship between technology and performance: a meta-analysis of technology models Omega 31(3):189–204

    Google Scholar 

  18. Burgess TF, Gules HK, Tekin M (1997) Supply chain collaboration and success in technology implementation. Integr Manuf Syst 8(5):323–332

    Article  Google Scholar 

  19. Kidd PT (1990) Organisation, people and technology: advanced manufacturing in the1990 s. Comput Aid Eng J 7(5):149–153

    Article  Google Scholar 

  20. Saberi S, Rosnah MY, Zulkifli N, Megat Ahmad MMH (2010) Effective factors on advanced manufacturing technology implementation performance: a review. J Appl Sci 10(13):1229–1242

    Article  Google Scholar 

  21. Zhao H, Co HC (1997) Adoption and implementation of advanced manufacturing technology in Singapore. Int J Prod Econ 48:7–19

    Article  Google Scholar 

  22. Efstathiades A, Tassou SA, Antoniou A, Oxinos G (1999) Strategic considerations in the introduction of advanced manufacturing technologies in the cypriot industry. Technovation 15:105–115

    Google Scholar 

  23. Melnyk SA, Narasimham R (1992) Computer integrated manufacturing: guidelines and applications from industrial leaders. Business One lrwin, Homewood

    Google Scholar 

  24. Percival J, Cozzarin B (2010) Selecting manufacturing business practices for maximum competitive advantage. J Manuf Technol Manag 21(1):100–121

    Article  Google Scholar 

  25. David L, Hitt MA, Goldhar JD (1996) Advanced manufacturing technology: organizational design and strategic flexibility. Organ Stud 17(3):501–523

    Article  Google Scholar 

  26. Ferdows K, De Meyer A (1990) Lasting improvements in manufacturing performance: in search of a new theory. J Oper Manag 9(2):168–184

    Article  Google Scholar 

  27. Zammuto RF, O’Connor EJ (1992) Gaining advanced manufacturing technologies’ benefits: the roles of organization design and culture. Acad Manag Rev 17(4):701–728

    Google Scholar 

  28. Belassi W, Fadlalla A (1998) An integrative framework for fms diffusion. Omega 26:699–713

    Article  Google Scholar 

  29. Bayo-Moriones A, JM-Dd Cerio (2004) Employee involvement: its interaction with advanced manufacturing technologies, quality management, and inter-firm collaboration. Hum Factors Ergonom Manuf 44(2):117–134

    Article  Google Scholar 

  30. Bidanda B, Cleland DI (1995) Human issues in technology implementation—part 1. Ind Manag 37(4):22–26

    Google Scholar 

  31. Siegel DS, Waldman DA, Youngdahl WE (1997) The adoption of advanced manufacturing technologies: human resource management implications. IEEE Trans Eng Manag 44(3)

  32. Small MH, Yasin MM (1997) Advanced manufacturing technology: implementation policy and performance. J Oper Manag 15:349–370

    Article  Google Scholar 

  33. Small MH (1993) Towards successful implementation of advanced manufacturing technology: a process-factors-process approach. Cleveland State University, Cleveland

    Google Scholar 

  34. Small MH, Chen IJ (1997) Economic and strategic justification of amt inferences from industrial practices. Int J Prod Econ 49:65–75

    Article  Google Scholar 

  35. Hanggi M, Moschytz GS (2000) Cellular neural networks: analysis, design and optimization. Kluwer, Dordrecht

    Google Scholar 

  36. Zhang G, Hu MY, Patuwo BE, Indro DC (1999) Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur J Oper Res 116:16–32

    Article  MATH  Google Scholar 

  37. Hu MY, Shanker M, Hung MS (1999) Estimation of posterior probabilities of consumer situational choices with neural network classifiers. Int J Res Mark 16:307–317

    Article  Google Scholar 

  38. Rojas R (1995) Neural networks: a systematic introduction. Springer, New York

    Google Scholar 

  39. Patterson DW (1996) Artificial neural networks. Prentice Hall, New York

    MATH  Google Scholar 

  40. Caudill M, Butler C (1994) Understanding neural networks: computer explorations: a workbook in two volumes with software for the macintosh and pc compatibles. MIT, Cambridge

    Google Scholar 

  41. Buscema PM (2000) Artificial neural networks and their application in drug addiction medicine (Artificial neural networks e loro applicazioni in medicina delle tossicodipendenze). Ann Ist Super Sanita 36(1):77–85

    Google Scholar 

  42. Ahmad Z, Noor RAM, Zhang J (2009) Multiple neural networks modeling techniques in process control: a review. Asia Pac J Chem Eng 4(4):403–419

    Article  Google Scholar 

  43. Wong BK, Bodnovich TA, Selvi Y (1997) Neural network applications in business: a review and analysis of the literature (1988–95). Decis Support Syst 19(4):301–320

    Article  Google Scholar 

  44. Limonadi FM, McCartney S, Burchiel KJ (2006) Design of an artificial neural network for diagnosis of facial pain syndromes. Stereotact Funct Neurosurg 84(5–6):212–220

    Article  Google Scholar 

  45. Gao M, Feng Q, Tian J Research and application of urban logistics demand forecast based on radial basic function neural network. In: Proceedings of the 1st international workshop on education technology and computer science, ETCS 2009, N36 2009. pp 853–856

  46. Wong BK, Selvi Y (1998) Neural network applications in finance: a review and analysis of literature (1990–1996). Inform Manag 34(3):129–139

    Article  Google Scholar 

  47. Yang J, Edwards DJ, Love PED (2004) Measuring the impact of daily workload upon plant operator production performance using artificial neural networks. Civil Eng Environ Syst 21(4):279–293

    Article  Google Scholar 

  48. Leea W-I, Shihb B-Y, Chung Y-S (2008) The exploration of consumers’ behavior in choosing hospital by the application of neural network. Expert Syst Appl 34(2):806–816

    Article  Google Scholar 

  49. Lehmann DR (1979) Market research and analysis. Irwin, Homewood

    Google Scholar 

  50. Miller JG, Roth AV (1994) A taxonomy of manufacturing strategies. Manag Sci 40(3):285–304

    Article  Google Scholar 

  51. Garson GD (1996) Statnotes: topics in multivariate analysis. http://faculty.chass.ncsu.edu/garson/pa765/statnote.htm

  52. Zineldin M, Jonsson P (2000) An examination of the main factors affecting trust/commitment in supplier-dealer relationships: an empirical study of the Swedish wood industry. TQM Mag 12(4):245–266

    Article  Google Scholar 

  53. Bohrmstedt GW (1983) Measurement. In: Rossi PH, Wright JD, Anderson AB (eds) Handbook of survey research. Academic Press Inc, New York

    Google Scholar 

  54. Fowler FJ (2009) Survey research methods, 4th edn. Sage, Los Angeles

    Google Scholar 

  55. SMIDEC (2007) SME handbook, policies, incentives, programmes and financial assistance for smes. Report, Kula Lumpur, Malaysia

  56. Bartlett JE, Kotrlik JW, Higgins CC (2001) Organizational research: determining appropriate sample size in survey research. Inform Technol Learn Perform J 19(1):43–50

    Google Scholar 

  57. Rosnah MY, Saberi S, Zulkifli N (2008) A comparison on the capabilities of Malaysian SMES with different equity structure in implementing advanced manufacturing technologies. AIJSTPME 1(1):63–75

    Google Scholar 

  58. Isa CR, Foong S-Y (2005) Adoption of advanced manufacturing technology (amt) and management accounting practices: the case of manufacturing firms in Malaysia. World Rev Sci Technol Sustain Dev 2(1):35

    Article  Google Scholar 

  59. Crowther PS, Cox RJ (2005) A method for optimal division of data sets for use in neural networks. In: Knowledge-based intelligent information and engineering systems, vol 3684. Springer, Berlin, pp 1–7

  60. Muthukumar S (2008) The application of advanced inventory techniques in urban inventory data development to earthquake risk modeling and mitigation in mid America. Georgia Institute of Technology

  61. Ben-David A (2006) What’s wrong with hit ratio? IEEE Intell Syst 21(6):68–70

    Article  Google Scholar 

  62. Nemati HR, Barko CD (2003) Organizational data mining: leveraging enterprise data resources for optimal performance IGI Global

  63. Hofmann C, Orr S (2005) Advanced manufacturing technology adoption—the german experience. Technovation 25:711–724

    Article  Google Scholar 

  64. Rosnah MY (2002) Implementation of advanced manufacturing technologies (amts) in the small and medium scale industries (SMIS) of Malaysia. University Putra Malaysia, Selangor

    Google Scholar 

  65. Swink M, Nair A (2007) Capturing the competitive advantages of amt: design–manufacturing integration as a complementary asset. J Oper Manag 25(3):736–754

    Article  Google Scholar 

  66. Shepherd DA, McDermott C, Stock GN (2000) Advanced manufacturing technology: does more radicalness mean more perceived benefits? J High Technol Manag Res 11(1):19–33

    Article  Google Scholar 

  67. Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. Wiley, New York

    Google Scholar 

  68. Fish KE, Barnes JH, AikenAssistant MW (1995) Artificial neural networks: a new methodology for industrial market segmentation. Ind Mark Manag 24(5):431–438

    Article  Google Scholar 

  69. Youn H (2008) Failure prediction for hospitality firms in US and Korea using logit and neural networks models. University of Nevada, Las Vegas

    Google Scholar 

  70. Tam KY, Kiang MY (1992) Managerial applications of neural networks: the case of bank failure predictions. Manag Sci 38(7):926–947

    Article  MATH  Google Scholar 

  71. Sexton RS, Sriram RS, Etheridge H (2003) Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach. Decis Sci 34(3):421–442

    Article  Google Scholar 

  72. Suresh N, Meredith JR (1985) Justifying multi machine systems: an integrated strategic approach. J Manuf Syst 4(2):117–134

    Article  Google Scholar 

  73. Mcdaniel JR (1989) Managing automation a study of the adoption, implementation, and evaluation of advanced manufacturing technology. University of Massachusetts, Boston

    Google Scholar 

  74. Small MH (2006) Justifying investment in advanced manufacturing technology: a portfolio analysis. Ind Manag Data Syst 106(4):485–508

    Article  Google Scholar 

  75. Boyer KK (1994) Patterns of advanced manufacturing technology implementation: technology and infrastructure. The Ohio State University, Columbus

    Google Scholar 

  76. Hayes RH, Pisano GP (1994) Beyond world-class: the new manufacturing strategy. Harv Bus Rev 72(10):77–86

    Google Scholar 

  77. Boyer KK, Lewis MW (2002) Competitive priorities: investigating the need for trade-offs in operations strategy. Prod Oper Manag 11(1):9–20

    Article  Google Scholar 

  78. Chen YY, Huang HL (2008) Examining the effect of strategic alignment on business performance: knowledge management, information technology, and human resource management strategies. In: Proceedings of the 4th IEEE international conference on management of innovation and technology, ICMIT, H8 2008, pp 987–992

  79. Waldeck NE (2007) Worker assessment and the provision of developmental activities with advanced technology: an empirical study. Int J Prod Econ 107(2):540–554

    Article  Google Scholar 

  80. Mitala A, Pennathur A (2004) Advanced technologies and humans in manufacturing workplaces: an interdependent relationship. Int J Ind Ergonom 33:295–313

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by a grant from the Ministry of Science, Technology, and Innovation (MOSTI) under the 9th Malaysian plan.

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia

    Sara Saberi & Rosnah Mohd. Yusuff

Authors
  1. Sara Saberi
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  2. Rosnah Mohd. Yusuff
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Corresponding author

Correspondence to Rosnah Mohd. Yusuff.

Appendix

Appendix

 

1. The level of technologies or practices utilized in company

Stand-alone Technologies

Computer-aided manufacturing

Computer-aided design

Computer-aided engineering

Computer-aided process planning

Computerized numerical control

Programmable logic control

Pick-and-place robots

Materials working lasers

Bar code

Intermediate technology

Automated storage/retrieval systems

Automated material handling systems

Automated inspection and testing

Integrated technology

Flexible manufacturing cells/systems

Computer-integrated manufacturing

Electronic data interchange

Manufacturing resources planning

Total quality management

Enterprise resource planning

2. The extent a company emphasize on these methods for justifying a new technology

Economic approach

Payback period (PP)

Return on investment (ROI)

Internal rate of return (IRR)

Net present value (NPV)

Cost-benefit analysis (CBA)

Strategic approach

Analysis of technical importance

Analysis of business objectives

Comparison with competitors

Analytic approach

Value analysis (VA)

Risk analysis (RA)

3. The importance of implementing the following strategies

Flexibility strategy

Develop and produce new products

Provide extensive customer service

Switch quickly between productions

Offer customized products

Cost strategy

Price basic products below competitors

Improve operating efficiency

Reduce operating costs

Delivery strategy

Provide fast, on-time deliveries

Reduce production lead time

Quality strategy

Improving product reliability

Maintain the plant’s reputation

4. To what extent these practices describe your company culture

Flexibility oriented

Company is personal, like an extended family

Company is dynamic and entrepreneurial

The head of company is considered to be a sage

The head of company is considered to be an entrepreneur

Company is toward loyalty and tradition

Company is toward commitment to innovation and development

Company emphasizes human resources

Company emphasizes growth and acquiring new resources

Control oriented

Company is formalized and structured

The head of company is generally considered to be a coordinator

Company is production oriented

The head of company is generally considered to be a producer

Company has formal rules and policies

Company emphasis on tasks and goal accomplishment

Company emphasizes permanence and stability

Company emphasizes competitive actions and achievement

5. Company emphasizes on these human resource practices

Having multi-skilled production workers

Emphasizing teamwork activities

Pre-installation training for all project participants

Training are given to employees during their work life

Employee participation in all stages of the implementation process

The presence of technological champion

Employment security as basic platform for change

Motivate employees with compensation programs (rewards, bonus,…)

Worker empowerment

Management efforts to effect culture change and to support and guide the development process

6. The extent to which data transactions between the following pairs of processes are accomplished

Between product design and process planning (i.e. CAD data directly linked to CAPP)

Between product design and manufacturing (i.e. CAD data directly controlling production equipment such as CNC machines, robots)

Between product design and production planning (i.e. parts data from CAD linked to MRP software)

Between process planning and manufacturing (i.e. CAPP schedules control equipment like CNS or FMS)

Between production planning and manufacturing (i.e. production schedules generated by MRP controlling production equipment)

Between product design and process planning (i.e. CAD data directly linked to CAPP)

7. Change of company’s performance after AMT implementation

Operator autonomy

Use of work teams

Worker’s skill level

Product quality

Product innovation

Process flexibility

Efficiency

Reliability

Lead time

Flow of work

Communication

Integration of business activity

Management control

Met organizational goals

Customer satisfaction

Sales growth

Market share

Return on Investment (ROI)

Return on sales (ROS)

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Saberi, S., Yusuff, R.M. Neural network application in predicting advanced manufacturing technology implementation performance. Neural Comput & Applic 21, 1191–1204 (2012). https://doi.org/10.1007/s00521-010-0507-0

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