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Prognosis agent technology: influence on manufacturing organizations

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Abstract

The objective of this paper is to examine the effect of prognosis agent technology (PAT) on manufacturing organization’s productivity and control. Extant literature advocates that various agents have been developed in the paradigms of designing, diagnosis, production, marketing, etc., but research on the effect of PAT on organizational output has not been explored yet. This paper uses the construct of PAT and checks its effect on performance of organizations. The research utilizes data collected from various manufacturing organizations. Based on literature survey, a set of hypotheses were proposed. Then, exploratory and confirmatory factor analyses were performed on collected data. Thereafter, a hypothetical model on PAT comprising five factors (manufacturing processes, fault identification, integration of manufacturing system with maintenance, manufacturing organization’s productivity, and control) is developed using structural equation modeling. All the hypotheses formed were supported, which indicates that the use of PAT in manufacturing organizations does increase organizational productivity and manufacturing control.

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References

  1. Sharma SK (2013) Maintenance reengineering framework: a case study. J Qual Maint Eng 19(2):96–113

    Article  Google Scholar 

  2. Chan FTS, Swarnkar R, Tiwari MK (2007) Infrastructure for co-ordination of multi-agents in a network-based manufacturing system. Int J Adv Manuf Technol 31:1028–1033. doi:10.1007/s00170-005-0115-9

    Article  Google Scholar 

  3. Mahesh M, Ong SK, Nee AYC (2007) A web-based multi-agent system for distributed digital manufacturing. Int J Comp Integ Manuf 20(1):11–27

    Article  Google Scholar 

  4. Schrader PG, Lawless KA (2004) The knowledge, attitude, & behavior approach: how to evaluate performance and learning in complex environments. Perform Improv 43(9):8–15

    Article  Google Scholar 

  5. Jiao J, Zhang W, Zhao Z, Cha J (1999) An integrated intelligent approach to process diagnosis in process industries. Int J Prod Res 37(11):2565–2583

    Article  MATH  Google Scholar 

  6. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J of Inter Serv and Applicat 1:7–18

    Article  Google Scholar 

  7. Yu R, Iung B, Panetto H (2003) A multi-agents based e-maintenance system with case-based reasoning decision support. Eng Appl Artif Intell 16:321–333

    Article  Google Scholar 

  8. Kothamasu R, Huang SH, VerDuin WH (2009) System health monitoring and prognostics—a review of current paradigms and practices. In handbook of maintenance management and engineering. Springer, London

    Google Scholar 

  9. Zhenyou Z, Yi W, Kesheng W (2013) Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis and artificial neural networks. The Int J Adv Manuf Technol l 68:763–773

    Article  Google Scholar 

  10. Sikorska JZ, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mechan Syst and Sign Proces 25:1803–1836

    Article  Google Scholar 

  11. Linxia L, Jay L (2010) Design of a reconfigurable prognostics platform for machine tools. Expert Syst Appl 37:240–252

    Article  Google Scholar 

  12. Evandro LST, Benny T et al (2012) A novel framework to link prognostics and health management and product–service systems using online simulation. Comput Ind 63:669–679

    Article  Google Scholar 

  13. Mirabedini SN, Hossein I (2014) A scheduling model for serial jobs on parallel machines with different preventive maintenance. Int J Adv Manuf Technol 70:1579–1589

    Article  Google Scholar 

  14. Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17(5):383–391

    Article  Google Scholar 

  15. Mourtzis D, Papakostas N, Makris S, Xanthakis V, Chryssolouris G (2008) Supply chain modeling and control for producing highly customized products. CIRP Ann – Manuf Tech 57(1):451–454

    Article  Google Scholar 

  16. Changqing L, Yingguang L, Weiming S (2014) Integrated manufacturing process planning and control based on intelligent agents and multi-dimensional features. Int J Adv Manuf Technol 75:1457–1471. doi:10.1007/s00170-014-6246-0

    Article  Google Scholar 

  17. Duffie NA, Prabhu VV (1994) Real-time distributed scheduling of heterarchical manufacturing systems. J Manuf Syst 13(2):94–107

    Article  Google Scholar 

  18. Ming PT, James TL (2005) Web-based distributed manufacturing control system. Int J Adv Manuf Technol 25:608–618. doi:10.1007/s00170-003-1805-9

    Article  Google Scholar 

  19. Lin GY-J, Solberg JJ (1992) Integrated shop floor control using autonomous agents. IIE Trans 24(3):57–71

    Article  Google Scholar 

  20. Mcfarlane DC, Bussmann S (2000) Developments in holonic production planning and control. Prod Plan Cont: Manag Operat 11(6):522–536

    Article  Google Scholar 

  21. Bechte W (1988) Theory and practice of load-oriented manufacturing control. Int J Prod Res 26(3):375–395

    Article  Google Scholar 

  22. Wörn H, Längle T, Albert M, Kazi A, Brighenti A, Seijo SR, Christopher Senior Bobi MAS, Collado JV (2004) DIAMOND: distributed multi-agent architecture for monitoring and diagnosis. Prod Plan Cont: Manag Operat 15(2):189–200

    Article  Google Scholar 

  23. Xu Z, Zhao Z, Baines RW (2000) Constructing virtual environments for manufacturing simulation. Int J Prod Res 38(17):4171–4191

    Article  Google Scholar 

  24. Shaw MJ (2004) Dynamic scheduling in cellular manufacturing systems: a framework for networked decision making. J Manuf Syst 7(2):83–94

    Article  Google Scholar 

  25. Pechoucek M, Vokrinek J, Becvar P (2005) A multi-agent support for manufacturing decision making. IEEE Intel Syst 20(1):67–74

    Article  Google Scholar 

  26. Ottaway TA, Burns JR (2000) An adaptive production control system utilizing agent technology. Int J Prod Res 38(4):721–737

    Article  MATH  Google Scholar 

  27. Cheeseman MJ, Swann P, Hesketh GB, Barnes S (2005) Adaptive manufacturing scheduling: a flexible and configurable agent-based prototype. Prod Plan Cont: Manag Operat 16(5):479–487

    Article  Google Scholar 

  28. Brennan RW, William O (2004) Performance analysis of a multi-agent scheduling and control system under manufacturing disturbances. Prod Plan Cont: Manag Operat 15(2):225–235

    Article  Google Scholar 

  29. Vrba P, Radakovic M, Obitko M, Marik V (2011) Semantic technologies: latest advances in agent-based manufacturing control systems. Int J Prod Res 49(5):1483–1496

    Article  Google Scholar 

  30. Jaime CV (2009) Development in the application of ICT in condition monitoring and maintenance. Comput Ind 60:1–20

    Article  Google Scholar 

  31. Muller A, Crespo Marquez A, Iung B (2008) On the concept of e-maintenance: review and current research. Reliab Eng Syst Saf 93:1165–1187

    Article  Google Scholar 

  32. Xinhua L, Xiaolong C, Guomin S, Bihong X (2014) Development of a virtual maintenance system with virtual hand. The Int J Adv Manuf Technol 70:2241–2247

    Article  Google Scholar 

  33. Huiskonen J (2001) Maintenance spare parts logistics: special characteristics and strategic choices. Int J Prod Econ 71(1):125–133

    Article  Google Scholar 

  34. Santiago A, Rosa MS (1997) An information system for computer-integrated manufacturing systems. Rob & Comp-Integ Manuf 13(3):217–228

    Article  Google Scholar 

  35. Ercan O, Esra KT (2009) A general framework of a Reference Model for Intelligent Integrated Manufacturing Systems (REMIMS). Eng Appl Artif Inte 22:855–864

    Article  Google Scholar 

  36. Weerahandi S, Kurien TV, Sadrian A (1996) Estimating and optimizing the efficacy of predictive systems in proactive maintenance. Telecom Syst 6(1):315–327

    Article  Google Scholar 

  37. Shaw CF, Keith AS, Kevin KJ (2005) Manufacturing planning and predictive process model integration using software agents. Advan Eng Inform 19:135–142

    Article  Google Scholar 

  38. Li X, Chaoyong Z, Liang G, Weidong L, Xinyu S (2010) An agent-based approach for integrated process planning and scheduling. Expert Syst Appl 37:1256–1264

    Article  Google Scholar 

  39. Cowling PI, Ouelhadj D, Petrovic S (2003) A multi agent architecture for dynamic scheduling of steel hot rolling. J Intel Manuf 14:457–470

    Article  Google Scholar 

  40. Yunfei C, Fengqi Y, John MW (2014) Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes. Comp Chem Eng 60:277–296

    Article  Google Scholar 

  41. Miltenburg J, Sparling D (1996) Managing and reducing total cycle time: models and analysis. Int J Prod Econ 46:89–108

    Article  Google Scholar 

  42. Yang J, Deane RH (2002) A lotsize reduction model for just-in-time manufacturing systems. Integ Manuf Syst 13(7):471–488

    Article  Google Scholar 

  43. Lin HF, Lee GG (2005) Impact of organizational learning and knowledge management factors on e-business adoption. Manag Decis 43(2):171–188

    Article  Google Scholar 

  44. Maurice B, Mohamad YJ (2013) Developing an input–output activity matrix (IOAM) for environmental and economic analysis of manufacturing systems and logistics chains. Int J Prod Econ 143:589–597

    Article  Google Scholar 

  45. Koufteros XA (1999) Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. J Oper Manag 17:467–488

    Article  Google Scholar 

  46. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2006) Multivariate data analysis. Pearson Prentice Hall, New Jersey

  47. Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334

    Article  MATH  Google Scholar 

  48. Kaynak H (2003) The relationship between total quality management practices and their effects on firm performance. J Oper Manag 21(4):405–436

    Article  Google Scholar 

  49. Gijo EV, Scaria J (2014) Process improvement through six sigma with beta correction: a case study of manufacturing company. Int J Adv Manuf Technol 71:717–730

    Article  Google Scholar 

  50. Liu X, Peng GL, Liu XM, Hou YF (2010) Development of a collaborative virtual maintenance environment with agent technology. J Manuf Syst 29(4):173–181

    Article  Google Scholar 

  51. Leger JB, Neunreuther E, Iung B, Morel G (1999) Integration of the predictive maintenance in manufacturing system. In: Tzafestas SG (ed) Advances in manufacturing. Springer, London, pp 133–144

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Mechanical Engineering Department, National Institute of Technology Hamirpur, Office No. 305, MED, Hamirpur, Himachal Pradesh, 177005, India

    Somesh Kumar Sharma

  2. Fabrication Engineering, Afcons Infrastructure Ltd., AIL, Mumbai, India

    Swatantra Vishwakarma

  3. Application Specialist, SKF India Ltd., SKF Ltd. Gurgaon, India

    Nishant Jha

Authors
  1. Somesh Kumar Sharma
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  2. Swatantra Vishwakarma
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  3. Nishant Jha
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Correspondence to Somesh Kumar Sharma.

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Sharma, S.K., Vishwakarma, S. & Jha, N. Prognosis agent technology: influence on manufacturing organizations. Int J Adv Manuf Technol 92, 435–446 (2017). https://doi.org/10.1007/s00170-017-0025-7

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  • DOI: https://doi.org/10.1007/s00170-017-0025-7

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