Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process

Overall equipment effectiveness (OEE) is implemented by the case company,an aerospace part manufacturing company, to encourage machines to operate all the time at the ideal speed and produce no quality defect in extreme case.However,integration between workstations and transporting activities,deviat...

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institution Universiti Teknikal Malaysia Melaka
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advisor Perumal, Puvanasvaran A.

topic T Technology (General)
TS Manufactures
spellingShingle T Technology (General)
TS Manufactures
Teoh, Yong Siang
Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
description Overall equipment effectiveness (OEE) is implemented by the case company,an aerospace part manufacturing company, to encourage machines to operate all the time at the ideal speed and produce no quality defect in extreme case.However,integration between workstations and transporting activities,deviation of production from customer demand,and imbalanced capacity among processes are neglected under OEE implementation.The consequences include inefficient material flow,overproduction and excessive inventory level,as well as lack of interaction between workstations.Therefore,objectives of this study aim to quantify the impact of transportation efficiency onto the workstations,to synchronize capacity available among them and also to monitor the fulfillment of customer demand in terms of delivery time and production amount.The critical measures are shorter lead time and wait time,less throughput,minimal equipment utilization and less capacity incurred.Simulation results have shown that both transportation efficiency and performance of Autoclave workstation affect material flow and throughput rate respectively.Consequently,the performance of workstations they connect with are also affected.Besides, simulation also proves different production rate and imbalanced capacity throughout production system. Therefore,Overall Performance Effectiveness (OPE) is proposed to consider customer demand,historical equipment utilization and Takt time of each workstation.This promotes reasonable utilization of resource to avoid both overprocessing and overproduction issues which are invisible in OEE.Furthermore,delay propagation throughout production system and interrelationship between processes are quantified by delivery performance (DP) of OPE.The waiting time and lead time spent in each workstation are monitored under the DP.Responsibility of all workstations and transportation process in delivering demand on time are quantified.Last but not least,transportation process which serves as the connectors of manufacturing processeses is also quantified and monitored by proposed Transportation Measure (TM).TM aims to reduce the queue length at destination and the corresponding waiting time with reasonable utilization of forklift.It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.In short,newly proposed Overall Performance Effectiveness (OPE) and the quantification of Transportation Measure (TM),which affect each other,help in promoting better delivery performance in terms of production amount and lead time.Besides,reasonable utilization equipment and minimal consumption of material are promoted to fulfill the demand.The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation.Both OPE and TM could be implemented together to optimize the production system.All of these are not quantified and provided by the OEE implemented by the case company.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Teoh, Yong Siang
author_facet Teoh, Yong Siang
author_sort Teoh, Yong Siang
title Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
title_short Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
title_full Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
title_fullStr Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
title_full_unstemmed Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process
title_sort development of overall performance effectiveness in job shop manufacturing process
granting_institution UTeM
granting_department Faculty Of Manufacturing Engineering
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/23380/1/Development%20Of%20Overall%20Performance%20Effectiveness%20In%20Job%20Shop%20Manufacturing%20Process.pdf
http://eprints.utem.edu.my/id/eprint/23380/2/Development%20Of%20Overall%20Performance%20Effectiveness%20In%20Job%20Shop%20Manufacturing%20Process.pdf
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spelling my-utem-ep.233802022-02-16T12:24:58Z Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process 2018 Teoh, Yong Siang T Technology (General) TS Manufactures Overall equipment effectiveness (OEE) is implemented by the case company,an aerospace part manufacturing company, to encourage machines to operate all the time at the ideal speed and produce no quality defect in extreme case.However,integration between workstations and transporting activities,deviation of production from customer demand,and imbalanced capacity among processes are neglected under OEE implementation.The consequences include inefficient material flow,overproduction and excessive inventory level,as well as lack of interaction between workstations.Therefore,objectives of this study aim to quantify the impact of transportation efficiency onto the workstations,to synchronize capacity available among them and also to monitor the fulfillment of customer demand in terms of delivery time and production amount.The critical measures are shorter lead time and wait time,less throughput,minimal equipment utilization and less capacity incurred.Simulation results have shown that both transportation efficiency and performance of Autoclave workstation affect material flow and throughput rate respectively.Consequently,the performance of workstations they connect with are also affected.Besides, simulation also proves different production rate and imbalanced capacity throughout production system. Therefore,Overall Performance Effectiveness (OPE) is proposed to consider customer demand,historical equipment utilization and Takt time of each workstation.This promotes reasonable utilization of resource to avoid both overprocessing and overproduction issues which are invisible in OEE.Furthermore,delay propagation throughout production system and interrelationship between processes are quantified by delivery performance (DP) of OPE.The waiting time and lead time spent in each workstation are monitored under the DP.Responsibility of all workstations and transportation process in delivering demand on time are quantified.Last but not least,transportation process which serves as the connectors of manufacturing processeses is also quantified and monitored by proposed Transportation Measure (TM).TM aims to reduce the queue length at destination and the corresponding waiting time with reasonable utilization of forklift.It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.In short,newly proposed Overall Performance Effectiveness (OPE) and the quantification of Transportation Measure (TM),which affect each other,help in promoting better delivery performance in terms of production amount and lead time.Besides,reasonable utilization equipment and minimal consumption of material are promoted to fulfill the demand.The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation.Both OPE and TM could be implemented together to optimize the production system.All of these are not quantified and provided by the OEE implemented by the case company. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23380/ http://eprints.utem.edu.my/id/eprint/23380/1/Development%20Of%20Overall%20Performance%20Effectiveness%20In%20Job%20Shop%20Manufacturing%20Process.pdf text en public http://eprints.utem.edu.my/id/eprint/23380/2/Development%20Of%20Overall%20Performance%20Effectiveness%20In%20Job%20Shop%20Manufacturing%20Process.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112881 phd doctoral UTeM Faculty Of Manufacturing Engineering Perumal, Puvanasvaran A. 1. Adanna, I. W., and Shantharam, A., 2013. Improvement of setup time and production output with the use of single minute exchange of die principles (SMED). Int. J. Eng. Res, 2, pp. 274-277. 2. Adenso-Diaz, B., Gonzalez-Torre, P., and Garcia, V., 2002. A capacity management model in service industries. International Journal of Service Industry Management, 13(3), pp. 286-302. 3. Afefy, I. H., 2013. Implementation of total productive maintenance and overall equipment effectiveness evaluation. International Journal of Mechanical and Mechatronics Engineering, 13(01), pp. 69-75 4. Ahire, C. P., and Relkar, A. S. 2012. Correlating failure mode effect analysis (FMEA) and overall equipment effectiveness (OEE). Procedia Engineering, 38, pp. 3482-3486. 5. Ak, A., and Erera, A. L., 2007. A paired-vehicle recourse strategy for the vehicle-routing problem with stochastic demands. Transportation Science, 41(2), pp. 222-237 6. Albrecht, F., Faatz, L., and Abele, E. 2013. Multidimensional evaluation of the changeability of interlinked production processes with material flow simulation. Procedia CIRP, 7, pp. 139-144 7. Ali, R., and Deif, A., 2016. Assessing leanness level with demand dynamics in a multi-stage production system. Journal of Manufacturing Technology Management, 27(5), pp. 614-639. 8. Alpenberg, J., and Scarbrough, D. P., 2016. Exploring communication practices in lean production. Journal of Business Research, 69(11), pp. 4959-4963. 9. Al-Turki, U., and Duffuaa, S., 2003. Performance measures for academic departments. International Journal of Educational Management, 17(7), pp. 330-338. 10. Anvari, F., Edwards, R., and Starr, A., 2010. Evaluation of overall equipment effectiveness based on market. Journal of Quality in Maintenance Engineering, 16(3), pp. 256-270 11. Aqlan, F., Lam, S. S., and Ramakrishnan, S., 2014. An integrated simulation.optimization study for consolidating production lines in a configure-to-order production environment. International Journal of Production Economics, 148, pp. 51-61 12. Arafa, A., and ElMaraghy, W. H., 2011. Manufacturing strategy and enterprise dynamic capability. CIRP Annals-Manufacturing Technology, 60(1), pp. 507-510. 13. Arakawa, M., Fuyuki, M., and Inoue, I. 2003. An optimization-oriented method for simulation-based job shop scheduling incorporating capacity adjustment function. International Journal of Production Economics, 85(3), pp. 359-369. 14. Arbos, L. C. 2009. Diseno avanzado de procesos y plantas de produccion flexible: tecnicas de diseno y herramientas graficas con soporte informatico. Profit Editorial. 15. Badiger, A. S., and Gandhinathan, R., 2008. A proposal: evaluation of OEE and impact of six big losses on equipment earning capacity. International Journal of Process Management and Benchmarking, 2(3), pp. 234-248. 16. Bamber, C. J., Castka, P., Sharp, J. M., and Motara, Y., 2003. Cross-functional team working for overall equipment effectiveness (OEE). Journal of Quality in Maintenance Engineering, 9(3), pp. 223-238. 17. Banks, J., Carson II, J. S., Nelson, B. L. and Nicol, D. M., Eds. 2010. Discrete-event system simulation, New Jersey: Pearson 18. Barletta, I., Andersson, J., Johansson, B., May, G., and Taisch, M., 2014, December. Assessing a proposal for an energy-based overall equipment effectiveness indicator through discrete event simulation. In Simulation Conference (WSC), 2014 Winter, pp. 1096-1107. IEEE 19. Baykaso.lu, A., and Ozbak.r, L. 2008. Analysing the effect of flexibility on manufacturing systems performance. Journal of Manufacturing Technology Management, 19(2), pp. 172-193 20. Benjamin, S. J., Marathamuthu, M. S., and Murugaiah, U., 2015. The use of 5-WHYs technique to eliminate OEE�fs speed loss in a manufacturing firm. Journal of Quality in Maintenance Engineering, 21(4), pp. 419-435 21. Benttaleb, M., Hnaien, F., and Yalaoui, F., 2016. Two-machine job shop problem for makespan minimization under availability constraint. IFAC-PapersOnLine, 49(28), pp. 132-137 22. Bernstein, R. 2005. Insights on implementation TPM collected practices and cases 23. Bhasin, S. 2008. Lean and performance measurement. Journal of Manufacturing Technology Management, 19(5), pp. 670-684. 24. Bititci, U., McLeod, M., and Turner, T. 2003. Using OEE to improve performance of a fast moving food company. In Proceedings of the 3rd International Workshop on Performance Measurement 25. Bonal, J., Ortega, C., Rios, L., Aparicio, S., Fernandez, M., Rosendo, M., and Malvar, S. 1996. Overall fab efficiency [semiconductor manufacturing]. In Advanced Semiconductor Manufacturing Conference and Workshop, 1996. ASMC 96 Proceedings. IEEE/SEMI 1996 (pp. 49-52). IEEE 26. Borkowski, S., Czajkowska, A., Stasiak-Betlejewska, R., and Borade, A. B., 2014. Application of TPM indicators for analyzing work time of machines used in the pressure die casting. Journal of Industrial Engineering International, 10(2), pp. 55-65 27. Braglia, M., Frosolini, M., and Zammori, F. 2008. Overall equipment effectiveness of a manufacturing line (OEEML) An integrated approach to assess systems performance. Journal of Manufacturing Technology Management, 20(1), pp. 8-29 28. Brandt, J.R. and Taninecz, G. 2005, Capacity utilization, Glass in It Style, Vol. 1 No. 1, pp. 10-18 29. Bruggen, A., 2015. An empirical investigation of the relationship between workload and performance. Management Decision, 53(10), pp. 2377-2389 30. Cesarotti, V., Giuiusa, A., and Introna, V., 2013. Using Overall Equipment Effectiveness for Manufacturing System Design. INTECH Open Access Publisher 31. Chahal, V., and Narwal, M., 2017. An empirical review of lean manufacturing and their strategies. Management Science Letters, 7(7), pp. 321-336 32. Cheema, C. D. D., Ed. 2005. Operations Research, New Delhi: Laxmi Publications (P) LTD. 33. Chen, Z., and Sarker, B. R., 2015. Aggregate production planning with learning effect and uncertain demand: A case based study. Journal of Modelling in Management, 10(3), pp. 296-324 34. Chiarini, A. 2013. Waste savings in patient transportation inside large hospitals using lean thinking tools and logistic solutions. Leadership in Health Services, 26(4), pp. 356-367 35. Chong, K. E., Ng, K. C., and Goh, G. G. G., 2015, December. Improving Overall Equipment Effectiveness (OEE) through integration of Maintenance Failure Mode and Effect Analysis (maintenance-FMEA) in a semiconductor manufacturer: A case study. In Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference on, pp. 1427-1431. IEEE. 36. Chowdhury, C., and Mandal, T. K., 1995, Equipment effectiveness and six big losses. Productivity, 36(1), pp. 110-117. 37. Chung, C. A. 2004. Simulation modelling handbook. A practical approach. First edition, CRC press, Boca Raton 38. Colares, E. M. T. How to solve the trade-off between capacity utilization and service level 39. Cuatrecasas-Arbos, L., Fortuny-Santos, J., Ruiz-de-Arbulo-Lopez, P., and Vintro-Sanchez, C., 2015. Monitoring processes through inventory and manufacturing lead time. Industrial Management and Data Systems, 115(5), pp. 951-970. 40. Dal, B., Tugwell, P., and Greatbanks, R., 2000. Overall equipment effectiveness as a measure of operational improvement-A practical analysis.International Journal of Operations and Production Management, 20(12), pp. 1488-1502. 41. De Carlo, F., Arleo, M. A., and Tucci, M., 2014. OEE Evaluation of a Paced Assembly Line Through Different Calculation and Simulation Methods: A Case Study in the Pharmaceutical Environment. International Journal of Engineering Business Management. 42. De Ron, A. J., and Rooda, J. E., 2005. Equipment effectiveness: OEE revisited. IEEE Transactions on Semiconductor Manufacturing, 18(1), pp. 190-196. 43. Demeter, K., and Matyusz, Z. 2011. The impact of lean practices on inventory turnover. International Journal of Production Economics, 133(1), pp. 154-163. 44. Dhand, S., and Singla, A., 2016. Sensitivity Analysis and Optimal Production Scheduling as a Dual Phase Simplex Model. Indian Journal of Science and Technology, 9(39) 45. Domingo, R., and Aguado, S. 2015. Overall environmental equipment effectiveness as a metric of a lean and green manufacturing system. Sustainability, 7(7), pp. 9031-9047. 46. Dro.ge, C., Jayaram, J., and Vickery, S. K., 2004. The effects of internal versus external integration practices on time-based performance and overall firm performance. Journal of operations management, 22(6), pp. 557-573 47. Duran, O., Capaldo, A., and Duran Acevedo, P. A. 2018. Sustainable Overall Throughputability Effectiveness (SOTE) as a Metric for Production Systems. Sustainability, 10(2), pp. 362-370. 48. Ebrahim, Z. 2011. Fit Manufacturing: Production Fitness as the measure of production operations performance. Cardiff University (United Kingdom) 49. Esmaeel, R. I., Zakuan, N., Jamal, N. M., and Taherdoost, H. 2018. Understanding of business performance from the perspective of manufacturing strategies: fit manufacturing and overall equipment effectiveness. Procedia Manufacturing, 22, pp. 998-1006 50. Eswaramurthi, K. G., and Mohanram, P. V., 2013. Improvement of manufacturing performance measurement system and evaluation of overall resource effectiveness. American Journal of Applied Sciences, 10(2), p. 131-140. 51. Fawcett, S. E., Calantone, R., and Smith, S. R., 1997. Delivery capability and firm performance in international operations. International Journal of Production Economics, 51(3), pp. 191-204 52. Fawcett, S. E., and Closs, D. J., 1993. Coordinated global manufacturing, the logistics/manufacturing interaction, and firm performance. Journal of Business Logistics, 14(1), pp. 1-12 53. Fleischer, J., Weismann, U., and Niggeschmidt, S., 2006. Calculation and optimisation model for costs and effects of availability relevant service elements. Proceedings of LCE, pp. 675-680. 54. Flynn, B. B., Huo, B., and Zhao, X., 2010. The impact of supply chain integration on performance: A contingency and configuration approach. Journal of operations management, 28(1), pp. 58-71 55. Gan, S. Y., and Chong, K. E., 2014. Improving throughput and completion date estimation in high precision component manufacturer using simulation approach. Journal of Advanced Manufacturing Technology (JAMT), 7(1) 56. Gansterer, M., Almeder, C., and Hartl, R. F. 2014. Simulation-based optimization methods for setting production planning parameters. International Journal of Production Economics, 151, pp. 206-213 57. Garza-Reyes, J. A., 2015. From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering, 21(4), pp. 506-527 58. Germain, R., and Iyer, K. N. 2006. The interaction of internal and downstream integration and its association with performance. Journal of Business Logistics, 27(2), pp. 29-52 59. Goldratt, E. M, and Cox, J. 1992. The Goal: A Process of Ongoing Improvement. North River Press 60. Graves, S. C., 1981. A review of production scheduling. Operations research, 29(4), pp. 646-675 61. Grewal, C. S., Enns, S. T., and Rogers, P. 2010. Dynamic adjustment of replenishment parameters using optimumseeking simulation. In Proceedings of the Winter Simulation Conference (pp. 1797-1808). Winter Simulation Conference 62. Gunasekaran, A. 1999. Agile manufacturing: a framework for research and development. International journal of production economics, 62(1-2), pp. 87-105 63. Gyulai, D., and Monostori, L., 2017. Capacity management of modular assembly systems. Journal of Manufacturing Systems, 43, pp. 88-99 64. Gyulai, D., Kadar, B., Kovacs, A., and Monostori, L., 2014. Capacity management for assembly systems with dedicated and reconfigurable resources. CIRP Annals-Manufacturing Technology, 63(1), pp. 457-460 65. Hansen, R. C. 2001. Overall equipment effectiveness: a powerful production/maintenance tool for increased profits. Industrial Press Inc 66. Heilala, J., Vatanen, S., Tonteri, H., Montonen, J., Lind, S., Johansson, B., and Stahre, J., 2008, December. Simulation-based sustainable manufacturing system design. In 2008 Winter Simulation Conference, pp. 1922-1930. IEEE. 67. Helo, P. T., 2000. Dynamic modelling of surge effect and capacity limitation in supply chains. International Journal of Production Research, 38(17), pp. 4521-4533 68. Hill, A. V., 2012. The encyclopedia of operations management: a field manual and glossary of operations management terms and concepts. FT Press 69. Huang, R. H., Yang, C. L., and Cheng, W. C., 2013. Flexible job shop scheduling with due window- a two-pheromone ant colony approach. International Journal of Production Economics, 141(2), pp. 685-697 70. Huang, S. H., and Keskar, H., 2007. Comprehensive and configurable metrics for supplier selection. International journal of production economics, 105(2), pp. 510-523. 71. Huang, S. H., Dismukes, J. P., Shi, J., Su, Q. I., Razzak, M. A., Bodhale, R., and Robinson, D. E., 2003. Manufacturing productivity improvement using effectiveness metrics and simulation analysis. International Journal of Production Research, 41(3), pp. 513-527 72. Huang, S. H., Dismukes, J. P., Shi, J., and Su, Q., 2002. Manufacturing system modeling for productivity improvement. Journal of Manufacturing Systems, 21(4), pp. 249-259 73. Iannone, R., and Nenni, M. E. 2013. Managing OEE to optimize factory performance. In Operations Management. InTech. 74. Ivancic, I. 1998. Development of Maintenance in Modern Production: proceedings of 14th European Maintenance Conference, EUROMAINTENANCE�fOctober. Du-brovnik, Hrvatska, 98, pp. 5-7 75. Iyer, K. N., Germain, R., and Frankwick, G. L., 2004. Supply chain B2B e-commerce and time-based delivery performance. International Journal of Physical Distribution and Logistics Management, 34(8), pp. 645-661 76. Jackson, J. R., 1956. An extension of Johnson's results on job IDT scheduling. Naval Research Logistics (NRL), 3(3), pp. 201-203 77. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., and Young, T. 2010. Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), pp. 1-13 78. Jebaraj Benjamin, S., Murugaiah, U., and Srikamaladevi Marathamuthu, M., 2013. The use of SMED to eliminate small stops in a manufacturing firm. Journal of Manufacturing Technology Management, 24(5), pp. 792-807 79. Jonsson, P., and Lesshammar, M., 1999. Evaluation and improvement of manufacturing performance measurement systems-the role of OEE. International Journal of Operations and Production Management, 19(1), pp. 55-78. 80. Kennerley, M., and Neely, A., 2003. Measuring performance in a changing business environment. International Journal of Operations and Production Management, 23(2), pp. 213-229. 81. Krol, T. A., Seidel, C., and Zaeh, M. F. 2013. Prioritization of process parameters for an efficient optimisation of additive manufacturing by means of a finite element method. Procedia CIRP, 12, pp. 169-174 82. Kuhn, W. 2006. Digital factory-simulation enhancing the product and production engineering process. In Simulation Conference, 2006. WSC 06. Proceedings of the 83. Winter (pp. 1899-1906). IEEE 84. Kuo, Y., and Lin, K. P. 2010. Using neural network and decision tree for machine reliability prediction. The International Journal of Advanced Manufacturing Technology, 50(9-12), pp. 1243-1251 85. Kutanoglu, E., and Sabuncuoglu, I. 2001. Experimental investigation of iterative simulationbased scheduling in a dynamic and stochastic job shop. Journal of Manufacturing Systems, 20(4), pp. 264-279 86. Lee, J., Lapira, E., Bagheri, B., and Kao, H. A., 2013. Recent advances and trends in predictive manufacturing systems in big data environment Manufacturing Letters, 1(1), pp. 38-41 87. Lee, J., and Lapira, E., 2011. TPM gets smart. Manufacturing Engineering,146(6), pp. 70- 75 88. Liu, C. H. 2010. A coordinated scheduling system for customer orders scheduling problem in job shop environments. Expert Systems with Applications, 37(12), pp. 7831-7837 89. Martinez Sanchez, A., and Perez Perez, M. 2001. Lean indicators and manufacturing strategies. International Journal of Operations and Production Management, 21(11), pp. 1433-1452. 90. Morash, E. A., and Clinton, S. R., 1998. Supply chain integration: customer value through collaborative closeness versus operational excellence. Journal of Marketing Theory and Practice, 6(4), pp. 104-120. 91. Mourtzis, D., and Doukas, M. 2014. Design and planning of manufacturing networks for mass customisation and personalisation: challenges and outlook. Procedia CIRP, 19, pp. 1- 13 92. Mourtzis, D., Doukas, M., and Bernidaki, D. 2014. Simulation in manufacturing: Review and challenges. Procedia CIRP, 25,pp. 213-229 93. Mourtzis, D., Doukas, M., and Psarommatis, F. 2012. A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment. CIRP Annals-Manufacturing Technology, 61(1), pp. 427-430 94. Mrugalska, B., and Wyrwicka, M. K., 2017. Towards lean production in industry 4.0. Procedia Engineering, 182, pp. 466-473 95. Muchiri, P., and Pintelon, L. 2008. Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. International journal of production research, 46(13), pp. 3517-3535 96. Mugwindiri, K., Nyemba, W. R., Madanhire, I., and Mushonga, R., 2013. The Design of a Production Planning and Control System for a Food Manufacturing Company in a Developing Country , using Simulation ., 2(6), pp. 116.125 97. Munirathinam, S., and Ramadoss, B., 2014. Big Data Predictive Analtyics for Proactive Semiconductor Equipment Maintenance: A Review. 98. Muthiah, K. M. N., and Huang, S. H. 2007. Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection. International Journal of Production Research, 45(20), pp. 4753-4769 99. Muthiah, K. M., Huang, S. H., and Mahadevan, S. 2008. Automating factory performance diagnostics using overall throughput effectiveness (OTE) metric. The International Journal of Advanced Manufacturing Technology, 36(7-8), pp. 811-824. 100. Nachiappan, R. M., and Anantharaman, N. 2006. Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system. Journal of Manufacturing Technology Management, 17(7), pp. 987-1008 101. Nakajima, S., 1988. Introduction to TPM: Total Productive Maintenance.(Translation). Productivity Press, Inc., 1988, pp. 129-140. 102. Natrella, M. G., 1963. National Bureau of Standards handbook 91: Experimental statistics. Washington, DC: US Department of Commerce. 103. Noble, M. A., 1997. Manufacturing competitive priorities and productivity: an empirical study. International Journal of Operations and Production Management, 17(1), pp. 85-99 104. Nollet, J., 1991. Services Plus: effective service management. Boucherville, Que.: Morin. 105. Oechsner, R., Pfeffer, M., Pfitzner, L., Binder, H., Muller, E., and Vonderstrass, T. 2002. From overall equipment efficiency (OEE) to overall Fab effectiveness (OFE). Materials Science in Semiconductor Processing,5(4), pp. 333-339 106. Pakdil, F., and Leonard, K. M., 2014. Criteria for a lean organisation: development of a lean assessment tool. International Journal of Production Research, 52(15), pp. 4587-4607. 107. Papakostas, N., Efthymiou, K., Mourtzis, D., and Chryssolouris, G. 2009. Modelling the complexity of manufacturing systems using nonlinear dynamics approaches. CIRP annals, 58(1), pp. 437-440 108. Pegden, C. D., Shannon, R. E., and Sadowski, R. P. 1995. Introduction to simulation using SIMAN (Vol. 2). New York: McGraw-Hill 109. Pham, D. T., and Thomas, A. J. 2011. Fit manufacturing: a framework for sustainability. Journal of Manufacturing Technology Management, 23(1), pp. 103-123 110. Pierreval, H., Bruniaux, R., and Caux, C. 2007. A continuous simulation approach for supply chains in the automotive industry. Simulation Modelling Practice and Theory, 15(2), pp. 185-198 111. Ponsignon, T., and Monch, L., 2014. Simulation-based performance assessment of master planning approaches in semiconductor manufacturing.Omega, 46, pp. 21-35. 112. Puvanasvaran, A. P., Mei, C. Z., and Alagendran, V. A. 2013. Overall equipment efficiency improvement using time study in an aerospace industry. Procedia Engineering, 68, pp. 271- 277. 113. Ramlan, R., Ngadiman, Y., Omar, S. S., and Yassin, A. M., 2015, August. Quantification of machine performance through Overall Equipment Effectiveness. In Technology Management and Emerging Technologies (ISTMET), 2015 International Symposium on (pp. 407-411). IEEE. 114. Raouf, A. 1994. Improving capital productivity through maintenance. International Journal of Operations and Production Management, 14(7), pp. 44-52 115. Rawabdeh, I. A., 2005. A model for the assessment of waste in job shop environments. International Journal of Operations and Production Management, 25(8), pp. 800-822. 116. Razzak, M. A., Daley, G., and Dismukes, J. P., 2002. Factory Level Metrics: Basis for Productivity Improvement. In Proceedings of the International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM2002), pp. 158-162 117. Render, B., and Stair Jr, R. M. 2006. Quantitative Analysis for Management, 12e. Pearson Education India 118. Robinson, C. 2004. Calculating line or process OEE. Maintenance Technology, available at:www.mt-online.com/newarticles2/06-94mm.cfm 119. Robinson, S. 2004. Simulation: the practice of model development and use (p. 336). Chichester: Wiley 120. Rosenzweig, E. D., Roth, A. V., and Dean, J. W., 2003. The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers. Journal of operations management, 21(4), pp. 437-456 121. Roser, C., Lorentzen, K., and Deuse, J., 2014. Reliable shop floor bottleneck detection for flow lines through process and inventory observations. Procedia CIRP, 19, pp. 63-68 122. Sakun Boon-itt and Chee Yew Wong, 2011. The moderating effects of technological and demand uncertainties on the relationship between supply chain integration and customer delivery performance. International Journal of Physical Distribution and Logistics Management, 41(3), pp. 253-276 123. Salegna, G. J., and Park, P. S., 1996. Workload smoothing in a bottleneck job shop. International Journal of Operations and Production Management, 16(1), pp. 91-110 124. Samad, M. A., Hossain, M. R., and Major, S., 2012. Analysis of Performance by Overall Equipment Effectiveness of the CNC Cutting Section of a Shipyard, 2(11), pp. 1091.1096 125. Savsar, M., and Al-Jawini, A., 1995. Simulation analysis of just-in-time production systems. International Journal of Production Economics, 42(1), pp. 67-78 126. Scott, D., and Pisa, R., 1998. Can overall factory effectiveness prolong Mooer's law? Solid state technology, 41(3), pp. 75-81 127. Shannon, R. E. 1975. Systems simulation; the art and science (No. 04; T57. 62, S4.) 128. Simons, D., Mason, R., and Gardner, B., 2004. Overall vehicle effectiveness. International Journal of Logistics Research and Applications, 7(2), pp. 119-135 129. Singh, R., Shah, D. B., Gohil, A. M., and Shah, M. H., 2013. Overall Equipment Effectiveness (OEE) calculation-Automation through hardware and software development. Procedia Engineering, 51, pp. 579-584 130. Soltan, H., and Mostafa, S. 2015. Lean and agile performance framework for manufacturing enterprises. Procedia Manufacturing, 2, pp. 476-484 131. Stamatis, D. H. 2010. The OEE primer: understanding overall equipment effectiveness, reliability, and maintainability. CRC Press 132. Sternberg, H., Stefansson, G., Westernberg, E., Boije af Gennas, R., Allenstrom, E., and Linger Nauska, M., 2012. Applying a lean approach to identify waste in motor carrier operations. International Journal of Productivity and Performance Management, 62(1), pp. 47-65 133. Swink, M., Narasimhan, R., and Wang, C., 2007. Managing beyond the factory walls: effects of four types of strategic integration on manufacturing plant performance. Journal of Operations Management, 25(1), pp. 148-164 134. Tamizharasi, G., and Kathiresan, S. 2012. Optimizing Overall Equipment Effectiveness of high precision SPM using TPM tools. International Journal of Computer Trends and Technology (IJCTT), 3(4), pp. 1-9 135. Teoh, Y. S., and Ito, T., 2015. Customer Demand and Its Impact on Equipment Utilization. In Manufacturing System Division Conference 2015 (pp. 106.107). The Japan Society of Mechanical Engineers 136. Thurer, M., Stevenson, M., Silva, C., and Qu, T., 2017. Drum-buffer-rope and workload control in High-variety flow and job shops with bottlenecks: An assessment by simulation. International Journal of Production Economics, 188, pp. 116-127. 137. Vancza, J., Monostori, L., Lutters, D., Kumara, S. R., Tseng, M., Valckenaers, P., and Van Brussel, H., 2011. Cooperative and responsive manufacturing enterprises. CIRP Annals- Manufacturing Technology, 60(2), pp. 797-820 138. Villarreal, B., Garza-Reyes, J. A., and Kumar, V., 2016. A lean thinking and simulationbased approach for the improvement of routing operations.Industrial Management and Data Systems, 116(5), pp. 903-925 139. Villarreal, B., 2012. The transportation value stream map (TVSM). European Journal of Industrial Engineering, 6(2), pp. 216-233. 140. Villarreal, B., Sanudo, M., Vega, A., Macias, S., and Garza, E., 2012. A Lean Scheme for Improving Vehicle Routing Operations. In Proceedings of the 2012 International Conference on Industrial and Operations Management (IEOM), pp. 3-6. 141. Vollmann, T. E., Berry, W. L., and Whybark, D. C. 2005. Manufacturing planning and control systems. 142. Williamson, R. M. 2006. Using overall equipment effectiveness: the metric and the measures. Strategic Work System, Inc, pp. 1-6. 143. Wong, W. P., Soh, K. L., Chong, C. L., and Karia, N., 2015. Logistics firms performance: efficiency and effectiveness perspectives. International Journal of Productivity and Performance Management, 64(5), pp. 686-701 144. Wongwiwat, A., Bohez, E. L., and Pisuchpen, R. 2013. Production scheduling for injection molding manufacture using Petri Net model. Assembly Automation, 33(3), pp. 282-293. 145. Wudhikarn, R., 2012. Improving overall equipment cost loss adding cost of quality. International Journal of Production Research, 50(12), pp. 3434-3449 146. Xing, L. N., Chen, Y. W., and Yang, K. W. 2009. Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling. Applied Soft Computing, 9(1), pp. 362-376 147. Yuan, R., and Graves, S. C., 2016. Setting optimal production lot sizes and planned lead times in a job shop. International Journal of Production Research, 54(20), pp. 6105-6120 148. Zammori, F., Braglia, M., and Frosolini, M., 2011. Stochastic overall equipment effectiveness. International Journal of Production Research,49(21), pp. 6469-6490. 149. Zammori, F., 2015. Fuzzy Overall Equipment Effectiveness (FOEE): capturing performance fluctuations through LR Fuzzy numbers. Production Planning and Control, 26(6), pp. 451-450 150. Zeller, M., 2014, May. Convert operational data into maintenance savings. In Rural Electric Power Conference (REPC), 2014 IEEE (pp. B6-1). IEEE. 151. Zhang, Y., Wang, Y., and Wu, L., 2012. Research on demand-driven leagile supply chain operation model: a simulation based on any logic in system engineering. Systems Engineering Procedia, 3, pp. 249-258 152. Zhou, J., Zhao, S., Li, P., Zhou, H., Zhang, Q., and Shang, Z., 2009, February. Research on processes simulation and reconfiguration for piston production lines. In Computer Modeling and Simulation, 2009. ICCMS'09. International Conference on (pp. 49-52). IEEE.