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A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs

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  • Poorya Ghafoorpoor Yazdi

    (Department of Mechanical Engineering, Eastern Mediterranean University, 99628 Famagusta, Northern Cyprus
    Department of Engineering, German University of Technology, Muscat 130, Oman)

  • Aydin Azizi

    (Department of Engineering, German University of Technology, Muscat 130, Oman)

  • Majid Hashemipour

    (Department of Mechanical Engineering, Cyprus International University, 99258 Nicosia, Cyprus)

Abstract

The properties of small- and medium-sized enterprises (SMEs) make them one of the most important categories of enterprises for the economics of challenging world. SMEs, in most countries, are still enterprises with marketing and financial challenges. In addition, most of these challenges are related to their production and product characteristics. On the other hand, SMEs should fulfil the costumer’s demands. In order to reach these goals, SMEs must reach the highest level of production quality and quantity and successfully sustain them. Consequently, various manufacturing paradigms have been offered by an Industry 4.0 concept, which offers a variety of solutions to increase the productivity and enhance the performance of SMEs. It should be noted that implementation of these manufacturing paradigms for SMEs is quite difficult and sometimes risky for several reasons. Still, amidst all these difficulties and challenges, the benefits and idealism of the Industry 4.0 paradigms prevail. From productivity to market, it is difficult to deny that SMEs are frightened by the challenges they face and fleeing from the potential of overcoming them. This paper is an extended version of the research by Ghafoorpoor Yazdi et al. (2018) and conducts a hybrid methodology to satisfy the SMEs by validating and verifying any optimization idea before implementing the Industry 4.0 concept. To reach the study goals, an intelligent Material Handling System (MHS) with agent-based control architecture has been developed. The developed MHS has been utilized for auto parts distribution. The system performance has been evaluated, and some solutions have been provided to optimize the performance of system. To evaluate the target system’s performance, an analytical time study method has been utilized. The time study has an Overall Equipment Effectiveness (OEE) standard approach to identifying the matters that need to be resolved and optimized to increase system performance. The other part of the methodology is generating a simulation model of the real system by use of ARENA ® software to evaluate the system’s performance before implementing the optimization idea and modifying the real system. Furthermore, as the sustainability strategies create many synergistic effects for SMEs, after evaluating the effects of the optimization ideas on OEE percentage, the influence of the OEE changes on manufacturing sustainability has been investigated. The results show that optimizing the OEE in SMEs with sustainability approaches can create competitive advantages, rather than simply focusing on reducing unsustainability.

Suggested Citation

  • Poorya Ghafoorpoor Yazdi & Aydin Azizi & Majid Hashemipour, 2019. "A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs," Sustainability, MDPI, vol. 11(5), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1454-:d:212388
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    References listed on IDEAS

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    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    3. Maria Elena Nenni & Raffaele Iannone, 2013. "Managing OEE to Optimize Factory Performance," Chapters, in: Massimiliano M. Schiraldi (ed.), Operations Management, IntechOpen.
    4. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    5. Durieux, Severine & Pierreval, Henri, 2004. "Regression metamodeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource," International Journal of Production Economics, Elsevier, vol. 89(1), pages 21-30, May.
    6. Maria Lai‐Ling Lam, 2011. "Challenges of sustainable environmental programs of foreign multinational enterprises in China," Management Research Review, Emerald Group Publishing Limited, vol. 34(11), pages 1153-1168, October.
    7. Poorya Ghafoorpoor Yazdi & Aydin Azizi & Majid Hashemipour, 2018. "An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach," Sustainability, MDPI, vol. 10(9), pages 1-28, August.
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    2. Eduardo Machado & Luiz Felipe Scavarda & Rodrigo Goyannes Gusmão Caiado & Antonio Márcio Tavares Thomé, 2021. "Barriers and Enablers for the Integration of Industry 4.0 and Sustainability in Supply Chains of MSMEs," Sustainability, MDPI, vol. 13(21), pages 1-31, October.
    3. Piccarozzi, Michela & Silvestri, Cecilia & Aquilani, Barbara & Silvestri, Luca, 2022. "Is this a new story of the ‘Two Giants’? A systematic literature review of the relationship between industry 4.0, sustainability and its pillars," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    4. Giancarlo Nota & Francesco David Nota & Domenico Peluso & Alonso Toro Lazo, 2020. "Energy Efficiency in Industry 4.0: The Case of Batch Production Processes," Sustainability, MDPI, vol. 12(16), pages 1-28, August.
    5. Masood Fathi & Amir Nourmohammadi & Morteza Ghobakhloo & Milad Yousefi, 2020. "Production Sustainability via Supermarket Location Optimization in Assembly Lines," Sustainability, MDPI, vol. 12(11), pages 1-15, June.

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