Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1823 2022-09-08 18:15:57 |
2 layout + 7 word(s) 1830 2022-09-09 02:05:44 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Mourtzis, D.;  Angelopoulos, J.;  Panopoulos, N. Resilient Manufacturing Systems. Encyclopedia. Available online: https://encyclopedia.pub/entry/27028 (accessed on 24 June 2024).
Mourtzis D,  Angelopoulos J,  Panopoulos N. Resilient Manufacturing Systems. Encyclopedia. Available at: https://encyclopedia.pub/entry/27028. Accessed June 24, 2024.
Mourtzis, Dimitris, John Angelopoulos, Nikos Panopoulos. "Resilient Manufacturing Systems" Encyclopedia, https://encyclopedia.pub/entry/27028 (accessed June 24, 2024).
Mourtzis, D.,  Angelopoulos, J., & Panopoulos, N. (2022, September 08). Resilient Manufacturing Systems. In Encyclopedia. https://encyclopedia.pub/entry/27028
Mourtzis, Dimitris, et al. "Resilient Manufacturing Systems." Encyclopedia. Web. 08 September, 2022.
Resilient Manufacturing Systems
Edit

Resilient Manufacturing is defined as the ability of a manufacturing system to efficiently mitigate any external disruptions either derived from the supply chain of the company or resulted from the volatility of the market demand.

discrete-event simulation resilient manufacturing design design of experiments

1. Introduction

Manufacturing is an extremely important sector of the global economy, accounting for 15.391 percent of global GDP in 2019, representing an added value of US$ 13,779 trillion [1][2]. In particular, due to the rapid development of digital technologies, the manufacturing environment is changing rapidly. This rapid growth and adoption of emerging technology by manufacturing companies has created the so-called “Fourth Industrial Revolution” or “Industry 4.0. Industry 4.0 is associated with manufacturing trends and technologies such as cloud technology, cyber-physical systems, the Internet of Things, augmented and virtual reality, and many more [3][4]. Living in the Industry 4.0 era, where competitiveness is the dominant factor in the modern market landscape, the constant evolution of manufacturing systems constitutes an indispensable procedure. This ceaseless progress requires the investigation of various technologies and techniques in order to respond to the volatile changes in the demands of the customers. Given the plethora of new technologies paired with numerous functionalities that have been integrated into manufacturing the last decades, it becomes apparent that the number of alternatives is increasing rapidly. Although searching for the optimum alternative can be proved strenuous, time-consuming and costly, state-of-the-art Information and Communications Technology (ICT) tools have enabled manufacturers to reduce development time, eliminate a significant part of the design and build cycles, as well as to address the need for more customer-oriented product variants [5]. In this context, a new trend regarding the efficient adaptation of modern manufacturing systems to external disruptions, is the so-called Resilient Manufacturing. Resilient Manufacturing is defined by the literature [6], as the ability of a manufacturing system to efficiently mitigate any external disruptions either derived from the supply chain of the company or resulted from the volatility of the market demand. Further to that, the response of the system to these volatile changes must be as rapid as possible in order for the company to maintain their competitive advantage in the market landscape.
Towards that end, simulation comprises a focal point of digital manufacturing solutions since it allows the experimentation and validation of different products, processes and manufacturing system configurations [7]. Consequently, with simulation modeling the decision-making process can be facilitated as it provides useful insight regarding the behavior of the system under various conditions, in order to gain full perception of the system response under different, usually unpredicted scenarios. The number of combinations due to different settings can be countless; aiming to reduce the simulation effort, the main factors that affect the system are identified. On the contrary, engineers are constantly seeking ways for mitigating disruptions in manufacturing systems as fast as possible. Therefore, in many cases detailed simulation models and techniques are adequate since the computational domain is large and requires big computational resources and time. Consequently, robust engineering is a suitable candidate, especially in the case of resilient manufacturing, as it was described in the previous paragraph. More specifically, Pahdke in his book [8], defines robust engineering as a method for studying large numbers of decision variables with the bare minimum number of experiments required. In order to accomplish this, orthogonal arrays are utilized.

2. State of the Art for Resilient Manufacturing System

Simulation modelling and analysis is conducted in order to gain insight into these complex systems, testing new operating or resource policies and new concepts or systems before implementing them, as well as to gather information and knowledge without disturbing the actual system [9]. Simulation is a very helpful and valuable ICT tool in modern manufacturing. It provides decision-makers and engineers with a secure and low cost tool that provides fast analysis for the investigation of the system complexity as well as for identifying possible changes regarding the system configuration. Further to that with simulation, the operational policies which may affect the performance of the system or organization can also be examined minimizing intrusion, and thus cause disturbances to the physical/actual system [10]. Simulation models are categorized into static, dynamic, continuous, discrete, deterministic, and stochastic. Static models comprise a system of equations, which represent the state of the system under investigation in a specific point in time [7]. If the relationships that compose the model are simple enough, it may be possible to use mathematical methods, such as algebra, calculus, or probability theory, to obtain exact information on questions of interest; this is called an analytic solution. However, most real-world systems are too complex to allow realistic models to be evaluated analytically, and these models must be studied with the utilization of suitable simulation models [11].
Τhe traditional role of simulation is to present a “smart” business with a significant competitive advantage during the development, deployment and implementation of its plans and strategies. Simulations are accomplished by virtualizing processes with the use of tools, testing virtual models prior to application in the real world and proving to help: (i) performance prediction, such as latency, utilization and bottlenecks, (ii) disclosure of how the various components of a system interact, (iii) experimenting with and evaluating the merits of alternative scenarios, (iv) providing a knowledge base of the system configuration, (v) serving as a valuable means of demonstration and as a consequence of the above and finally, to (vi) promote decision-making [12]. Discrete event simulation (DES) as a discrete sequence of events is a form of computer-based modeling of a system. Simulation has a number of advantages over other operational research (OR) techniques, including the ability to experiment with any aspect of a business system [13]. The term “discrete” refers to the simulation progressing through time at mutually exclusive intervals. A mechanism is required to track the evolution of time, taking into account the dynamic nature of the systems to be modelled. This is accomplished using a tool known as a “simulation clock,” which changes as events occur. As previously stated, DES models employ two prominent approaches as follows [11]: (i) Next Event Time Advances (NETA) and (ii) Fixed Increment Time Advances (FITA). Generally, NETA is more commonly used in simulation than FITA, because it is less complicated [12]. Finally, the effectiveness of DES and its flexibility result from its stochastic nature which makes it suitable for use in a wide range of applications, including warehouse operations, as a means of validating the performance of different indicators such as concerning material handling systems or order-picking and product location strategies [14]. Nonetheless, as computing processing power has increased, artificial intelligence (AI) has emerged, and IoT sensing has expanded in the modern warehouse, a new type of ”simulation” paradigm known as the ”Digital Twin” has emerged, allowing real-time control and digitization of a physical system [15][16][17].
Moreover, by modifying the production line for personalized production it is necessary to integrate Cyber Physical Production Systems to achieve the unique customer requirements and to achieve resilience [18]. Resilience is a quality that is related to robustness and prevents performance indicators from deteriorating. Under the resilient production control, unauthorized events that can cause bullwhip and ripple effects are managed. Five key functional requirements for handling events must be met by resilience: (1) selection of actions, (2) measurement of key performance indicators (KPI), (3) monitoring, (4) notification of fluctuations, and (5) adjustment. Resilience is achieved by meeting requirements 1, 2, and 5 in terms of production control [19].
This benefit perceived early from the researchers but also from the companies which in combination with the rapid evolution of computer systems beetled off exploiting the advanced decision-making assistant. The results of two simulation models to investigate the effects of push and pull systems on a printed circuit board manufacturing process at an electronics company in Ankara, Turkey presented in [20] while a diamond tool manufacturing system simulation is developed to predict the number of machines and the number of workers necessary to maintain desired levels of production by the same company [21]. As make-to-order (MTO) strategy is increasingly adopted by companies at the recent years, the management of the delivery dates of the orders have become a concern for researchers who attempted to address it by the means of simulation [22][23]. The complexity and the stability of manufacturing systems, introducing concepts based on DES and nonlinear dynamics theory also investigated by [24]. Through DES models with the use of multiple conflicting user-defined criteria has been also used by [25] for examining the evaluation of the performance of automotive manufacturing networks under highly diversified product demand. Last but not least, regarding the design of lean manufacturing systems through simulation, a case study of an organization following a job shop production system to manufacture doors and windows is presented in [26]. It is quite discernible that simulation is preferred in many instances since it provides insight to numerous problems of manufacturing systems.
In order to comprehend the system response to the change of a parameter state, several experiments must be executed and still the conclusion is not sure that it will be valid. Experiments though can be proved costly and time-consuming. DOE begins with determining the objectives of an experiment and selecting the factors for the study. Well-chosen experimental designs maximize the amount of information that can be obtained from a given amount of experimental effort [27]. Towards that end, matrix experiments are preferred, since they provide a set of experiments where the settings of the system that need to be examined are selected, they change into various levels and then the data of all experiments are collected and analyzed. Following the data analysis, meaningful inferences for the effect of each parameter can be determined. Taguchi design method is a fractional factorial design which uses an orthogonal array that can greatly reduce the number of experiments [28]. Despite the fact that methods for designing experiments, addressed to simulation, have been investigated a long time [29][30], it is observed that are still adopted the simulation models that describe present-day production lines in order to provide a prompt inspection of the system factors. A two-stage sequential approach to design experiments for studying simulation systems which have additional stochastic constraints, so the input factor space is restricted and irregularly shaped suggested by [31]. A multi-objective formulation of the buffer allocation problem in unreliable production lines presented in [32] where the factorial design has been used to build a meta-model for estimating production rate, based on a detailed, DES model. In addition to that, the relationship between various factors leading to output yield strength of rebar has been investigated by the authors in [33] as also the decision process of evaluating and selecting shop floor improvement solution by integrating DOE in a DES environment [34] undoubtedly stated the usefulness of Taguchi method and DOE philosophy into simulation models of real production lines.
Although many studies addressed the manufacturing design using simulation, there is a lack of publications that combine framework application with the real data for validation and further experimentation. The results of individual modules often contradict each other because they refer to indirectly related manufacturing information and context which hinders the applicability of tools to real life manufacturing systems and pilot cases beyond the ones initially studied [35].

References

  1. The World Bank. Available online: https://data.worldbank.org/indicatorNV.IND.MANF.ZS?end=2019&start=2004&view=chart (accessed on 27 February 2021).
  2. The World Bank. Available online: https://data.worldbank.org/indicator/NV.IND.MANF.CD (accessed on 27 February 2021).
  3. Mourtzis, D.; Siatras, V.; Angelopoulos, J. Real-Time Remote Maintenance Support Based on Augmented Reality (AR). Appl. Sci. 2020, 10, 1855.
  4. Mourtzis, D.; Vlachou, E.; Dimitrakopoulos, G.; Zogopoulos, V. Cyber-Physical Systems and Education 4.0—The Teaching Factory 4.0 Concept. Procedia Manuf. 2018, 23, 129–134.
  5. Chryssolouris, G. Manufacturing Systems: Theory and Practice, 2nd ed.; Springer: New York, NY, USA, 2006.
  6. Kusiak, A. Resilient manufacturing. J. Intell. Manuf. 2020, 31, 269.
  7. Mourtzis, D.; Papakostas, N.; Mavrikios, D.; Makris, S.; Alexopoulos, K. The role of simulation in digital manufacturing: Applications and outlook. Int. J. Comput. Integr. Manuf. 2018, 28, 3–24.
  8. Phadke, S.M. Quality Engineering Using Robust Design, 1st ed.; Prentice Hall: Upper Saddle River, NJ, USA, 1989.
  9. Mourtzis, D.; Doukas, M.; Bernidaki, D. Simulation in manufacturing: Review and challenges. Procedia CIRP 2014, 25, 213–229.
  10. Hosseinpour, F.; Hajihosseini, H. Importance of simulation in manufacturing. World Acad. Sci. Eng. Technol. Int. J. Econ. Manag. Eng. 2009, 51, 292–295.
  11. Law, A.M.; Kelton, W.D.; Kelton, W.D. Simulation Modeling and Analysis; McGraw-Hill: New York, NY, USA, 2009; Volume 3.
  12. Ferreira, W.; Armellini, F.; De Santa-Eulalia, L.A. Simulation in industry 4.0: A state-of-the-art review. Comput. Ind. Eng. 2020, 149, 106868.
  13. Tang, J.; Leu, G.; Abbass, H.A. Simulation and Computational Red Teaming for Problem Solving; John Wiley & Sons: Hoboken, NJ, USA, 2019.
  14. Agalianos, K.; Ponis, S.T.; Aretoulaki, E.; Plakas, G.; Efthymiou, O. Discrete Event Simulation and Digital Twins: Review and Challenges for Logistics. Procedia Manuf. 2020, 51, 1636–1641.
  15. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Equipment Design Optimization Based on Digital Twin Under the Framework of Zero-Defect Manufacturing. Procedia Comput. Sci. 2021, 180, 525–533.
  16. Mourtzis, D.; Angelopoulos, J.; Siatras, V. Cycle Time Estimation Model for Hybrid Assembly Stations Based on Digital Twin. In Advances in Production Management Systems, Proceedings of the The Path to Digital Transformation and Innovation of Production Management Systems. APMS 2020. IFIP Advances in Information and Communication Technology, Novi Sad, Serbia, 30 August–3 September 2020; Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D., Eds.; Springer: Cham, Switzerland, 2020; Volume 591.
  17. Papacharalampopoulos, A.; Giannoulis, C.; Stavropoulos, P.; Mourtzis, D. A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT. Appl. Sci. 2020, 10, 2377.
  18. Park, K.T.; Lee, J.; Kim, H.-J.; Noh, S.D. Digital-twin-based cyber physical production system architectural framework for personalized production. Int. J. Adv. Manuf. Technol. 2020, 106, 1787–1810.
  19. Ivanov, D. Structural Dynamics and Resilience in Supply Chain Risk Management; Springer International Publishing: Berlin/Heidelberg, Germany, 2018.
  20. Dengiz, B.; Akbay, K.S. Computer simulation of a PCB production line: Metamodeling approach. Int. J. Prod. Econ. 2000, 63, 195–205.
  21. Dengiz, B.; Bektas, T.; Ultanir, A.E. Simulation optimization based DSS application: A diamond tool production line in industry. Simul. Model. Pract. Theory 2019, 14, 296–312.
  22. Ebadian, M.; Rabbani, M.; Torabi, S.A.; Jolai, F. Hierarchical production planning and scheduling in make-to-order environments: Reaching short and reliable delivery dates. Int. J. Prod. Res. 2019, 47, 5761–5789.
  23. Li, M.; Yang, F.; Wan, H.; Fowler, J.W. Simulation-based experimental design and statistical modeling for lead time quotation. J. Manuf. Syst. 2015, 37, 362–374.
  24. Papakostas, N.; Efthymiou, K.; Mourtzis, D.; Chryssolouris, G. Modelling the complexity of manufacturing systems using nonlinear dynamics approaches. CIRP Ann. 2009, 58, 437–440.
  25. Mourtzis, D.; Doukas, M.; Psarommatis, F. Design and operation of manufacturing networks for mass customisation. CIRP Ann. Manuf. Technol. 2019, 62, 467–470.
  26. Gurumurthy, A.; Kodali, R. Design of lean manufacturing systems using value stream mapping with simulation: A case study. J. Manuf. Technol. Manag. 2019, 22, 444–473.
  27. NIST/SEMATECH, e-Handbook of Statistical Methods. Available online: http://www.itl.nist.gov/div898/handbook/ (accessed on 27 February 2021).
  28. Ezoddin, M.; Jönsson, J.Å.; Kyani, A. Equilibrium sampling through membrane based on a hollow fiber for determination of naproxen and diclofenac in sludge slurry using Taguchi orthogonal array experimental design. Desalination Water Treat. 2014, 52, 2472–2480.
  29. Kleijnen, J.P. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation; Wiley and Sons, Inc.: Hoboken, NJ, USA, 1998; pp. 173–223.
  30. Shang, J.S.; Li, S.; Tadikamalla, P. Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization. Int. J. Prod. Res. 2004, 42, 3823–3849.
  31. Mu, S.; Yin, J.; Yuan, J.; Ng, S.H. Design of experiments for simulation models with stochastic constraints. In Proceedings of the Industrial Engineering and Engineering Management, Hong Kong, China, 8–11 December 2009; pp. 2094–2098.
  32. Amiri, M.; Mohtashami, A. Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. Int. J. Adv. Manuf. Technol. 2012, 62, 371–383.
  33. Sastry, S.B.V.S.P.; Rao, V.K. Application of Generalized Taguchi and Design of Experiment Methodology for Rebar Production at an Integrated Steel Plant. World Academy of Science, Engineering and Technology. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 2016, 10, 1033–1041.
  34. Low, S.N.; Kamaruddin, S.; Azid, I.B.H.A. An integrated simulation with design on experiment approach for shop floor improvement solution selections. Eur. J. Ind. Eng. 2016, 10, 479–498.
  35. Mourtzis, D. Simulation in the design and operation of manufacturing systems: State of the art and new trends. Int. J. Prod. Res. 2020, 58, 1927–1949.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , ,
View Times: 373
Revisions: 2 times (View History)
Update Date: 09 Sep 2022
1000/1000
Video Production Service