This paper discusses the incorporation of discrete event simulation technology in supply chain management to enhance efficiency and productivity. It provides case studies and outlines the key stages involved in simulation projects.
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1 Running Head: SUPPLY CHAIN MODELING AND SIMULATION MODULE Supply Chain Modeling and Simulation Module Name Institution
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2 SUPPLY CHAIN MODELING AND SIMULATION MODULE Introduction Automotive manufacturing and logistics is based on designing, planning and controlling the flow of material and related information flows in relation to their supply chain. This calls for strategic, tactical and operational task ranging from simple equipment all the way to international supply chain thus calling for relating different entities to come up with a system. This paper is going to give way that can be used to incorporate discrete event simulation . technology to perform such complex tasks. This report is intended to address the request placed by the line manager of Tier-1 automotive supplier. The paper will dwell majorly on how the company can incorporate strategic use of discrete-event simulation techniques in the business to enhance supply chain. Over the years, there has been rapid development in the technological sector, which greatly influences the business supply chain and productivity. Supply chain management is one of most critical areas in any organization thus requires to be handled with precaution in order to realize the goals and target of the organization. In this case, the paper tends to come up with recommendation that can be used by the firm to utilize this technology as discussed below This paper is going to focus on discrete supply chain by giving three case studies in which this technique has been used in addition to decoding the stages necessary in the simulation project. Consequently, the focus of this report is to give the way forward that can be used by Tier-1 automotive Supplier to facilitate its operation since it has never used this kind of technology. Due to the large amount of movements of supplies taking place in the firm, the firm wishes to integrate discrete-event simulation to monitor the flow of goods in the supply chain. The active streamlining of activities to transform raw materials into products thus call for the
3 SUPPLY CHAIN MODELING AND SIMULATION MODULE utilization of this technology to reduce make management and monitoring much easier. The reason for the use of this strategy is to increase competitive advantage in the market place Discrete –event simulation With reference to this kind of technology, it is used to simulate components that operate at the highest level of abstraction than components simulated by continuous simulators on a normal basis. DES is a computer-based modeling technique, which gives intuitive and less rigid method with the ability to produce dynamic behavior of sophisticated systems and interaction between characters or objects (Schmidtke, Heise & Hinrichsen 2014).. Within the same context of discrete event simulation, an event is considered an incident responsible for causing changes in some way in a given system. For instance, creation of a new event is as a result of simulation component generates output. According to Prinz, Väätäinen, Laitila, Sikanen & Asikainen (2019), the distinct feature of discrete simulator is that events here can only occur during a distinct unit of time in the process of simulation. In this case, events cannot occur in between time unit. This kind of simulation is usually faster and has the ability of producing accurate approximation of system behavior. According to Hoad & Kunc (2018), discrete event simulation can also be used to refer to the codifying process of the behavior of complex or sophisticated systems as an ordered sequence of events that are well defined. This event means changes that are specific in the state of a system at specified point time. It is the method of simulating the behavior and performance of facilities or life processes. In order to use this kind of technology in the supply chain management, it requires the utilization of the high speed of computer and memory. Consider the example presented below.
4 SUPPLY CHAIN MODELING AND SIMULATION MODULE Fig1.0 shows simple digital logic circuit. Fig 1.2 shows graphical representation of discrete behavior. Based on the assumption that NAND gate delays with two time unit and the NOT gate has a delay of one time unit, the logic circuit tends to produce the output given in figure 1.2
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5 SUPPLY CHAIN MODELING AND SIMULATION MODULE As asserted by Steinemann, Taiber, Fadel, Wegener & Kunz (2013), DES functions by modeling systems as series of events. Discrete event simulation holds on the assumption that there are no changes in the system between events. In this, characters or objects/patients are modelled as independent entities through which each entity is assigned associated attribute information. The information can be modified as time runs in the given Simulation. With changes in the information, location of event changes depending on the status of the unit hence modification of care given out by the system (Padhi, Wagner, Niranjan & Aggarwal 2013). DES models are in apposition of allowing complex decision logic to be integrated and incorporated in systems, which is not the case with other types of models. The most important aspect of discrete event simulators is their ability to allow scenarios to be tested before being implemented. This is important to decision makers as it gives them room to understand the alternative methods through which a new policy or production may best be met (Navarra, Marambio, Oyarzún, Parra, & Mucciardi 2017).. Case study of the use of discrete event simulation Discrete event simulation for over the last decades has taken over many different areas of application. This is due to the wider use of this technology in different field fields of science and the continuous use of available software programs by experts who are dedicated in this field. To begin with, DES has in the resent years been used the health sector and medicine to help health practitioners provide quality services to patient and manage diseases (Moghaddam, Sadrnia, Aghel & Bannayan 2018). Simulations have been applied to projects in hospitals that can be put into five categories namely, surgeries, intensive care units (ICU) hospitals, specialties, outpatient clinics and demographic health provision in addition to healthcare supply chain environment. The main
6 SUPPLY CHAIN MODELING AND SIMULATION MODULE component of hospital activities are always surgical procedures that are applied on patients in either ICU or being released to the recovery area (Prajapat & Tiwari 2017). Discrete event simulation technology has been utilized in combination with other solutions to create optimal environment in the surgical procedure and use of ICU. Instance, DES technology is being used in the ICU to monitor neonates and babies born before the right period. Mixed integer programing has been used to optimize the utilization of operating theatres .it has helped streamline surgery sequences efficiently without disruption, which has been achieved through balancing the scheduled blocks. This model has been used find the best utilization in surgical procedures (Dunke & Nickel 2017). Consequently, simulation models for determining the capacity of bed planning in the ICU are being utilized. ICUs are using multiple server model that help to identify bottlenecks in addition to improving the flow of patients and communication between surgeons with their administrative staff Case study 2 vehicle manufacturing As denoted by Kotiadis & Tako (2018),Furthermore, DES software has been of continuous use industry, the software has been used to breakdown each process in the manufacturing of vehicle bodies into discrete parts. Vehicle manufacturing companies use this technology in order to make it easier to analyze and give room for more factors to be considered when modeling. The ability of DES to approximate continuous processes into defined non- continuous event has enabled car manufactures to model he movement of a car part from assembling until it reaches the paint shop as independently. It functions based on departure and arrival time (Yong-Kuk, Philippe & Jong 2018).
7 SUPPLY CHAIN MODELING AND SIMULATION MODULE Consequently, vehicle-manufacturing companies are using DES technology to mold complete vehicle bodies due to its powerful and accurate approximation of events (Johansson, Skoogh, Andersson, Ahlberg & Hanson 2015). The entire vehicle manufacturing facility has been modeled inform of sequence of operations being performed based on passive entities. The attributes of the components passing through the processing sequence makes them to be handled differently. Bokrantz, Skoogh, Lämkull, Hanna, , & Perera, (2018), denote that the use of the DES in the vehicle manufacturing industry makes it easier in the production process because; DES has the ability of predicting the results of the system being used over time; it also allows the operators know how various parts of the system interact. Furthermore, vehicle companies like TOYOTA, BMW and MISTUBISHI utilize the technology to help produced different models of the vehicles (Ghani, Monfared & Harrison 2015. Case study 3 use of DES in learning institutions Universities, high schools and other institutions of learning have incorporated discrete event simulation technologies in their schools to help create a conducive environment for learning. DES soft wares are currently being used in schools to determine the student flow in academic study periods (Bokrantz, Skoogh, Lämkull, Hanna & Perera 2018). In the event of seeking optimal completion time of career and providing efficient services, DES programs are being used. They have been used to model complex flow scenarios for each career in institutions of higher learning to predict possible outcomes if the flow rules and polices of admission criteria changes. Taking into consideration the number of students to be enrolled, it becomes possible to estimate the amount of resources (teachers, sections, laboratories and councilors) to meet the demand. Student programing is thus used to estimate and allocate sufficient resources to reduce
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8 SUPPLY CHAIN MODELING AND SIMULATION MODULE bottleneck thus improving exit time for students in different academic career (Pedrielli, Matta, Alfieri & Mengyi 2018). Key stages involved in the simulation projects Simulation application involves a set of specific steps so that the simulation may emerge out successfully. The key steps to be undertaken in simulation projects are; Problem definition This step involves determining the goals of the study and defining what is to be solved by the simulation. In order to understand the problem, further definition involving objective observations of the process have to be studied. During this stage, it is important to determine whether simulation is the best solution for the problem (Dode, Greig, Zolfaghari & Neumann 2016). Project planning At this step, the project tasks are broken down into packages of work and assigned to a responsible party.it is necessary to indicate the milestones used for tacking progress. This is necessary to determine whether time and resources will allow the accomplishment of the project. ï‚·System definition-The main task of this step is the identification of system components that are to be modelled and the measures of performance to be analyzed. The system is usually complex thus to define it calls for experienced stimulator to find appropriate levels of details and flexibility.
9 SUPPLY CHAIN MODELING AND SIMULATION MODULE ï‚·Model formulation- According to Bokrantz et al. (2018) this call for clear understanding of how the actual system will function and determining the basic requirements of the model that are necessary. ï‚·Input data collection and analysis-The end of module formulation results to determination of the type of data to be collected. This data is then fitted to theoretical distribution. Consequently, the model is transplanted into programing language. ï‚·Verification and analysis-This is the process of ensuring the model works as it was planned. It is done through debugging or animation. The information can be verified but not validated. Validation can only be achieved through statistical analysis. ï‚·Experimentation and analysis-This is the second last step, which involves experimenting and developing the alternative models, executing the simulation and comparing it statistically with of real system. ï‚·Documentation and analysis- It is made up of written reports or presentation. The report usually discusses the results and implications of the study. Thus, the best course of action is identified. Recommended and justified. Fig 1.3 shows a sample representation of discrete event simulation model that can be used in the supply chain management.
10 SUPPLY CHAIN MODELING AND SIMULATION MODULE The model above can be used to give an overview of how incorporating DES program can facilitate easy management of unrelated tasks in an organization ranging from acquisition of raw materials to supply of finished products. Conclusion and Recommendations Following the nature of the Tier-1 automotive supply firm which involves complex and variegated manufacturing contexts, with supply chains that are large and complex, it is recommended for the firm to use DES software to help monitored the process of production since it provides accurate approximation of events. Consequently, we do recommend that before resolving into the use of simulation in the firm, the firm should conduct careful research to determine the data and information needed in order to build a clear model of simulation. This should only be done by experienced simulators to identify areas of flexibility. To sum up, discrete event simulation technology is one of the most growing field especially when it comes to application due to its accurate approximation of events thus resulting quality products. This idea can be incorporated in various areas that need accuracy to increase competitive advantage of the firm.
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11 SUPPLY CHAIN MODELING AND SIMULATION MODULE References Bokrantz, J., Skoogh, A., Lämkull, D., Hanna, A., & Perera, T. (2018). Data quality problems in discrete event simulation of manufacturing operations.Simulation,94(11), 1009–1025. Dode, P. (Pete), Greig, M., Zolfaghari, S., & Neumann, W. P. (2016). Integrating human factors into discrete event simulation: a proactive approach to simultaneously design for system performance and employees’ well being.International Journal of Production Research,54(10), 3105–3117. Dunke, F., & Nickel, S. (2017). Evaluating the quality of online optimization algorithms by discrete event simulation.Central European Journal of Operations Research,25(4), 831–858. Ghani, U., Monfared, R., & Harrison, R. (2015). Integration approach to virtual-driven discrete event simulation for manufacturing systems.International Journal of Computer Integrated Manufacturing,28(8), 844–860. Hoad, K., & Kunc, M. (2018). Teaching system dynamics and discrete event simulation together: a case study.Journal of the Operational Research Society,69(4), 517–527. Johansson, B., Skoogh, A., Andersson, J., Ahlberg, K., & Hanson, L. (2015). Power-level sampling of metal cutting machines for data representation in discrete event simulation.International Journal of Production Research,53(23), 7060–7070. Kotiadis, K., & Tako, A. A. (2018). Facilitated post-model coding in discrete event simulation (DES): A case study in healthcare.European Journal of Operational Research,266(3), 1120–1133.
12 SUPPLY CHAIN MODELING AND SIMULATION MODULE Moghaddam, A. K., Sadrnia, H., Aghel, H., & Bannayan, M. (2018). Optimization of tillage and sowing operations using discrete event simulation.Research in Agricultural Engineering,64(4), 187–194. Navarra, A., Marambio, H., Oyarzún, F., Parra, R., & Mucciardi, F. (2017). System dynamics and discrete event simulation of copper smelters.Minerals & Metallurgical Processing,34(2), 96–106. Padhi, S., Wagner, S., Niranjan, T., & Aggarwal, V. (2013). A simulation-based methodology to analyse production line disruptions.International Journal of Production Research,51(6), 1885–1897. Pedrielli, G., Matta, A., Alfieri, A., & Mengyi Zhang. (2018). Design and control of manufacturing systems: a discrete event optimisation methodology.International Journal of Production Research,56(1/2), 543–564. Prajapat, N., & Tiwari, A. (2017). A review of assembly optimisation applications using discrete event simulation.International Journal of Computer Integrated Manufacturing,30(2/3), 215–228. Prinz, R., Väätäinen, K., Laitila, J., Sikanen, L., & Asikainen, A. (2019). Analysis of energy efficiency of forest chip supply systems using discrete-event simulation.Applied Energy,235, 1369–1380. Schmidtke, D., Heiser, U., & Hinrichsen, O. (2014). A simulation-enhanced value stream mapping approach for optimisation of complex production environments.International Journal of Production Research,52(20), 6146–6160.
13 SUPPLY CHAIN MODELING AND SIMULATION MODULE Steinemann, A., Taiber, J., Fadel, G., Wegener, K., & Kunz, A. (2013). Adapting discrete-event simulation tools to support tactical forecasting in the automotive industry.CoDesign,9(3), 159–177. Yong-Kuk Jeong, Philippe Lee, & Jong Hun Woo. (2018). Shipyard Block Logistics Simulation Using Process-centric Discrete Event Simulation Method.Journal of Ship Production & Design,34(2), 168–179.