Aim

Aim:
The Main aim of this literature review is to gain a better understanding, advantages, and limitation of Discrete event simulation in the domain of Supply chain, logistics, and transportation.
Methodology:
The articles for the literature review were selected by utilizing the Winter simulation conference database and from the digital library of Uppsala University. In this literature review three articles where used , Two articles from winter simulation conference Article 1:”Logistics Evaluation of An underground Mine using Simulation”, Article 2:”A Case Study For simulation and optimization Based planning of Production and Logistics System” and one Journal article from International journal of production research, Article 3:”Simulation modeling for food supply chain redesign ;integrated decision making on product quality, sustainability and Logistics”. The Major reason to select this articles out 70 hits during the selection process Since, I found interesting that how Discrete event simulation has helped in real time scenario in industries and how it has helped them to optimize in certain situations to get better understanding of Simulation in particular domain and selected articles also dealt with different industries with different issues.
Main findings:
In article 1, The scope of the study is about logistics evaluation of an underground mine using simulation, where four different type of layout options were proposed with different transportation strategies and to find the best transportation capacity to achieve the planned production with less tunnel traffic using discrete event simulation with help of Arena Simulation software. While modeling the mine a typical approach was used known as “Single-oriented approach” this approach focus on some particularities that should be addressed locally and situation for modeling in the mine case demanded it. The main aim of approach is to focus on signal intelligence to decide with the truck should be allowed first in-order to get maximum efficiency. A Boolean expression is also associated with each signal which helps to check which path is free and prioritize the path to avoid the traffic. A set of KPI model was used to validate the system and to compare between different scenario, also scheduled production and simulated production was compared to find the goal achievement. From the comparison between the scenarios, the major findings after simulation was the scenario 4 achieved the scheduled production with lowest TTC(Total Transportation Capacity).
In article 2, The scope of the study is about a food industry where an offline-coupled multilevel simulation approach is used to smooth production and logistics planning by optimization. and to configure the production system Discrete event simulation is used and to optimize the logistics network Agent-based simulation was used. In this study, each module receives the results of the previous module as its input. Module 1: Smoothing of production and logistics is done through optimization in-order to tackle the external fluctuation in the demand with amplifying effects in the supply chain (Bullwhip Effect).An optimization model was developed that is split into various phases based on forecast sales data. the developed model was implemented in Java eclipse neon the end results briefs that the capacity of the multistage production process is reduced by 40% compared to the unsmoothed production plan. Module 2: Configuring the Production and logistics system to increase the capacity of the new production plant in these module time lapse of a process is considered at certain points of time, determined by events. An approach was developed that had three stages of the production process along with it a simulation model was developed to optimally configure the key properties that define cost and performance. The proposed approach was implemented in the DES software and it is found that there is an additional reduction of capacity demand by 3% and also had a reduction of 20% transport distance. Module 3: Optimizing the logistics network where different network scenarios are designed and evaluated using an agent-based simulation of the logistics performance for each variant. An approach was developed that uses the scheduled production volume as the input and the start of production date are converted to corresponding supplier delivery date. The two scenarios were proposed based on the geological location of both supplier and customer is used as input for the simulation module in both the case the suppliers and customers are identical. The results suggest that scenario B with centralized production achieves high performance according to the key indicators.
In article 3, The scope of the study is towards food quality change models and sustainability indicators with help of simulation software ALADIN. The journal also briefs about the redesigning of FSC(Food supply chain )that leads to several strategic, tactical and operational strategies in order to improve efficiency and effectiveness. A case was discussed that gives a brief picture of an integrated analysis of alternative FSC designs Pineapple transportation, Scenario 1 transportation of pineapple through air chain and the second scenario about transportation through sea chain. The main challenge was to maintain the quality of the fruit during the transportation along the supply chain and till retail outlet. The two scenarios are compared on key indicators like logistics cost, product quality decay, energy use, and CO2 emissions. In order to analyze the two scenario data are collected using document analysis and expert interview that includes all distribution activity from harvest to retail outlet. The simulation was done by modeling the supply chain using some building blocks and designed the scenario by setting model elements. The major finding, from cost and sustainability perspective sea chain, provides the best output on the contrary product quality was better in air chain. It was found in scenario 1 that air transport was responsible for 70% of all logistics cost, energy use happens mostly during transport 85 %, and keepability is between 3.5 days to 5.5 days. On the flip side Scenario B, it was sea transport was responsible for 600% of all logistics cost, energy use happens mostly during transport 50 %, and keepability is between from less than 3.5 days to more than 4.5 days.
Reflection:
From the above findings each articles use discrete event simulation and optimized the system, by analyzing the previous system or new system , even though they use discrete event simulation in article 1 they incorporate Single-oriented approach, this approach focus on some particularities that should be addressed locally and situation for modeling. Since, the mine case demanded it. Thus, it gives a brief picture that while incorporating in a real-time scenario company doesn’t rely on simulation methods but also they use specific methodology if it is demanded or to increase the efficiency and effectiveness from the simulation software. Also, assumptions are made during analyzing the data and even the data collection are made based on forecast method of sales rate in article 2 which will affects the end results of the System. Thus, it makes sure that precise results cannot be obtained through simulation if there is no proper data collection.

Conclusion:
From the above findings, it is clear that each case use simulation software to optimize, if there are any bottlenecks in their system also to compare different scenario and to find out which scenario has most efficiency and effectiveness. However, the methodology of conducting simulation varies from firm to firm depending on the situations and the goal of the end result. Also, there is a certain limitation on framing the data for analyzing which can’t give efficient results for the system. The above study also briefs the results obtained through Simulation technology cannot be applied directly in the system, firms do consider other external factors that might influence the obtained results.

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