Discrete Event Simulation Model Research Articles

An alternative solution to congestion relief of U.S. seaports by container-on-barge: A simulation study

Port congestion has become a big challenge for many seaports in the United States (U.S.). There are multiple factors contributing to port congestion, such as the booming market of international container trading, limited space and capacity at a seaport, labor shortages, equipment failures, and supply chain disruptions (e.g., COVID-19 pandemic). Without appropriate management, port congestion will increase transportation costs, cause delays in goods movement and cargo delivery, reduce port operation efficiency, and lead to supply chain disruptions. Traditional strategies, such as infrastructure investment, truck appointments optimization, and party cooperation enhancement, have been implemented by ports for years, but the results are often unsatisfactory. To learn the needs of U.S. seaports and seek an efficient method for congestion relief, we develop a discrete event simulation model incorporating Container-on-Barge (COB) as an alternative transportation mode in this study. The simulation model mimics port operations involving COB and investigates congestion times at berth, yard, and gates. A case study on the Port of New Orleans (Port NOLA) is conducted to demonstrate the implementation of the simulation model and to evaluate the potential of COB for seaport congestion relief. The simulation results suggest that with a good level of COB development, the congestion at berth, yard and entrances of a seaport can be decreased significantly.

Embedding extreme events to mine project planning: Implications on cost, time, and disclosure standards

Extreme events, characterized as infrequent and catastrophic occurrences, can present significant challenges in various industries. These events can be associated with earthquakes, landslides, hurricanes, floods, rainfalls, and wildfires. The mining industry is also highly vulnerable to extreme events because it threats operation sustainability, employees' safety, and environmental compliance. This paper presents a comprehensive analysis of earthquakes in a mining area and evaluates their impacts on a mining operation through a case study based on a mine construction project. To this end, Extreme Value Theory (EVT) using both the Block Maxima (BM) and Peaks Over Threshold (POT) methods was employed to analyze the historical earthquake data and estimate the return levels corresponding to the planned mine project completion time. Furthermore, a Discrete Event Simulation (DES) model was developed to perform a comprehensive risk assessment, allowing for a detailed examination of the potential consequences that a specified magnitude earthquake could have on the project's cost and duration. 1000 earthquake scenarios for the period covering the project life were generated for a thorough risk assessment. The case study focused on a hypothetical open pit mine construction project located in the province of British Columbia, Canada. Historical earthquake data specific to the selected region were analyzed, and the potential consequences of earthquakes on the project were evaluated. According to the results, the return level of 4.7 ML earthquake was obtained corresponding to the return period defined as the planned project completion time. In the event of an earthquake with this specified magnitude, simulation results revealed that there is a 95% probability that the project will experience a delay of at least 6.28% of the project completion time, ultimately leading to project costs exceeding the normal project cost by a minimum of 4.85%. The study highlights the importance of considering extreme natural events, such as earthquakes, in mining project planning and risk mitigation. The results provide valuable insights into the likelihood and potential consequences of earthquakes, contributing to better risk assessment, strategic planning, informed decision-making, and improved project management practices in the mining industry.

Discrete-Event Simulation Model for Monitoring Elderly and Patient’s Smart Beds

In this paper, we explore the development of a simulation model to assist in the decision-making process for monitoring the health conditions of elderly patients using data from their smart beds. The main objective of the proposed simulation model is to estimate the transitional condition of the health system by tracking patients’ physiological signals via a growth model. This approach allows the model to mimic the dynamics of various health-related issues faced by patients and the elderly. When combined with real-world data and practical expertise, the discrete-event simulation model can become a valuable tool for improving patient health and streamlining medical treatment processes. The proposed simulation model incorporates input from subject-matter experts and utilises statistical analysis to fit essential parameters based on patient data obtained from smart beds. However, authors should consider rephrasing this section to improve clarity and coherence.

Simulation and optimization of dynamic-hybrid parking reservation strategies for one-way vehicle-sharing systems

ABSTRACT In this paper, we explore the regulation of one-way station-based vehicle-sharing system (OSVS) through dynamic-hybrid parking reservation policies. We first propose a dynamic-hybrid parking reservation policy. This policy only requires trips with expected travel distances shorter than a specific threshold to make a parking reservation. The distance threshold varies with time. We develop a discrete event simulation model based on the O2DES (object-oriented discrete event simulation) framework to compare the dynamic-hybrid parking reservation (DHPR) strategy with the no-reservation (NR), static-hybrid parking reservation (SHPR) and complete parking reservation (CPR) strategies. Furthermore, we propose a simulation-optimization model and an Elitism-based Genetic algorithm with the optimal computation budget allocation to determine the fleet size, station capacity, and dynamic reservation distance threshold. The analysis of case studies of a real-world system indicates that DHPR is always superior to NR, SHPR and CPR.

A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

The difficulty that hospital management has been experiencing over the past decade in balancing demand and capacity needs is unprecedented in the United Kingdom. Due to a shortage of capacity, hospitals cannot treat all patients. We developed a whole hospital-level decision support system to assess and respond to the needs of local populations. We integrated a comparative forecasting approach and discrete event simulation modelling using Hospital Episode Statistics and local datasets. It is clear from the literature that this level of whole hospital simulation model has never been developed before (an innovative decision support system). First, the demands of all hospital specialties were forecasted, and the forecasts were embedded into the simulation model as input. Secondly, a simulation model was developed to capture the patient pathway of all specialties. The model integrates every component of a hospital to aid with efficient and effective use of scarce resources (e.g., staff and beds). As a result, the hospital can meet the increasing demand with its current resources. According to the scenario analysis, the hospital bed occupancy rate will reach the national target (i.e., 85%), and the total hospital revenue will increase by approximately 13%, with a 10% increase in A&E and outpatient and a 20% increase in inpatient demand. In conclusion, the hospital-level simulation model can become a crucial instrument for decision-makers to provide an efficient service for hospitals in England and other parts of the world.

A comparison of financial and operational efficiency between smart and traditional insufflation for laparoscopic surgery: A granular analysis

ObjectiveSmart insufflation (SI) techniques relying on valve and membrane-free insufflation are increasing in usage. Although considerable literature exists demonstrating the benefits of SI on procedural ease and patient outcomes, there remains a paucity describing the financial impact of these devices. The purpose of this study was to determine the financial and efficiency impact of these devices on the operating room and inpatient wards of a hospital. MethodsA discrete event simulation model representing a typical mid-sized North American hospital comparing SI to traditional insufflation (TI) was generated. The National Surgical Quality Improvement database from 2015 to 2019 was used to populate the model with data supplemented from the literature. Outcomes included length of stay (LOS), duration of surgery (DOS), annual procedure volume, profit, return on investment (ROI), and gross profit margin (GPM). From the literature review, DOS savings were 10–32 minutes/case, while LOS savings were 0–3 days/case. ResultsImplementation of an SI led to an increase in annual throughput of 42–346 (4.4%–36.6%) cases for all procedures and 38 to 297 (4.3%–33.3%) cases for complex procedures. LOS was found to be decreased by 175–614 (18.3%–64.2%) days for all procedures and 231 to 614 (35.6%–77.9%) cases for complex procedures with the implementation of an SI. Together, this resulted in an increase in net profit of $104,685 per annum. The ROI of SI over the TI device was >1000%, and the GPM for the TI was 90.0%, while the GPM for the SI was 71.7%. ConclusionDespite the initial financial investment being greater, the implementation of SI offsets these expenses and yields significant financial benefits. Our study demonstrates the financial benefits of SI over TI and illustrates how granular operational and financial analyses of technologies are essential to aid in sound healthcare procurement decision making.

Enhancement the Performance of Multi-Level and Multi-Commodity in Supply Chain: A Simulation Approach

In today's global economy, effective supply chain management plays a pivotal role in the success of a business. The repercussions of a business strategy on the entire supply chain remain uncertain until it is implemented. Utilizing simulations offers the opportunity to gauge performance before implementing the strategy. The primary aim of employing supply chain simulation is to analyze the effects of different strategies on profit enhancement and cost reduction across all supply chain tiers. This research paper has formulated a discrete event simulation model using Arena software to evaluate and enhance the operational efficiency of the detergent supply chain. The problem involves multiple levels and commodities, encompassing four manufacturers, two intermediate storage warehouses, and four main distributors following an (s, S) inventory control approach. Shortages are permitted, leading to a partial loss or back-ordering of products. The overarching objective is to minimize the overall inventory costs within the system, accounting for holding costs at each tier, managing shortages, and the expense incurred due to lost sales. A range of scenarios are developed to set control parameters, with the evaluation of supply chain performance falling into two main categories: financial and operational considerations.

A Simulation Study for a Safe Reopening and Operation of the Trager Institute Optimal Aging Clinic During the COVID-19 Pandemic

In this study, we develop a discrete-event simulation model to aid the Trager Institute, an outpatient clinic for optimal aging located in Louisville, Kentucky, in determining their safe reopening strategies during the COVID-19 pandemic and operational strategies beyond the pandemic. The model studies the movement of several groups of people (e.g., healthcare providers, navigators, patients, staff) and the operations of the clinic’s primary and ancillary services. The main objective is to ensure that the clinic operates safely while COVID-19 restrictions are in place and to improve its providers’ utilization rate. The model simulates people’s movement in the clinic, monitors the congestion level in four common areas, and identifies the peak hours during a day. We also study various overbooking and telehealth policies to overcome high cancelation or no-show rates and low utilization for providers. Simulation results using AnyLogic have helped the management decide to reopen the in-person services during the COVID-19 pandemic based on the safe congestion level demonstrated by the simulation. Insights on optimal overbooking and telehealth policies can shed a broader light on other healthcare organizations. History: This paper was refereed.

Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks

Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput.

The emergency hierarchical medical system in China: Improving patient waiting times and operation management

ABSTRACT The emergency hierarchical medical system, a multi-hospital collaborative approach, is one of the innovative healthcare management modes in China during the COVID-19 epidemic. This study aims to prove the waiting cost-saving effectiveness of the emergency hierarchical medical system. Two discrete event simulation models based on this system and traditional admission system will be constructed, and the M/M/1 queue structure with outpatient phase and hospitalization phase will be used to calculate the waiting time of those two systems. Furthermore, we do sensitivity analyses of hospital level, in-hospital disease cross-infection, and resource allocation to get the least costly strategy and to provide operational guidance. In the result, based on the regional virtual hospital structure constructed, the emergency hierarchical medical system outperforms the traditional system when the number of infection patients exceeds a threshold. Besides, the rate of in-hospital cross-infection does not affect the cost-saving effectiveness of system, and the centralized hospital should be set considering the hospital service ability. Moreover, given limited medical resources during the epidemic, the resource allocation between hospitals and patients can be optimized to improve the system’s cost-saving.

Discrete Event Simulation-Based Risk Analysis for Efficient, Sustainable and Resilient Transportation


 
 
 In a resource-constrained world the avoidance of transport-related emissions is crucial to fulfill the sustainable development goals, the Paris agreement on climate change and the Aichi biodiversity targets. Transport simulation enables the development of transport fleet strategies in risk scenarios to further improve efficientcy, sustainability and resilience of supply chains. Discrete event simulation is a valuable methodology for transport simulation, because it focuses on business processes for a digital representation of supply chains and provides an intuitive approach for facilitating stakeholder participation. Consequently, discrete event simulation models for unimodal, multimodal and multi-echelon unimodal wood transport are presented, which enable multicriteria-based strategy development, optimal fleet configuration as well as risk management and wood quality preservation. Furthermore, the verificated and validated discrete event simulation models were used in game-based stakeholder workshops to establish credibility, provide hands- on decision support and facilitate knowledge transfer between science, industry and education. A previous version of this revised and extended journal article was published as conference paper in the Proceedings of the XIVth Junior Forensic Science Conference (JuFoS), 18–19 May 2023 in Brno.
 
 

Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer

As the world is witnessing unprecedented events such as the COVID-19 pandemic, we live in a volatile, uncertain, complex, ambiguity (VUCA) world. Where volatility in supplies, Uncertainty in demand, Complexity in getting the products, and Ambiguity in understanding the issues. Such a scenario constitutes a VUCA world, and inventory positioning is no exception. Inventory positioning manages the safety stock across echelons to maintain customer service levels undersupply or demand uncertainties. Therefore, this article focuses on optimizing the inventory levels in demand uncertainty and supply complexity through inventory positioning and making reliable forecasts using machine learning for biomedical equipment, especially knee implants. The product flow is mapped through a discrete event simulation model by considering a biechelon supply chain. The parameters like reorder point, order quantity, supply lead time, and inventory costs are considered, and Arena modelled and simulated inventory replenishment. They are optimized with in-built OptQuest to minimize back orders and total costs. The model determines the safety stock inventories positioned at both echelons to achieve service level constraints. The uncertainty in demand is the root cause of the abovementioned issues and may be reduced through more reliable forecasts.

A bi-objective decision support tool based on system dynamics and discrete event modelling for sustainable supply chain

Based on the development trend of sustainable concepts and the implement ability of composites combined with Agave Bagasse Fibre (ABF) and Polyethylene Terephthalate (PET). The purpose of this paper is to design a bi-objective decision support tool for supply chain of composite material which combined with ABF and PET. Through the production, processing, recycling, and reprocessing of composite materials, the sustainable supply chain model of four different schemes is designed, and the data results of each scheme model are calculated and analysed. The tool can support supply chain modelling solutions that seek best practices for sustainable supply chains and optimize resource efficiency through cost and carbon dioxide emissions. The sustainable supply chain model was designed, created, and optimized in AnyLogic software using System Dynamics and Discrete Event Simulation modelling methods based on the supply chain model established by previous researchers. According to the analysis results of the model data, the reasonable design of the whole process can effectively reduce the cost and carbon dioxide emissions and achieve the effectiveness and implementation of the sustainable supply chain. The results of this study will provide reference for more sustainable supply chain models in the future. Further research on composite materials can be carried out by combining with practice.

MODELING TRAFFIC OPERATIONS AT A TOLL PLAZA USING DISCRETE-EVENT SIMULATION

The objective of this study is to develop a discrete-event simulation model of traffic operations at a toll plaza to compare the performance measures between the manual and electronic toll collection (ETC) systems. This study also attempts to simulate the scenarios where the number of servers in these systems is reallocated to improve the overall operational performance. The field data were collected at a toll plaza on an expressway on-ramp during a PM peak period via video recording. The collected data were summarized to determine the characteristics of parameters as inputs for the simulation model. The simulation model was developed in EZStrobe, while the results were carried out via STROBOSCOPE. The simulation model was calibrated with the field data prior to making modifications and simulating various scenarios. The results show that, under the existing conditions, switching an electronic tollgate to a manual tollgate reduces the average overall queue delay and queue length by 71% and 78%, respectively. After reallocation, the average queue delays in these systems equalize as the proportion of ETC users reaches 72%. The additional throughput time of 0.4 s/veh in the ETC system is small compensation for the 6.8-second deduction in the average overall throughput time. In conclusion, shortening the queue lengths at the toll plaza by efficiently utilizing the existing tollgates is possible for improving the overall operational performance of the toll plaza without the additional tollgate installation.

Multi-criteria scheduling of realistic flexible job shop: a novel approach for integrating simulation modelling and multi-criteria decision making

Increased flexibility in job shops leads to more complexity in decision-making for shop floor engineers. Partial Flexible Job Shop Scheduling (PFJSS) is a subset of Job shop problems and has substantial application in the real world. Priority Dispatching Rules (PDRs) are simple and easy to implement for making quick decisions in real-time. The present study proposes a novel method of integrating Multi-Criteria Decision Making (MCDM) methods and the Discrete Event Simulation (DES) Model to define job priorities in large-scale problems involving multiple criteria. DES approach is employed to model the PFJSS to evaluate Makespan, Flow Time, and Tardiness-based measures considering static and dynamic job arrivals. The proposed approach is implemented in a benchmark problem and large-scale PFJSS. The integration of MCDM methods and simulation models offers the flexibility to choose the parameters that need to govern the ranking of jobs. The solution given by the proposed methods is tested with the best-performing Composite Dispatching Rules (CDR), combining several PDR, which are available in the literature. Proposed MCDM approaches perform well for Makespan, Flow Time, and Tardiness-based measures for large-scale real-world problems. The proposed methodology integrated with the DES model is easy to implement in a real-time shop floor environment.

Highly Purified Human Menopausal Gonadotropin (HP-hMG) Versus Recombinant Follicle-Stimulating Hormone (rFSH) for Controlled Ovarian Stimulation in USPredicted High-Responder Patients: A Cost-Comparison Analysis.

Infertility imposes considerable clinical and economic burden, and the high costs of fertility care are a major barrier to payers. This study assessed the cost differences of highly purified human menopausal gonadotropin (HP-hMG) versus recombinant follicle stimulating hormone (rFSH) for controlled ovarian stimulation (COS) protocols in predicted high-responders from the US payer perspective. A discrete event simulation model was built to perform a cost-comparison analysis of HP-hMG versus rFSH in a cohort of predicted high-responders undergoing up to three embryo transfer cycles, informed by efficacy data from the MEGASET-HR trial. The model considered an event-based treatment pathway and transition probabilities were derived from MEGASET-HR. A variable time horizon was employed, and deterministic and probabilistic sensitivity analyses conducted. Subjects undergoing COS with HP-hMG and rFSH demonstrated comparable live birth rates following three in vitro fertilization (IVF) cycles, with 161 live births with HP-hMG and 152 live births with rFSH, per 310 high-responders. The total cost saving per live birth in subjects receiving HP-hMG versus rFSH was US$3024. These cost savings were largely driven by the need for fewer embryo transfers to achieve similar efficacy outcomes and a reduced rate of ovarian hyperstimulation syndrome. Following deterministic sensitivity analysis, HP-hMG remained cost saving in all baseline parameter variations. No parameters led to rFSH providing cost savings when compared with HP-hMG. Comparable clinical outcomes can be achieved at a lower cost when using HP-hMG versus rFSH based COS protocols in a cohort of predicted high-responders. Such cost savings may reduce the economic burden infertility currently presents to US healthcare providers and those seeking fertility care.

Cost-Effectiveness of Hydroxyurea for Sickle Cell Anemia in a Low-Income African Setting: A Model-Based Evaluation of Two Dosing Regimens.

The disease burden of sickle cell anemia (SCA) in sub-Saharan African (SSA) countries is substantial, with many children dying without an established diagnosis or proper treatment. The global burden of SCA is increasing each year, making therapeutic intervention a high priority. Hydroxyurea is the only disease-modifying therapy with proven feasibility and efficacy suitable for SSA; however, no one has quantified the health economic implications of its use. Therefore, from the perspective of the health care provider, we estimated the incremental cost-effectiveness of hydroxyurea as a fixed-dose regimen or maximumtolerated dose (MTD) regimen, versus SCA care without hydroxyurea. We estimated the cost of providing outpatient treatment at a pediatric sickle cell clinic in Kampala, Uganda. These estimates were used in a discrete-event simulation model to project mean costs (2021 US$), disability-adjusted life years (DALYs), and consumption of blood products per patient (450mL units), for patients between 9months and 18years of age. We calculated cost-effectiveness as the ratio of incremental costs over incremental DALYs averted, discounted at 3% annually. To test the robustness of our findings, and the impact of uncertainty, we conducted probabilistic and one-way sensitivity analyses, scenario analysis, and price threshold analyses. Hydroxyurea treatment averted an expected 1.37 DALYs and saved US$ 191 per patient if administered at the MTD, compared with SCA care without hydroxyurea. In comparison, hydroxyurea at a fixed dose averted 0.80 DALYs per patient at an incremental cost of US$ 2. The MTD strategy saved 11.2 (95% CI 11.1-11.4) units of blood per patient, compared with 9.1 (95% CI 9.0-9.2) units of blood per patient at the fixed-dose alternative. Hydroxyurea at MTD is likely to improve quality of life and reduce the consumption of blood products for children with SCA living in Uganda. Compared with a fixed dose regimen, treatment dosing at MTD is likely to be a cost-effective treatment for SCA, using realistic ranges of hydroxyurea costs that are relevant across SSA. Compared with no use of the drug, hydroxyurea could lead to substantial net savings per patient, while reducing the disease morbidity and mortality and increasing quality of life.

Optimal Equipment Capacity Planning in the Neonatal Intensive Care Unit with Simulation-Optimization Approach

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal numbers of equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics.

Cost of treatment support for multidrug-resistant tuberculosis using patient-centred approaches in Ethiopia: a model-based method

BackgroundPatient and health system costs for treating multidrug-resistant tuberculosis (MDR-TB) remain high even after treatment duration was shortened. Many patients do not finish treatment, contributing to increased transmission and antimicrobial resistance. A restructure of health services, that is more patient-centred has the potential to reduce costs and increase trust and patient satisfaction. The aim of the study is to investigate how costs would change in the delivery of MDR-TB care in Ethiopia under patient-centred and hybrid approaches compared to the current standard-of-care.MethodsWe used published data, collected from 2017 to 2020 as part of the Standard Treatment Regimen of Anti-Tuberculosis Drugs for Patients with MDR-TB (STREAM) trial, to populate a discrete event simulation (DES) model. The model was developed to represent the key characteristics of patients’ clinical pathways following each of the three treatment delivery strategies. To the pathways of 1000 patients generated by the DES model we applied relevant patient cost data derived from the STREAM trial. Costs are calculated for treating patients using a 9-month MDR-TB treatment and are presented in 2021 United States dollars (USD).ResultsThe patient-centred and hybrid strategies are less costly than the standard-of-care, from both a health system (by USD 219 for patient-centred and USD 276 for the hybrid strategy) and patient perspective when patients do not have a guardian (by USD 389 for patient-centred and USD 152 for the hybrid strategy). Changes in indirect costs, staff costs, transport costs, inpatient stay costs or changes in directly-observed-treatment frequency or hospitalisation duration for standard-of-care did not change our results.ConclusionOur findings show that patient-centred and hybrid strategies for delivering MDR-TB treatment cost less than standard-of-care and provide critical evidence that there is scope for such strategies to be implemented in routine care. These results should be used inform country-level decisions on how MDR-TB is delivered and also the design of future implementation trials.Graphical

Productivity improvement by application of simulation and lean approaches in an multimodel assembly line

In the era where mass production is inevitable, developments in the manufacturing process have helped industries to produce goods in batches of enormous quantities. With the drastic growth in technology, it is crucial to update, and improve assembly lines continuously. This study aims to systematically understand the area where an assembly line needs improvement to enhance its process and productivity. Published articles related to assembly line improvements, lean techniques, and simulation were synthesized exhaustively. A Discrete Event Simulation model of an automated manufacturing company was developed based on the data acquired and observations. Integration of lean techniques along with simulation adds to the effectiveness of the proposed method. Crucial areas that need improvement were identified, and lean techniques such as Kaizen and Poka-Yoke were applied. The efficiency of the proposed improvements was visualized by altering the simulation model. The average WIP was reduced from 87.2 to 81.2, and the cycle time of seven stations was reduced as a result of the implementation of Lean principles. Laser Marking Station (Fixture clamping and Stopper Cylinder) and Pad Printing & Vision Inspection Station witnessed a twofold improvement in the proposed model. There was an improvement in throughput by 7.14%. The study would prove helpful for managers and policymakers to visualize the usefulness of implementing lean techniques with minimal investment.