Simulation Model Of Supply Chain Research Articles

Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation

PurposeThis study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.Design/methodology/approachResponse surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.FindingsThe high R2 values of 97.38% for CO2 and 97.28% for service level, along with adjusted R2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.Originality/valueThis study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

Novel Simulation Optimization Approach for Supply Chain Coordination and Management

The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulation modeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering.

A rolling horizon planning approach for short-term demand supply matching

AbstractDemand fulfillment and order management are important functions in semiconductor supply chains to interact with customers. In this paper, an iterative short-term demand supply matching (STDSM) algorithm based on mixed-integer linear programming (MILP) is proposed. This approach repromises orders taking into account the finite capacity of the shop floor. Decomposition is used to obtain computationally tractable subproblems. The STDSM approach is applied together with master planning and allocation planning in a rolling horizon setting. A simulation model of a simplified semiconductor supply chain is used for the rolling horizon experiments. The experiments demonstrate that the proposed STDSM scheme outperforms conventional business rule-based heuristics with respect to several delivery performance-related measures and with respect to stability.

Surrogate-assisted evolutionary optimization for perishable inventory management in multi-echelon distribution systems

Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems.

Internet of Things based Decision Support System for Green Logistics

This work proposes an IoT-based Real-Time Decision Support System for Perishable Products. The proposed system collects data during the transportation process and will interfere in the case of failure. Three different simulation models corresponding to different configurations and mitigation plans are built. The simulation models consider decisions such as stopping transportation and rerouting shipments to minimize losses in case of failure. The three different supply chain simulation models are implemented through a case study that considers transporting a perishable fruit in the intercontinental United States. A financial and environmental analysis is conducted to show the benefits of the proposed system.

Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach

CONTEXTThe world is experiencing frequent extreme weather events like droughts, snowstorms, and shifting of seasons due to climate change. Increased frequency and severity of these extreme weather events threaten food security because agriculture depends on climate conditions. Impacts of climate change on the agricultural system not only occur at the grower's level, but also cascade to other levels along the supply chain. OBJECTIVEThis study aims to quantify a wide range of economic impacts of different extreme climate events on different stages of a food supply chain. METHODSWe chose the potato supply chain in Idaho as a case study. We developed a multi-echelon supply chain simulation model using an agent-based modeling (ABM) approach with five types of agents—farmers, shippers, processors, retailers, and logistics companies. In addition to the business-as-usual (BAU) scenario, we designed two climate-related disruption events—drought and snowstorm. We quantified the heterogeneous impacts at different stages of the supply chain for both fresh and processed potatoes using key performance indicators (KPIs) including revenues, prices, lead times, traded quantities, and food waste quantity. RESULTS AND CONCLUSIONThe impacts of the disruption events are different on different agents in the supply chain for different product categories. The price hike of fresh potatoes is far higher than processed potatoes during disruption events. This price hike makes consumers switch to processed potatoes, which require more fresh potatoes as an input that further reinforces the price hike. However, because processed potatoes have an elastic demand, once their prices go up due to higher input cost, their demand drops. Non-contracted farmers gain additional revenues from the disruption events, whereas contracted farmers incur a loss due to lock-in price and lower than usual harvest. SIGNIFICANCEThe methodology developed in this study could be applied to other food and agricultural supply chains for understanding the vulnerabilities at agent levels due to climate change disruption events. The findings would help develop mitigation strategies or policies to improve the well-being of the overall supply chain.

Assessing recycling, displacement, and environmental impacts using an economics‐informed material system model

Material production drives an increasingly large fraction of CO2-equivalent emissions. Material efficiency strategies such as recycling serve to reduce these emissions. Current analyses of the effectiveness of such strategies do not include economically induced rebound effects, overestimating the associated environmental benefits. We present a dynamic supply chain simulation model for copper through 2040 incorporating inventory-driven price evolution, dynamic material flow analysis, and life cycle assessment alongside mine-level economic evaluation of opening, closing, and production decisions. We show that permanent increases in recycling displace ∼0.5 kilotonnes mine production per kilotonne increase in scrap supply on average, while short-lived recycling policies can lead to increased mine production. We find evidence for supply chain evolution pathways minimizing the rebound effect and maximizing displacement of primary material, where increasing refined copper and concentrate prices and decreasing demand serve to decrease mining. However, even in best-case scrap supply scenarios, CO2e emissions from the copper sector increase 25% by 2040 relative to 2018 due to demand growth, ore grade decline, and lower displacement among large scrap supply changes. With implementation of best available technologies across all supply chain components, we estimated 2040 CO2e emissions 10% below those of 2018 are possible, though still well short of 2°C emissions targets. We find increasing mine taxes and royalties, reclamation costs, and exploration costs further increase displacement, as does the inclusion of scrap prices on major futures exchanges. These results highlight the importance of considering the economics of the entire material supply chain when implementing material efficiency strategies. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Disruption evaluation in end-to-end semiconductor supply chains via interpretable machine learning

COVID-19 has posed unprecedented challenges to global health and the world economy. Two years into the pandemic, the widespread impact of COVID-19 continues to deepen, impacting different industries such as the automotive industry and its supply chain. This study presents a hybrid approach combining simulation modeling and tree-based supervised machine learning techniques to explore the implications of end-market demand disruptions. Specifically, we apply the concept of born-again tree ensembles, which are powerful and, at the same time, easily interpretable classifiers, to the case of the semiconductor industry. First, we show how to use born-again tree ensembles to explore data generated by a supply chain simulation model. To this end, we demonstrate the influence of varying behavioral and structural parameters and show the impact of their variation on specific key performance indicators, e.g., the inventory level. Finally, we leverage a counterfactual analysis to identify detailed managerial insights for semiconductor companies to mitigate adverse impacts on one echelon or the entire supply chain. Our hybrid approach provides a simulation model enhanced by a tree-based supervised machine learning model that companies can use to determine optimal measures for mitigating the adverse effects of end-market demand disruptions. We close the loop of our analysis by integrating the findings of the counterfactual analysis backward into the simulation model to understand the overall dynamics within the multi-echelon supply chain.

Simulation model of natural gas supply chain in a function of costs optimization: the case of Croatia

The purpose of this study is to develop a natural gas simulation model for costs optimization. The main focus of the model is on the transmission system since its imbalance leads to the penalties which must be paid by the suppliers. The total nominated amount of natural gas (the gas injected into the transmission system) must be consumed (withdrawn from the system) in order for the system to be in balance. In practice, this is not the case since it is very hard to accurately predict the future hourly consumption (in order to make a nomination) and certain deviations appear which leads to the imbalance. However, this problem could be solved by introducing a special electromotor valve which would be placed at the beginning of the distribution system and control the accumulation of the system. To test this solution, a simulation model was created using Arena Simulation tool. Data for the simulation model are collected by the natural gas distributor and consist of natural gas consumption and nomination values for one measuring-reduction station on the hourly basis. Thus, the final dataset includes 8.754 records. The separate As-Is and To-Be models for seven (summer) months were made and the results were compared. The simulation experiment shows that the positive rebalancing energy would be reduced by 32%, and the negative one by 34%. The created model can serve as a good initial step for the analysis of the justification of investment in the implementation of a technical solution that would manage the accumulation of the distribution system.

RESOURCE UTILIZATION ANALYSIS OF HIGHWAY PROJECTS USING ARENA

The construction sector is a significant contributor to the Gross Domestic Product of a developing country. Infrastructure improvement plays a vital role in this wherein highway construction is a dynamic sector requiring proper planning and scheduling multiple resources. Appropriate integration among various associated stakeholders is essential for a project’s success, aided by supply chain management. Resource planning is one of the basic concepts in supply chain management, with material and equipment management being the critical area. The main objective of this study is to develop a conceptual supply chain simulation model using ARENA, to analyze the equipment idling and utilization rate, keeping inter-arrival time for dispatch, the number of equipment, and working hours as constant. This model employs the real-time ‘best fit’ material utilization data as input. Material utilization data collected from 62 construction projects are analyzed to arrive at a ‘best fit’ probability distribution. This study’s conceptual supply chain simulation model helps formulate suitable material and equipment delivery plans to lessen risk in construction projects.

Additive manufacturing for customized hearing aid parts production: an empirical study

ABSTRACT The recent supply chain networks that utilize traditional manufacturing processes in their flow are hereafter referred to as Traditional Supply Chain Networks (TSCN). In fact, these TSCNs achieve a fair degree of customization through parts standardization in parts and product range with available specific customized parts. Yet, true customization remains unattainable in TSCNs, at least in terms of economy of scale. 3D printing integration in supply chains can make them truly agile by offering product customizations only on necessity, thereby eliminating the need for producing unwanted parts. The proposed research study investigates the logistical implications of integrating Additive Manufacturing (AM) in the healthcare industry supply chain, and examines the impacts and subsequent results obtained by simulation analysis. The resultant supply chain is referred to as 3DPSCN (3D Printing integrated Supply Chain Network). Simulation models of the related healthcare supply chains, both Traditional (TSCN) and 3D printing integrated (3DPSCN), are developed using data gathered from available resources and real-time observations. Ultimately, a comparative study is done by simulation analysis. The main contribution of this paper is to propose of an optimized supply chain simulation model that incorporates AM in the manufacturing of a hearing aid prosthetic. The results of the analysis elaborate the benefits obtainable such as lead-time reduction, waste reduction, and number of customers served.

The principal-agent problem in supply chain management – the simulation based framework

Abstract This research deals with a phenomenon well-known in socio-economic studies and referred to as the Agency Theory: the principal-agent problem. The agent is designated to act on behalf of the company owner, i.e., the principal, in the domain of supply chain management in the face of a supplier’s default uncertainty. Each of the players is gain-oriented, but their objective functions and the degree of risk-bearing may significantly differ, leading to an agent’s decisions not being optimal in terms of the principal’s outcome. This article proposes a multi-period supply chain simulation model that can be used to perform a supply chain optimization and comparison between the agent and the principal. The proposed framework allows for differentiating the model parametrization depending on the industry, in particular the consequences of the inability to deliver the end product, costs of keeping safety stocks, or the uncertainty regarding the suppliers’ delivery failure. As players’ objective functions, we consider expected profit-based indicators and measures taking the inter-period variance into account. We find that the agent’s actions may diverge from the principal’s optimum if the agent’s incentive system is not selected correctly. We also propose a solution that unifies the goals of the players. The recent COVID-19 pandemic amplifies the importance of such research. Many companies had to limit production capacities due to global lockdowns and, per the JIT production strategy, the prior safety stock levels were low.

Quality safety of agricultural products in supply chain-computer simulation based on multi-agent system

As is known that, one of the challenges in ensuring the quality and safety of agricultural products in China is how to organize plenty of scattered small farmers and integrate them into the modern agricultural products supply chain system. In this paper, in order to promote the tight integration of agricultural products supply chain, based on multi-agent system, a computer simulation model of agricultural products supply chain is proposed. Through a series of simulation experiments, the evolution of the organizational structure of the agricultural products supply chain, its impact on the quality and safety of agricultural products under different government regulations are explored and discussed in detail. It follows from these simulation results that the more long-term-contract farmers and sellers, the more conducive to the improvement of the quality and safety of agricultural products, and the corresponding countermeasures and suggestions are also provided.

A Discrete Event Simulation Analysis of the Bullwhip Effect in a Multi-Product and Multi-Echelon Supply Chain of Fast Moving Consumer Goods

Timely delivery is the major issue in Fast Moving Consumer Good (FMCG) since it depends on the lead time which is stochastic and long due to several reasons; e.g., delay in processing orders and transportation. Stochastic lead time can cause inventory inaccuracy where echelons have to keep high product stocks. Such performance inefficiency reflects the existence of the bullwhip effect (BWE), which is a common challenge in supply chain networks. Thus, this paper studies the impact of stochastic lead time on the BWE in a multi-product and multi-echelon supply chain of FMCG industries under two information-sharing strategies; i.e., decentralized and centralized. The impact was measured using a discrete event simulation approach, where a simulation model of a four-tier supply chain whose echelons adopt the same lead time distribution and continuous review inventory policy was developed and simulated. Different lead time cases under the information-sharing strategies were experimented and the BWE was measured using the standard deviation of demand ratios between echelons. The results show that the BWE cannot be eliminated but can be reduced under centralized information sharing. All the research analyses help the practitioners in FMCG industries get insight into the impact of sharing demand information on the performance of a supply chain when lead time is stochastic.

Minimizing the bullwhip effect in a supply chain: a simulation approach using the beer game

In this research, the aim is to find the most appropriate inventory management logic and set of rules along with the optimal decision values that will minimize the bullwhip effect in a supply chain, taking the beer game supply chain as a reference model. In order to achieve this, a simulation model of the beer game supply chain is developed along with an ordering strategy based on the Economic Order Quantity with additional rules, such as no backorder policy, vendor-managed inventory, and taking into consideration route deliveries, all of which are implemented in the ordering algorithm. In the literature, there is extensive research conducted on the causes of the bullwhip effect and in the presence of certain inventory management policies. However, these terms are rarely combined with simulation modeling to provide satisfactory proven results. In this article, our proposed ordering algorithm avoids the bullwhip effect to a very large extent. The results show that approximately half the cost is incurred compared to recent studies with the same settings.

Dynamic simulation model of salt supply chain to increase farmers income

The average national salt production is around 2.5 million tons per year has not been able to meet domestic salt demands. One of the factors that caused this was the low incentives received by farmers so that farmers were reluctant to carry out production in the following year. As the main actor in the national salt industry, farmer’s welfare needs to be considered to ensure the sustainability of salt production. This study aims to develop a dynamic model of the salt production supply chain by identifying internal and external factors that influence the system. We use system dynamic methods because this framework can model nonlinear equations among variables and can represent feedbacks loop relationships. From the base model simulation results, we developed a scenario to improve the welfare of salt farmers. By using the scenario of land intensification with filtering – threaded technology, geomembrane, and tunnel system, farmer’s profits could be increased up to Rp 13,830,300 per ha per season in the shortest dry season.

Bypassing Data Issues of a Supply Chain Simulation Model in a Big Data Context

Supply Chains (SCs) are complex and dynamic networks, where certain events may cause severe problems. To avoid them, simulation can be used, allowing the uncertainty of these systems to be considered. Furthermore, the data that is generated at increasingly high volumes, velocities and varieties by relevant data sources allow, on one hand, the simulation model to capture all the relevant elements. While developing such solution, due to the inherent use of simulation, several data issues were identified and bypassed, so that the incorporated elements comprise a coherent SC simulation model. Thus, the purpose of this paper is to present the main issues that were faced, and discuss how these were bypassed, while working on a SC simulation model in a Big Data context and using real industrial data from an automotive electronics SC. This paper highlights the role of simulation in this task, since it worked as a semantic validator of the data. Moreover, this paper also presents the results that can be obtained from the developed model.

Research on Response Elastic Simulation and Optimization of Natural Disaster Emergency Supply Chain

China has a vast territory and a large population density. The frequent occurrence of natural disasters has caused enormous losses to the lives and property of our people. With the expansion of the connotation of rescue organizations, a cooperative relationship between rescue organizations and rescue organizations and commercial organizations to improve rescue efficiency and minimize losses caused by natural disasters has become a research in emergency supply chain research. New perspective. Through the construction of the natural disaster emergency supply chain elastic simulation model, the paper optimizes and analyzes the two strategies of fortification inventory and emergency procurement to obtain the optimal solution and proposes the elastic optimization strategy. With the dynamics of simulation, the simulation model of natural disaster emergency supply chain is established, and the elasticity is optimized as the optimization goal.

Evaluating different distance metrics for calculating distances of last mile deliveries in urban areas for integration into supply chain simulation

ABSTRACTThis paper describes how last mile deliveries can be considered in a supply chain simulation using distance metrics. A brief overview of urban morphologies and their characteristics will be presented, with a focus on traffic-relevant characteristics. Also, different distance metrics are investigated with respect to their suitability for urban logistics. After a short characterisation, common distance metrics will be applied and compared to the presented urban morphologies, taking into account their interrelationships, properties and peculiarities by means of example cases. The aim here is to determine whether existing approximations for supply chain simulation models are sufficient, even against the background of changing urban design forms, or whether adjustments need to be made. Subsequently, it will be explained how the findings gained for the improved simulation of urban distribution networks can be integrated into simulation tools. The authors will propose a distance metric and name a rescaling factor that can be applied independently of the cityscape.

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.