Optimizing Supply Chains Through System Dynamics Modelling and Simulation

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.

This paper proposes a groundbreaking theoretical model specifically designed for the FMCG sector, focusing on optimizing supply chain dynamics to balance operational efficiency with customer satisfaction. Drawing on hands-on strategies and problem-solving abilities, the framework aims to offer practical solutions for common challenges faced by FMCG operations managers, such as inventory management, demand forecasting, and efficient resource allocation. The fast-moving consumer goods (FMCG) industry is characterized by intense competition, short product life cycles, and rapidly changing consumer preferences. In this challenging environment, optimizing supply chain dynamics is crucial for FMCG companies to achieve operational efficiency and enhance customer satisfaction. This concept paper presents a novel framework for integrating operational efficiency and customer satisfaction in FMCG supply chains, aiming to provide insights and strategies for FMCG companies to improve their supply chain performance. The framework emphasizes the importance of aligning supply chain strategies with customer needs and preferences. By focusing on key areas such as inventory management, distribution network efficiency, and technology integration, FMCG companies can improve their supply chain agility and responsiveness. Additionally, the framework highlights the importance of leveraging data analytics and technology to enhance supply chain visibility and decision-making. Implementing the framework requires collaboration among stakeholders, including suppliers, manufacturers, distributors, and retailers. By working together to optimize supply chain processes and enhance customer service, FMCG companies can gain a competitive advantage in the marketplace. Overall, the framework offers a comprehensive approach to optimizing FMCG supply chain dynamics, with the ultimate goal of improving operational efficiency, reducing costs, and enhancing customer satisfaction.
 Keywords: FMCG, Supply Chain, Customers, Framework.

Contributors

Research regarding supply chain optimisation has been performed for a long time. However, it is only in the last decade that the research community has started to investigate multi-objective optimisation for supply chains. Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. In this chapter, we present a comprehensive literature review of existing multi-objective optimisation applications, both analytical-based and simulation-based, in supply chain management publications. Later on in the chapter, we identify the needs of an integration of multi-objective optimisation and system dynamics models, and present a case study on how such kind of integration can be applied for the investigation of bullwhip effects in a supply chain.

Transport is a vast and complex socio-technical system, and despite a clear need to reduce dependence on fossil fuels due to undesirable environmental impacts, it is largely locked into business-as-usual. Systems approaches are a useful way to help make sense of multiple competing influences which may be simultaneously driving change and supporting the status quo. This paper applies qualitative system dynamics modelling to help interpret the results of a Delphi study into global transport transitions, involving 22 international experts in various aspects of transport. The main contribution of the paper is its exploration of the use of system dynamics (SD) modelling to interpret the Delphi findings. SD modelling was used to reveal and elucidate the causal arguments put forward by the expert panel about the factors driving business-as-usual, the factors creating barriers to more sustainable transport systems, and the drivers of change. The SD model is used to explore and expose the key causal patterns at play, and how these interact to both support and hinder change. The resulting model shows the complex, interdependent dynamics involved in supporting the status quo. Even at the relatively high level of analysis reported here, the model is useful in revealing interdependencies between parts of the system, where change in one part may well have knock-on effects elsewhere in the system. In particular the model reveals the strong reinforcing loops that act to minimise the impact of change drivers and thus retain the dominance of automobility. The result is a system that is highly dependent on the continued existence of key reinforcements such as policies that subsidise fossil fuels. From a methodological perspective, the outcomes of the Delphi study provided a rich source of qualitative material which was highly suitable for developing a system dynamics model.

In light of the information revolution, this study aims to clarify the impact of artificial intelligence and supply chain resilience on the supply chain performance of engineering, electrical, and information technology companies registered with the Jordan Chamber of Industry. This study expands knowledge by exploring the relationships between artificial intelligence and the moderating supply chain dynamism. This study looks at artificial intelligence as an important resource, in addition to resilience supply chains, an important resource in raising the supply chain performance for companies. The questionnaire was conducted via e-mail and the study sample included (208) companies registered with the Jordanian Chamber of Industry and Commerce. The data was analyzed using the smart (Pls) software and its direct link with artificial intelligence and supply chain resilience. In addition, the analysis shows that there is a direct relationship between the mediating variables supply chain dynamism and supply chain resilience and supply chain performance. These results provide an insight into the relationship between artificial intelligence and supply chains, and the Moderating variable on the performance of a company's supply chains, which may be an entry point for companies to enhance their performance due to the importance of this sector to the Jordanian economy.KeywordsArtificial intelligenceSupply chain ResilienceSupply chain performanceSupply chain dynamismEngineering electricalInformation technologyJordan

168 Impact of Connecticut’s Good Samaritan Laws in Preventing Opioid Overdose Deaths – An Applied System Dynamics Approach

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

Complex systems abound on this planet, in the composition of the human body, in ecosystems, in social interaction, in political decision-making, and more. Analytical methods allowing us to better understand how these systems operate and, consequently, to have a chance to intervene and change the undesirable behavior of some of the more pernicious systems have developed and continue to be enhanced via quickly changing technology. Some of these analytical methods are accessible by pre-college students, but have not been widely used at that level of education. Jay Forrester, the founder of one of the methodologies, System Dynamics (SD), used to study complex system behavior involving feedback, laments the lack of understanding of complex systems evident in short-sited decisions made by legislators -- global climate change and fiscal policies being cases in point. In order to better prepare future decision makers with tools that could allow them to make more informed decisions about issues involving complex systems efforts have been underway to increase pre-college teacher understanding of the SD method. The research described in this dissertation introduces the mathematics education community to the value of System Dynamics modeling in pre-college algebra classes, indicates a path by which a traditional mathematics curriculum could be enhanced to include small SD models as a new representation for elementary functions studied in algebra classes, and provides an empirical study regarding conceptual understanding of functions by students. Chapter 2 indicates the numerous beneficial learning outcomes that empirical studies have shown accompany model-building activities. Chapter 3 indicates the need for students to become familiar with complex systems analysis, how SD modeling (one method of complex systems analysis) aligns with the Common Core State Standards in Mathematics, and the work that has transpired over the past two decades using SD in K-12. Chapter 4 focuses on the importance of the concept of function in high school mathematics, some limitations of exclusive reliance on the closed form equation representation for mathematizing problems and the SD stock/flow representations of some of the elementary functions that are studied in algebra classes. Chapter 5 looks at the issues affecting two traditional teachers and the challenges they faced when trying to reintroduce SD modeling into their algebra classes. Chapter 6 explains the student component of the classroom experiment that was conducted by the teachers who are highlighted in Chapter 5. The analysis of the results of student model-building activities in the two classroom

Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model

Supply chain positioning strategy integration, evaluation, simulation, and optimization

Effective Supply Chain Management (SCM) is a pivotal issue in the dynamically changing business environment. Dynamic SCM requires integrated decision-making amongst autonomous chain partners with effective decision information synchronization amongst them. By exploiting flexibility in supply chain structures, better performance can be achieved. Similarly, by judiciously employing decision flexibility and the associated dynamic control amongst autonomous supply chain nodes, many improvements are possible. The paper presents a study on the role of different flexibility options (i.e. no flexibility, partial flexibility and full flexibility) in a dynamic supply chain model based on some key parameters and performance measures. Supply chain flexibility has significant potential and needs a greater research attention. The paper attempts to advance the knowledge of dynamic control on effective flexibility exploitation in the context of dynamic supply chains. A simulation model of a dynamic supply chain is used for this purpose. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. Based on the order and sample information available from the immediate buyers or customers, the supplier selection decisions are dynamically made. A seemingly good decision at a stage based on local information often ends up as detrimental not only to the total chain cost, but also to the total costs of the node itself. These observations are important for the designers and managers of the flexible supply chain systems to arrive at appropriate types and a judicious level of flexibility to attain significant improvements in total cost reduction. The modelling of dynamic supply chains with a focus on flexibility can offer enormous potential to the industry. This paper addresses this interesting and challenging domain.

The supply chain is an extremely important and complex system, which is widely related to the social system and the management system. The hierarchical scheduling and production planning of a dynamic supply chain can enable enterprises in the supply chain to obtain, and maintain, stable and lasting competitive advantages; thus, improving the overall competitiveness of the supply chain. Simultaneously, research on supply chains is also a hotspot in system engineering. At present, research on complex supply chain systems with dual channels is insufficient, and analysis of dynamic supply chains with supply failure of goods is rare. In the epidemic era, dynamic supply chain management was affected by the epidemic and became a very challenging topic. The control of the supply chain system under the impact of an epidemic situation is particularly important. This paper focused on H∞ the fault-tolerant control of a complex supply chain system with dual channels. First, from the perspective of engineering practice and green production, we established a new green supply chain model. This supply chain model includes the dual-channel supply of goods and also considers the supply failure of goods. At the same time, production delay and supply delay are also considered in this model. Second, a supply scheduling scheme was designed through cybernetics to ensure the stability of a supply chain with dual channels under supply failure of goods, and we completed supply chain management. Finally, the effectiveness of our results was verified by specific Chinese enterprise data.

With the purpose of detecting the impact that variations of demand cause in the milk-cheese supply chain, and determining how the operational policies of capacity, inventories or labor force can mitigate this impact, a system dynamics simulation model has been designed based on a survey conducted on a sample of cheese manufacturers and their links with milk farms, transportation companies and cheese distributors. This supply chain will be consolidated when a milk center that will collect the raw milk is completed. From this center, and after adequate treatment, milk will be distributed to the different cheese manufacturers in the supply chain. Managing adequately the milk-cheese supply chain represents an important challenge due to the short life of these products. Although this study was done in a region in Latin America, its results can be applicable to food supply chains by introducing some modifications. The milk-cheese supply chain in this case study contemplates three milk producers, one milk center, five cheese producers and several distributing agents. These companies operate individually under normal conditions, but they have understood that their integration in a supply chain improves the competitiveness of all its members. That is to say, the sum is greater than the parts. For its initial design a simulation software model is used in which the resources of the supply chain are optimized. Later the product of this optimization facilitates some initial values to be used in the system dynamics model in which cause-effect or influence relationships have been previously established considering the most representative variables. Finally, changes in operational policies that can reduce the level of pending orders in the supply chain are tested using other simulation software. The main contribution of this research is that it can serve as support or contribute to reduce the uncertainty in the decision making process of the supply chain management due to the speed with which individual or combined policies can be analyzed. In response to a variation of demand the most adequate policy may be selected and that can be done before the policy is implemented.

Optimal supply chain design depends on the business context the chain caters for. In this dissertation, we are concerned with attaining and maintaining this alignment between a firm's supply chain portfolio and its business context. Following an introduction in Chapter 1, in Chapter 2 of this dissertation, we analyze the influence of a firm's product types on optimal supply chain design: Academics and practitioners agree that functional products are best delivered via physically efficient supply chains, while innovative products are best delivered via market responsive chains. However, to date, only a few firms have systematically adjusted their supply chain strategies according to this argument. Instead of carrying only one product type, firms deliver a number of both functional and innovative products in parallel complicating the alignment of supply chain portfolios with product portfolios. We address this issue and analyze in detail a firm's optimal supply chain portfolio as a function of its product portfolio. We target the tactical level of decision-making and develop a generic mathematical model to analyze how product portfolios affect the optimal setup of supply chain portfolios. We identify generic problem properties and perform extensive sensitivity analysis to illustrate our findings with numerical examples. Our results indicate that effective supply chain and product portfolio alignment holds the potential for significant cost savings. Because of the large number of factors influencing the optimal set-up, however, profound quantitative analysis is required to exploit the full potential of aligning the supply chain with the product portfolio and to guide implementation. In Chapter 3, we extend our modeling framework and take into account the fact that a firm's products and global business parameters, such as labor rates and transportation costs, evolve over time. Hence, firms need to adapt their supply chain portfolios frequently to stay aligned in the long term. Adapting a well-established supply chain portfolio, however, may involve high costs and expose a firm to unforeseeable risks. We address this issue, differentiating business dynamics into product portfolio dynamics and global business dynamics, and classifying supply chain adaptation into high, medium and low. Building on these classifications, we extend our mathematical model to analyze how much supply chain adaptation a firm actually requires to respond to the business dynamics it faces. Our results indicate that supply chain adaptation may indeed be crucial for a firm to retain its competitiveness. The need for adaptation, however, varies widely across firms: For example, a firm faced with product portfolio commoditization may be required to adapt its entire manufacturing footprint, while a firm with a high product turnover rate may not need to adapt its supply chain portfolio at all. Furthermore, the need for supply chain adaptation is not only determined by the business context in which a firm operates, but can also be manipulated by the firm's product portfolio decisions. Finally, a firm should carefully align its supply chain portfolio with the employed supply chain adaptation strategy to exhaust attainable benefits. In Chapter 4, we widen our scope and develop a step-by-step process to guide sustained alignment between a firm's business context and its supply chain portfolio in practice. Drawing on academic literature, insights from our quantitative models, and extensive interviews with executives from leading firms, we propose and detail four process steps. In the first step we suggest challenging the performance of a firm's current supply chain setup and identifying pressure points to evaluate how acute supply chain realignment is. As a second step we propose and detail a business context analysis to identify what a supply chain lacks and potential areas for realignment. To complement the previous analysis, in a third step, we frame and discuss generic options for long-term supply chain strategy. As a fourth step, we lay out opportunities and pitfalls during the project implementation. We conclude with recommendations on how to stay aligned in the long term. We end the dissertation with a conclusion in Chapter 5.