System dynamics modeling (SDM) is a powerful tool and computer-aided simulation technique that can be applied to frame, understand, and discuss complex problems ^[1][2][1,2]. First called industrial dynamics [3], SDM was developed in the mid-1950s by Professor Jay W. Forrester at the Massachusetts Institute of Technology ^[3][4][3,4]. SDM originates from management and control engineering; this approach involves analyzing complex physical, biological, and social systems from the perspective of feedback and delays ^[5][6][5,6].

SDM starts with specifying the scope and boundary of the problem and then maps the problem in a visual environment as an interacting systems model that, through a visual programming protocol, can be used to execute quantitative simulations of different scenarios [7].

Supply chain management (SCM) involves managing the flow of goods and/or services from the material stage to the end user ^{[8][9][10][11][12][13]}[8,9,10,11,12,13]. A well-designed SCM system delivers high-quality goods and services efficiently and reliably. It can be difficult to create an efficient supply chain due to uncertainties and variabilities in materials flow, labor, and equipment availability. To resolve this, managers must understand the causes and impacts of these uncertainties and variabilities and work to reduce or eliminate them. The tools currently available for analyzing uncertainty are based on traditional mathematical approaches, such as single-parameter or local sensitivity analyses [14] that do not take variability into account. Since simulations can cope with variability, they are crucial for analyzing supply chains. In SCM, companies can use computer simulations to study operational problems that are difficult to model or solve analytically. Through simulation, companies can analyze how an innovative inventory system, such as just-in-time (JIT), would perform and how much it would cost without implementing it [15].

Many countries rely heavily on the apparel manufacturing industry to contribute to their national economies ^[16][17][16,17]. Although it is truly global in nature [18], it is plagued with several sustainability problems ^[19][20][21][19,20,21]. Excessive water and energy consumption, chemical usage, waste generation, poor labor conditions, and a lack of supply chain transparency all contribute to the environmental and social impacts of the industry. These issues pose serious challenges for the industry and call for immediate attention and action. To create an ethical and sustainable apparel manufacturing process, these issues must be addressed ^[22][23][24][22,23,24].

To address and improve sustainability and efficiency in apparel manufacturing, identifying the factors leading to a low input–output ratio and high energy consumption in the manufacturing industry is essential. These factors include inefficient production techniques, poor SCM, inadequate energy management systems, material waste and inefficiency, a lack of employee training and awareness, and the absence of sustainable practices. By addressing these factors and implementing appropriate measures, manufacturers can improve their input–output ratio and reduce energy consumption, leading to a more efficient and sustainable manufacturing process ^{[25][26][27][28]}[25,26,27,28].

Increased globalization of the apparel industry has led to many sustainable supply chain management issues. The sustainability of the supply chain in apparel manufacturing is heavily influenced by uncertainty and variability. To ensure that the entire process runs smoothly, understanding the influence of uncertainty and variability on supply chain sustainability is essential. Uncertainty refers to the lack of predictability or knowledge about future events, while variability represents the degree of change or inconsistency in processes. In the context of the supply chain, both uncertainty and variability can arise from factors such as demand fluctuations, supply disruptions, price volatility, and changing consumer preferences. Uncertainty and variability in the supply chain can significantly affect environmental, social, and economic sustainability efforts. Managing uncertainty effectively through accurate demand forecasting, efficient transportation planning, and strong supplier relationships is crucial for sustainability. Minimizing variability through robust quality control measures, efficient manufacturing processes, and waste reduction strategies also play a vital role. By addressing these challenges, the apparel manufacturing industry can enhance its supply chain sustainability and contribute to a more sustainable future ^{[29][30][31][32][33][34]}[29,30,31,32,33,34].

Apparel manufacturing in SDM models embodies various characteristics that prioritize sustainability, efficiency, and responsibility. Through an integrated supply chain, sustainable materials, energy efficiency, waste reduction, ethical labor practices, and technology adoption, manufacturers can create a more sustainable and responsible manufacturing process ^[34][35][36][34,35,36].

2. System Dynamics Modeling and Its Applications in Supply Chain Management

SDM was originally developed in the context of SCM, but it has a broad range of applications. SDM is now used in both the public and private sectors to help analyze and design policies [3]. It is used in policy development and organizational planning ^[4][12][4,12], public policy and management [13], behavioral economics [14], dynamics with complex nonlinearities [15], modeling in biology and medicine [16], natural and social science theory development [17], energy and the environment [18], dynamic decision-making processes [19], software development/engineering [20], and supply chain management ^[21][22][23][21,22,23]. This approach applies to complex problems in managerial, social, ecological, or economic systems; in fact, it applies to any dynamic system characterized by interconnection, mutual dependence, information flow, and circularity [6]. Through system dynamics (SD) models, various aspects of business operations ^[24][25][24,25] have been studied, such as strategic management [26], the management of manufacturing resources and capabilities ^[25][27][25,27], and marketing capabilities [28]. Recently, SDM has re-emerged in SCM after a prolonged period of dormancy. Recent research on SDM in SCM centers on inventory management and policies, time efficiency, demand augmentation, supply chain planning and implementation, and global supply chain management [2]. Several different conceptual frameworks and models for analysis have been proposed recently for SCM [37]. SDM has been used rarely in those frameworks and models, but given supply chain complexity, it has recently gained popularity [38]. Supply chains include numerous actors who organize the flow of materials and goods by sharing information. A supply chain system’s dynamic behavior is determined by various factors, including uncertainties in customer demand, multiple suppliers, various logistic routes, and different inventory methods. Supply chains are governed by uncertainty; therefore, SD simulations are helpful ^[38][39][38,39]. Over the last two decades, SD simulation has gained more attention as a tool for analyzing SCM. Forrester [3] initiated SD modeling to simulate production–distribution system interactions, among various supply chain actors. In complex SD models, variables are linked by nonlinear relationships, and feedback loops lead to uncertain system behavior [40]. Angerhofer and Angelides [5] developed categories of SD models for inventory management, amplification of demand, supply chain redesign, and international SCM. The authors examined these areas from the perspective of identifying and solving problems, and enhancing modeling approaches by providing exemplar models and studies that illustrated the applicability of SD to decision-making [5]. SD is used to model forward and reverse supply chains. In both manufacturing and supply chain-related applications, reverse and closed-loop supply chains are prevalent because SD models are based on feedback loops. Due to the dominant motivation of economic value recovery and not social and environmental objectives, reverse and closed-loop supply chains are not considered sustainable ^[41][42][41,42]. The reverse and closed-loop supply chain may lead to a more sustainable manufacturing approach by restoring financial returns, protecting natural resources, reducing pollution, and considering the social implications of supply chains. Feedback loops in circular economies and closed-loop supply chains require new SSCM requirements ^[43][44][43,44]. To address these challenges, SDM and a system thinking perspective may be beneficial in assisting sustainable decision-making and understanding complex supply chain systems.

3. System Dynamics Modeling for Sustainable Supply Chain Management

In SCM, customer and stakeholder requirements arise from the economic, environmental, and social dimensions of sustainable development ^{[8][9][10][11][12]}[8,9,10,11,12]. The SSCM concept combines sustainability and SCM ^[45][46][47][45,46,47]. As part of a sustainable supply chain, members are expected to meet environmental and social standards, as well as customer needs and related economic requirements, to remain in the chain ^{[48][49][50][51]}[48,49,50,51]. The literature on SDM for sustainable operations and SCM is sparse compared to well-known modeling methodologies, such as multi-criteria decision-making (MCDM) or linear optimization. MCDM, also known as multiple-criteria decision analysis (MCDA), entails analyzing various options for an event or area of research, which can encompass a range of subjects, including social sciences, medicine, daily life, engineering, and more ^{[52][53][54][55]}[52,53,54,55]. Linear optimization or linear programming is the process of optimizing a linear function, known as the objective function, subject to linear constraints, such as equality or inequality. It is extensively used in theoretical computer science ^[56][57][56,57]. Only a few conference proceedings that exclusively addressed SCM, sustainable manufacturing, or operations, could be determined ^{[58][59][60][61][62]}[58,59,60,61,62], which integrated manufacturing in environmental, financial, and social domains using a broad conceptual approach and SD model. Although the model is not studied in depth, the authors defined key components within each domain and outlined possible flow and stock variables for building the model. Kibira [58] presented an SD model framework for sustainable manufacturing to facilitate collaboration among researchers across the globe. It identifies four domains of relevant factors in the framework, including social, manufacturing, financial, and environmental. While the model is not analyzed in detail, it lists crucial components within each domain and identifies possible stock and flow variables for the model building. Likewise, Zhang [2] emphasized sustainable manufacturing and proposed a general framework for SD model development that incorporates sustainability metrics, considering the significance of systems thinking in engineering management. Wofuru-Nyenke [63] presented an approach to classifying manufacturing supply chain models by categorizing them into simulation models, hybrid models, mathematical models, and variations [63]. The study found that while simulation models and hybrid models have increased in use, mathematical models are used more for modeling sustainable manufacturing supply chains.

4. Sustainable Supply Chain Management in Apparel Manufacturing

It is particularly crucial to implement SSCM in the apparel manufacturing industry, which is highly labor intensive, uses materials with environmental effects, and relies heavily on global sourcing ^{[64][65][66][67]}[64,65,66,67]. Therefore, SSCM in the apparel manufacturing industry is a crucial area of research for both industry and academia ^[68][69][68,69]. SSCM issues in the apparel industry often center on corporate social responsibility ^[70][71][72][70,71,72], wages, fair treatment, workplace safety ^[73][74][75][73,74,75], carbon footprints [76], sustainable partnerships [77], sustainability education [78], and forecasting accuracy [79]. These issues have been studied from operational management ^{[35][80][81][82][83][84]}[35,80,81,82,83,84] and strategic management ^{[85][86][87][88]}[85,86,87,88] perspectives. Manufacturing in apparel supply chains generates challenges, including energy and resources usage, labor rights, and waste creation. The literature has extensively explored sustainability in the manufacturing process. Pineda-Henson [89] explored green productivity to improve resource efficiency and waste reduction in sustainable manufacturing. The authors found that water consumption, energy consumption, and land ecotoxicology played key roles in green productivity [89]. Ghazinoory [90] explored the implementation of a clean manufacturing approach in Iran for the promotion of industrial sustainability. This research found that the apparel industry is most in need of cleaner production practices [90]. Jin Gam [91] presented a “cradle-to-cradle apparel design” approach for sustainable apparel manufacturing. Using the proposed model, the authors demonstrated colorfastness and reliable function [91]. Jordeva found that most apparel cutting waste generated during apparel manufacturing in Macedonia is disposed of in landfills and identified a lack of investment in recycling infrastructure [92]. Wickramasinghe studied manufacturing issues in the apparel manufacturing industry and found that cost reduction and quality enhancement were positive effects of total productive maintenance [93]. Pinheiro examined the textile solid waste generated in the garment manufacturing industry in Brazil and suggested reusing raw materials for sustainability [94]. Van der Velden used social life cycle assessment (S-LCA) to analyze the production system in apparel supply chains. According to the author, the S-LCA considers five social issues: wage standards, child labor, poverty, working hours, and health and safety issues [95]. Hirscher offered a consumer-engaged design strategy, which aims to engage consumers in social manufacturing through participatory design [96].

5. System Dynamics Modeling for Supply Chain Management in Apparel Manufacturing

SDM in apparel SCM is rarely used. The researchers only identified a few papers using SDM in apparel manufacturing supply chains. Bala [97] used an SDM of supply chains in the ready-made garment (RMG) industry in Bangladesh for policy analysis and simulated it for the sustainable shipment of garments. The study concluded that the proposed model can be efficiently applied to developing policy scenarios. It can also be used to create a better supply chain approach for sustainable growth for RMGs and to optimize the model for further research in the future [97]. Issa [98] examined the performance of the apparel supply chain involving mass customization, using an SDM. This study showed that, under mass customization, the various products, lead times, return policies, and quality levels had a significant impact on supply chain profitability [98]. Wilson [83] examined the role that the government can play, through its industrial policy, in enhancing competitiveness in apparel manufacturing in Trinidad and Tobago. Using SDM and simulation to analyze data for vertical policies, the study found that three interrelated elements affected apparel manufacturing: the market, apparel products, and productive resources [99]. Mehrjoo examined the fast fashion apparel industry because of its features, including products with a short life cycle, fluctuating demand, uncertainty, impulse buying, high competition for price, and global sourcing. To analyze the fast fashion apparel industry’s behavior and relationships, an SDM was developed for investigating the relationships and behavior of the fast fashion apparel industry with the supply chain [100]. Lidia [101] constructed a supply chain model for a small and medium-sized enterprise in Indonesia by utilizing SD. The purpose of using SD was to introduce a decision support system, which helped management to determine the most effective business strategies [101]. Corinna Cagliano [102] employed SD to simulate the warehouse operations of a leading fast fashion vertical retailer. The author demonstrated case scenario simulations of how warehouse policies can be defined to increase efficiency, reduce costs, reduce inventory, and reduce lead times [102]. Haddad [103] provided a case study on lean implementation at a garment manufacturer in Lebanon. The author constructed an SD model of the production process to simulate the long-term effects of complementary lean techniques on system performance, and concluded that SD models can be used as decision-making tools to mitigate undesirable impacts and ensure the sustainability of lean initiatives over time [103].