Abstract
Strategic optimization in supply chain networks is a critical process that focuses on finding the efficient and time-effective combination of distribution centers and factories within a supply chain. It involves a well-executed deployment of resources and cutting-edge technologies to maximize performance and efficiency in a supply chain network. By optimizing the supply chain networks, small and medium enterprises (SMEs) can accrue a myriad of benefits, including but not limited to streamlinining operations, enhancing customer service, and reducing operational costs. Despite the promising benefits, SMEs still face many challenges that threated to disrupt their concerted efforts to build resilient supply chain networks. This paper aims to provide a critical review of the state of research on strategic optimization in optimization in supply chain networks, focusing on the application of Algorithmic Game Theory (AGT). The review delves into three major domains: the integration of the core tenets of AGT in supply chain management, strategies SMEs can implement to reduce uncertainty, and measures to mitigate adverse events. Identified gaps in existing literature include underrepresentation off real-time data, neglect of environmental and social considerations, limited application of AGT in SMEs, and the need for holistic uncertainty management. Besides examining the gaps in exiting literature, this paper also outlines future research opportunities, namely integrating cutting-edge technologies in supply chain networks, suggesting avenues for tailoring AGT models for SMEs, and incorporating environmental and social considerations.
Strategic Optimization in Supply Chain Networks: An Algorithmic Game Theory Approach to Managing Uncertainty and Adverse Events in SMEs
1.0 Introduction
In today’s ever-evolving and highly competitive business landscape, supply chain optimization has never been more important for SMEs looking to gain a competitve edge. Optimizing supply chain networks for these enterprises involves comprehensive analysis and well-planned reconfiguring of the structure of the supply chain to maximize costs, meet customer demands more effectively, and minimize costs. Recognizing the need for strategic optimization in SMEs supply chain networks, this paper presents a critical analysis of the existing literature, focusing on the application of AGT to provide sustainable solutions to address the challenges posed by adverse events and uncertainties. AGT is based on the intersection of computer science and game theory, with the objective of understanding and designing algorithms in strategic environments, in this case, supply chain networks. The introduction of the AGT introduces a strategic dimension to supply chain management, where optimization is viewed through the lens of interactive decision-making. This paper analyzes the current research on strategic optimization in supply chain networks, focusing on the AGT framework, and also charts a course for future research by identifying the gaps and opportunities in existing literature.
2.0 Current State of Research
2.1 Uncertainty and Adverse Events in Supply Chain Networks of SMEs
In the highly competitive business landscape, both SMEs and large companies face similar supply chain challenges. Etemad (2022) contends that SMEs find the intricate nature of supply chain challenges, coupled with uncertainty and adverse events, more difficult to overcome. Without strategic optimization in supply chain networks, SMEs face a myriad of challenges in predicting the future, maximizing available opportunities, and nullifying the negative impact of disruptions and uncertainties. One major challenge SMEs face in the sphere of supply chain optimization is data quality. Kumar et al (2022) maintain that SME supply chain networks are built on data, particularly inventory balances, open orders, or master data. According to this study, supply chain optimization helps to make sense of all of this data by generating insights and uncovering patterns. Such insights enable SMEs improve the quality, delivery, customer experience, and profitability of their products. Notwithstanding the positive outcomes of utilizing quality data, issues surrounding data availability, age, and quality are prevalent among the supply chain networks of SMEs (Omri et al., 2020). In situations where data is outdated, incomplete, or of suboptimal quality, the ensuing plans and decisions may prove to be ineffectual. This necessitates SMEs to adopt and implement digital technologies and effective data management processes to ensure data-driven decision-making and, ultimately, improve their supply chain management.
Besides data quality, uncertainties and adverse events in SMEs’ supply chain networks stems from inventory management challenges. In today’s business landscape, Alam et al. (2024) asserts that demand has never been more volatile, customer expectations are constantly changing, regulatory compliance is more complex than ever, and ecommerce is driving greater competition. A well-established inventory management is therefore crucial for SMEs to not only survive but also find opportunities for growth. However, Orobia et al. (2020) demonstrate that most SMEs fail to fulfil customer orders on time due to inventory management challenges, particularly supply chain disruptions and inventory shortage. The prospect of strategic application of the AT principles is to provide SMEs with real-time visibility into supply chain operations and also support informed supply chain decisions.
Vandchali et al (2021) assert that uncertainty and adverse events in supply chain networks of SMEs are also a result of complexities in supplier relationship management stemming from various issues such as quality of shipments, delayed shipments, and on-time billing and payment. Unlike large corporations, SMEs have little or no leverage over their suppliers, making it more challenging for them to negotiate effectively (Cathcart et al., 2020; de Goeji et al., 2021)). On the other hand, most suppliers expect payments upfront or upon delivery and sometimes they raise concerns about the promptness in payments. Such issues gets more complicated for SMEs that do not have inventory tracking systems. It becomes a point of conflict for both the SME and the supplier. According to Apeji & Sunmola (2022), this challenge can be resolved by strategically optimizing supply chain networks using management software to improve visibility and provide real-time information about the products being delivered as well as the payment status. SMEs and their suppliers can enhance order precision and forecasting of lead times through the application of AGT principles in supply chain optimization.
2.2 Algorithmic Game Theory in Supply Chain Management
The proliferation of the Internet and the widespread acceptance and adoption of e-commerce has changed SMEs’ relationship with computers and supply chain dynamics. Nasution et al. (2021) asserts that over the last decade, the primary role of computers and the Internet evolved from stand-alone, well-understood machines to a conduit for commerce and global communication. The algorithms and complexity theory community has formulated innovative theoretical frameworks and analytical methods tailored to the demands of contemporary operations. A transformative theoretical framework in the realm of algorithms and complexity theory is the Algorithmic Game Theory (AGT). Sohrabi & Azgomi (2020) affirm that AGT is the science of strategy that involves optimal decision-making of competing and independent factors in a strategic setting.
SMEs can apply the AGT frameworks to lay out various situations and predict the most likely outcomes. In order to understand the relevance and applicability of AGT in optimizing supply chain networks, it is vital to understand the underlying mechanisms of this framework. Essentially, the AGT aims to illuminate how the strategic actions of two or more players in any given system using a predefined set off rules and outcome (Chalkiadakis et al., 2022). The core tenet of this theory is that the payoff of one of the players is contingent on the strategy or course of action enforced by the other player. Graf et al. (2022) emphasize that the outcome of the AGT theory is referred to as the Nash Equilibrium, and once the equilibrium has been achieved, no player can alter the payoff by unilaterally changing decisions. It is worth noting that in the context of supply chain networks, there can be more than one Nash equilibrium because more complex elements than two players and two choices are involved.
Establishing a resilient supply chain network is intricately tied to effective decision-making. According to AGT, decisions within supply chain networks should be viewed as integral component of decision-making, bolstered by organizational and control procedures. The purpose of this theoretical framework is to address key challenges in SMEs’ supply chains, emphasizing nodal and environmental factors affecting internal production potential. Essentially, the decision-making involves a sequence of strategic actions that demand flexibility in response to the dynamic intricacies of the logistics environment. When executed effectively, these decisions mitigate threats and the risk of task and process non-performance, thereby guaranteeing the efficient execution of operations.
Bag et al. (2022) and Rzeczycki (2022) explain how AGT divides decisions related to strategic optimization of supply chain networks into two groups. The very first category revolves around the selection of products-markets which are most significant to logistics services. The selection of product markets is about establishing the effectiveness of vertical integration in supply chain management, and therefore clearly defining to and from which point each player in the supply chain is dedicated to both procurement, distribution, and logistics (Rzeczycki, 2022). It is worth noting that vertical integration is the strategic expansion of a small or medium-sized enterprise (SME) across the supply chain spectrum. By departing from a singular focus within the chain, SMEs can adopt vertical integration to increase self-reliance across diverse process components (Zaridis et al., 2021). This might involve the SME taking charge of product sourcing or directly engaging with consumers in the sales process. If the entire process is managed by the in-house, vertical integration of logistics systems is poised to lower transportation costs, reduce turnaround times, and provide direct control over the materials used through the manufacturing line. Vertical integration results into long-term saving due to minimal supply chain disruptions, favorable pricing, and greater control over the products.
The second group of AGT-enabled supply chain strategic decisions concerns the relationship between “SMEs’ logistic costs” and “customer service standards.” This relationship is directly related to the methods that SMEs adopt in their endeavors to gain competitve advantage (Rzeczycki, 2022). According to the postulates of AGT, formulation, implementation, and evolution of supply chain strategies encompass numerous interconnected decision-making processes. Soni et al., (2022) affirm that optimal decision-making empowers SMEs to enhance the efficiency of their supply chains, forecast the demand stream, select strategic suppliers of products, and select the best-suited structure of sale.
Overall, AGT recognizes that the entities within SMEs supply chain networks operate strategically instead of static and non-strategic behavior. The main objective of strategic optimizing of supply chain networks is to maximize the SMEs’ utility since decision-making is dynamic and interconnected (Baloch & Rashid, 2022). This calls for the respective SMEs to continuously adapt their strategies in response to changing circumstances. Regardless of the favorable aspects, the computational complexity of solving AGT models, especially in global supply chain networks remains major concern. The complex linkages and interactions between SMEs and suppliers on a global scale may lead to higher operational costs, delayed deliveries, and lack of knowledge transfer.
2.3 Managing Uncertainty and Adverse Events in Supply Chains
SMEs’ supply chain networks are subject to adverse events and uncertainties because the relationships between parties in the networks can be independent, dependent, or even interdependent, with different actions and decisions that are subject to influences from within the system or from the external environment (Sopha et al., 2021). The complexity in the supply chains are also attributed to globalization and huge supplier base, with a lot of information being exchanged between the different entities involved in the supply chain networks. Amidst the complexities, uncertainties, and adverse events affecting supply chain networks, AGT emerges as a promising framework.
Firstly, AGT models incorporate uncertainty parameters related to adverse events. Uncertainty means changes in profitability and fulfillment caused by unpredictable events and the difficulties in making decisions when there is ambiguousness in the supply chain. In the realm of AGT, uncertainty quantification is the science of quantitative characterization and estimation of uncertainties both in real world and computational applications (Abdar et al., 2021). The integration of uncertainty parameters in supply chain networks aims at determining how likely certain outcomes if some aspects of the supply chain network are not definite. By incorporating uncertainty parameters in decision making, SMEs can factor in a quantitative representation of the impact of adverse events and adapt strategies that are dynamic enough to cater to unforeseen circumstances.
AGT frameworks also makes use of theoretical approaches for risk mitigation. The most prominent theoretical approach based on the AGT framework is the Stackelberg model. In AGT terms, the players of Stackelberg model are a leader and a follower who are in constant competition for quantity, profits, and competitve edge (Kabaivanov and Zlatanov, 2021). The core tenet of this model is that the leader must have prior knowledge of what the follower plans to execute as the course of action. The Stackelberg is only viable when one of the parties have a competitve advantage enabling them to initiate a market disruption as well as the subsequent actions. This makes supply chain dynamics to assume a “leader-follower” game because the leader moves first and then the follower firm move sequentially. Through careful planning, SMEs can leverage AGT’s Stackelberg model by taking on the role of the leader. This places the SME in a position where they can make proactive decisions in anticipation of adverse events.
Gaps in Current Literature
The information provided by exiting literature provides a foundation of knowledge on the topic. The existing literature allows us to gain familiarity with the current knowledge on the application of AGT frameworks to enhance strategic optimization in supply chain networks of SMEs. However, a cross examination of the existing studies reveal questions that remain unaddressed.
3.1 Social and Environmental Considerations
Supply chain optimization in SMEs a not just about economic gains; it is a powerful force for driving positive social impact and environmental stewardship. However, most research endeavors, including Gao et al. (2022), investigate the applicability of AGT frameworks in the supply chain ecosystems to ensure profitability of the involved parties. The participants within the supply chain network are incentivized to act in ways that maximize profits and ensure security of operations. While the focus of this study is centered on economic viability, it neglects the environmental and social impacts of strategic decisions may lead to suboptimal outcomes.
In a parallel manner, Sun et al. (2020) uses AGT’s evolutionary game method to analyze the decision-making of SMEs in terms of supply chain finance. Like other articles, Sun et al. (2020) aims to reduce economic downturns in supply chain networks by analyzing credit and operational risks of financial institutions and, ultimately, improve their profit margins. Based on the above-mentioned gaps in research, future studies should prioritize sustainable practices, promote diversity, and fostering community engagement when investigating AGT models to provide insights that SMEs can use to create a more equitable and inclusive society. In light of this, future research should incorporate CSR, ethical considerations, and sustainable practices into AGT models for SMEs.
3.2 Limited Application in SMEs
Most studies also focus on the dynamics of AGT models in strategic optimization of supply chain models for large-scale organizations. Consequently, there are limited studies that focus on the specific needs of SMEs within the AGT framework. This is the case despite the fact that SMEs represent more than 50% of employment worldwide and approximately 90% of businesses. A perfect example of insufficient focus on the unique challenge faced by SMEs is Tokgöz et al. (2022). This study investigates supply chain design problems in final demands for the end products produced by a set of large-scale manufacturers. Organizational decisions within the network are collectively made by manufacturers who pool resources in a cooperative manner. These manufacturers actively shape strategic factors for retailers and exercise control over conditions impacting profit allocation in the supply chain network, including super additivity, fairness, and price stability. Similarly, Bestebreur (2020) examines the supply chain dynamics in industries with large social and environmental impact, delving into the shortcomings in transparency that influence decision-making in large-scale firms. This depicts how current research on the applicability of AGT focuses on strategic optimization in large industries, while overlooking its relevance in SMEs. This raises concerns about the generalizability of findings across different organizational scales.
4.0 Opportunities for Future Research
4.1 Tailoring AGT models for SMEs
As of today, there around 400 million SMEs across the globe. The trend is on the rise and it is projected that the number will grow to 600 million by 2030. These statistics depict how SMEs hold a prominent position in the global economy, constituting approximately 90% of businesses worldwide. Besides contributing a significant percentage of the global business, SMEs also contribute to more than 50% of total employment across the globe (Albaz et al., 2020). Not only that, a recent survey by McKinsey & Company found out that 29% of employees have plans to set up small and medium business in an attempt to create multiple sources of income. To harness the full potential of AGT in the context of SMEs, suture research should focus on developing models that align with the specific needs and constraints faced by SMEs. The following are critical areas in which future research should explore further: adaptation to limited resources, behavioral considerations of SME decision-makers, collaborative decision-making, and developing AGT models that are flexible and scalable.
4.2 Integration of Emerging Technologies
SMEs are the backbone of the global economy as they are the most contributors to global economic development and job creation. In terms of their financial standing, SMEs represent more than 50% of employment worldwide and approximately 90% of businesses (Albaz et al., 2020). Taking their significance into consideration, it is vital to acknowledge that SMEs must embrace cutting-edge technologies. Day after day, year after year, technology is advancing, bringing about multiple way to solve the usual traditional challenges and navigate the ever-evolving dynamics of supply chain networks. In addition, markets today are becoming more competitve than ever and adopting and implementing technology is the bare minimum for differentiation and staying relevant in the market. Future research should therefore invest in studying the integration of cutting-edge technologies to augment the capabilities of AGT models. Potential domains that require additional research is needed include investigating the adoption and deployment of blockchain technology to bolster supply chain transactions within AGT frameworks, exploring how IoT can be integrated into AGT models to provide real-time data, and addressing concerns related to data security when incorporating emerging technologies.
Conclusion
The present review provided a comprehensive understanding of SME supply chain dynamics, delving into the application of Algorithmic Game Theory (AGT) approach to managing uncertainty and adverse events in SMEs. The common causes of adverse events and uncertainties in SMEs supply chain networks include in consistencies with data quality, inventory management challenges, and complexities in supplier relationship management. These challenges can be addressed by effectively adopting and implementing AGT frameworks, such as uncertainty quantification and Stackelberg model, to enhance strategic decision-making. As the business landscape and SMEs continue to evolve, future research endeavors should be dedicated to refining AGT models for SMEs and exploring the synergies between AGT and emerging technologies.
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