The global supply chain landscape has transformed due to the COVID-19 pandemic. To adjust to the new normal, recent research has also been actively examining several aspects of supply chain management, such as reverse logistics to design and plan shorter supply chains for significant objects, as for resilience in logistics and supply chain operations ^[1][2][1,2]. Furthermore, many experts have investigated the many aspects and sub-aspects of the supply chain’s resilience, emphasizing the significance of organizational capability, cooperation, adaptability, and humanitarian values [3]. Thus, integrating supply chain management techniques to improve operational performance is becoming more and more important due to this pandemic. Hence, post-pandemic research has highlighted the need for supply chain resilience to build more robust and adaptive supply networks that can withstand disruptions ^[4][5][4,5] and explored ways to improve resilience, including dual sourcing, demand forecasting models, and technology adoption [6].

Globally, supply chains have been disrupted by the COVID-19 pandemic, for example, industrial facility closures, restricted travel and labor shortages, and widespread delays and shortages in several businesses [7]. These disruptions have made clear how important it is to have flexible, agile supply networks to adjust to unexpected difficulties [8]. Hence, with digitization, new metrics and models are being developed to assess the resilience of supply networks to unexpected shocks, protect product availability, and reduce production downtime [9]. In this case, research explores how artificial intelligence, blockchain, and Internet of Things (IoT) applications can improve real-time visibility, risk management, and decision making during disruptions [10]. Similarly, according to research, businesses use data analytics and digital technologies more frequently to strengthen their supply chains. However, these tools allow for real-time monitoring, demand forecasting, and inventory optimization [11]. Additionally, these technologies enable companies to make more informed decisions, respond faster to disruptions, and improve overall supply chain efficiency [12]. Hence, this digital transformation trend will probably continue to shape the logistics landscape in the post-pandemic world.

Furthermore, the pandemic accelerated the localization of supply chains near-shore [13]. For example, business organizations are rethinking their reliance on outside suppliers and exploring local alternatives [14] to shorten lead times and strengthen supply chains [15]. However, academics have looked at the operational and economic implications of the trend, including potential costs [16]. For example, supply chain participants are working together to improve reverse logistics methods in the wake of COVID-19 and to increase efficiency and sustainability through alliances between retailers, manufacturers, and logistics companies ^[17][18][17,18]. In a sense, this collaborative strategy increases trust and helps to manage risk by minimizing the impact of any disruption through improved communication and collaboration across the supply chain using digitization [19].

2. COVID-19 and Disruptions

The supply chain landscape faces multifaceted challenges, including production disruptions, industry-specific impacts, and global trade complexity [20]. Sudden demand changes and inventory dilemmas further complicate matters, while supplier relationships are tested with dependencies and bankruptcies [21]. The crisis prompted accelerated technology adoption and revealed the important role of government intervention [22]. Communication disconnects and workforce challenges add layers of difficulty, in addition to transportation issues and financial implications [23]. Among these challenges, the spotlight on sustainability and resilience prompts strategic reflection, emphasizes the importance of risk management, and gathers valuable lessons for future preparedness. Therefore, the COVID-19 pandemic triggered a profound impact on supply chains, exacerbating poverty in underdeveloped countries and fostering challenges like malnutrition ^[24][28]. Limited resources and opportunities in these regions contribute to widespread malnutrition, hindering individual and social progress ^[25][29]. The pandemic-induced economic downturn led to increased unemployment, creating financial hardships that extend beyond individuals to affect families and communities ^[26][30]. Navigating these challenges requires building resilience and fostering inclusion to mitigate the long-term consequences on communities ^[27][31]. The resulting economic crisis, marked by recessions and social upheavals, intensifies class conflicts and underscores the need for comprehensive strategies to address the multifaceted impact on society.

3. COVID-19 and Supply Chain Resilience

3.1. Vulnerabilities, Flexibility, and Resilience in the Pandemic

The post-COVID-19 environment has significantly changed how organizations respond to unexpected shocks. In this case, the pandemic provided a profound reminder of the value of flexibility and resilience ^[28][32]. Nowadays, businesses place a lot of weight on risk mitigation and backup planning, which ranges from labor shortages to supply chain disruptions ^[29][33]. Organizations are increasingly prioritizing the ability to manage unexpected events in an era characterized by increased complexity and unpredictability in the business landscape ^[30][34]. However, developing flexibility involves diversifying supplier networks, implementing multi-modal transportation options, and increasing close collaboration with suppliers to enhance responsiveness to unexpected changes ^[31][35]. 

3.2. Fluctuating Demand and Disruptions in the Pandemic

After COVID-19, product availability has also changed. The pandemic highlighted the importance of local sourcing and revealed the vulnerability of global supply systems ^[32][36]. Firms prefer more decentralized strategies after reassessing their reliance on single-source suppliers ^[33][37]. Literally, effective inventory control and data-driven forecasting are now essential to adjust to changing demand trends and reduce the possibility of overstocking or stock-out ^[34][38]. This is especially important for areas where customer demand changes or disruptions can lead to massive product shortages [7]. Experts suggest investigating improved inventory management techniques, demand forecasting, and predictive analytics to ensure that items are regularly available to customers ^[35][39]. As a result, the primary goal of recent research should be to guarantee product availability.

3.3. Production Downtime, Automation, and Digitization in Supply Chains

Reducing production downtime is the subject of the most current research in order to maintain operational effectiveness and competitiveness [8]. Predictive maintenance strategies, condition monitoring, and real-time data analysis are the key areas of study in this field, since automation and just-in-time manufacturing procedures are becoming more and more dependent on them ^[34][38]. Moreover, real-time monitoring and self-diagnosis in industrial settings are made possible by the advent of Industry 4.0 technologies, such as artificial intelligence (AI) and the Internet of Things (IoT) ^[36][42]. On the other hand, reducing manufacturing delays has become a top priority for companies operating in the aftermath of the epidemic [8]. Predictive maintenance and remote monitoring solutions have become more critical to businesses as they realize that extended disruptions can have adverse financial effects ^[37][43]. For instance, to reduce unexpected downtime, equipment faults are detected and prevented using machine learning algorithms and IoT technologies ^[38][44]. Likewise, to lessen the effect of unforeseen occurrences like labor shortages or lockdowns, many organizations wer

3.4. COVID-19 Disruptions and Digital Technologies

The IoT, blockchain, and AI address supply chain problems by adopting flexible production processes, investing in digital supply chain technology, and diversifying suppliers, which are summarized in Table 13:

	(a)
IoT	- Real-time tracking of supplier performance; - Demand forecasting based on market data and supplier collaboration; - Integration with supplier systems; - RFID and sensors for inventory management.
Blockchain	- Supplier transparency; - Secure supplier identity; - Immutable records of transactions; - Smart contracts for automated payments; - Enhanced trust and integrity in the supply chain.
AI	- Predictive analytics for supplier selection; - Supplier risk assessment; - Market analysis for identifying new suppliers.
	(b)
IoT	- Enhanced visibility into supply chain operations; - Improved demand forecasting through AI; - Real-time monitoring and alerting for supply chain disruptions; - Streamlined communication with suppliers and customers; - Inventory optimization through AI algorithms.
Blockchain	- Improved traceability of goods along the supply chain; - Reduction in fraud and counterfeiting; - Enhanced supply chain visibility and transparency; - Efficient and automated payments; - Reduced paperwork and manual processes.
AI	- Automated procurement and order management; - Real-time demand sensing; - Inventory optimization; - Data-driven decision making for investment and capacity planning; - Just-in-time production; - Adaptive manufacturing; - Dynamic routing of goods and resources.
	(c)
IoT	- Real-time data on machine performance and production processes; - Collaborative robots and automation for rapid reconfiguration; - IoT-enabled predictive maintenance for machinery and equipment; - Real-time production data analytics for efficient resource allocation.
Blockchain	- Smart contracts for automated production processes; - Enhanced collaboration between supply chain partners; - Improved quality control through blockchain-based tracking of product data; - Reduced lead times and lead time prediction; - Secure and transparent data sharing.
AI	- Real-time monitoring of production lines and equipment; - Demand-driven production adjustments based on market insights; - Dynamic scheduling and resource allocation; - Quick response to changes in demand or supply.

4. Navigating Logistics and Supply Chain Operations after COVID-19

4.1. Major Disruptions in Supply Chain

The supply chain landscape is characterized by various obstacles and challenges that significantly affect various aspects of the global industry. These disruptions include production stoppages, transportation restrictions, labor shortages, and raw material shortages ^[47][70]. The automotive industry faces production freezes and reduced demand, while electronics struggles with component shortages and pharmaceuticals face delays (Mallik, 2023). The complex web of global trade is affected by export/import restrictions, port closures, customs delays, and reduced aircraft cargo capacity ^[48][71]. Fluctuations in demand add to the complexity, with sudden increases in demand for healthcare contrasting with sharp declines in luxury goods ^[49][72]. Inventory challenges arise from stockouts due to panic buying, excess stock of nonessential products, and difficulty in inventory management ^[50][49]. Supply chain vulnerabilities are further highlighted by supplier relationship issues, such as reliance on single suppliers, supplier bankruptcies, and the need for contract re-negotiations ^[51][73]. However, the integration of technology becomes crucial, resulting in an accelerated need for digital solutions, an increased adoption of the IoT for real-time tracking, and significant growth in e-commerce ^[52][74]. Communication disconnects and a lack of real-time information plague supply chain networks, while workforce challenges include remote working difficulties, onsite worker safety concerns, and labor shortages in critical sectors ^[53][75]. Transportation problems occur with freight capacity shortages, canceled flights affecting air cargo, and container shortages and delays ^[54][76]. On the other hand, effective risk management includes identifying and mitigating supply chain risks, as well as scenario planning for future disruptions ^[55][77]. Financial impacts manifest as increased costs due to logistical challenges and revenue loss from production shutdowns ^[56][59]. 

4.2. Post-Pandemic Resilience and Approaches toward Digitization

The COVID-19 pandemic significantly changed the supply chain management landscape, forcing companies to re-evaluate and restructure their strategies ^[50][49]. In this changing environment, the integration of the Internet of Things (IoT) and artificial intelligence (AI) has played an important role in reshaping supply chain operations ^[41][63]. Additionally, IoT sensors and devices have improved visibility and tracking capabilities throughout the supply chain ^[40][62]. However, these devices can monitor product status and location in real time, allowing predictive maintenance and efficient route planning ^[57][41]. Thus, businesses have shortened lead times, improved inventory management, and become more agile in the face of disruptions in conjunction with AI [19]. A prominent application of the IoT and AI in post-COVID-19 supply chain management is predictive analytics ^[58][79]. AI algorithms can predict demand more accurately by analyzing historical data, market trends, and external factors ^[34][38]. This capability has proven invaluable as companies strive to maintain a lean inventory and reduce the risk of stock-outs or excess inventory [9]. Another key area where the IoT and AI have made significant inroads is in supply chain risk management. The pandemic exposed vulnerabilities in global supply chains, with disruptions ranging from factory shutdowns to transportation bottlenecks [7]. IoT devices can provide real-time data on potential disruptions, such as equipment failures or traffic congestion, allowing supply chain managers to take pre-emptive measures [9]. AI, on the other hand, can assist in scenario planning, helping organizations to identify and evaluate alternative suppliers or transportation routes in case of emergencies [15]. Post-COVID-19, supply chains have become more resilient and adaptive thanks to the fusion of these technologies [6]. Additionally, the COVID-19 pandemic catalyzed the adoption of the IoT and AI in supply chain management. By harnessing the IoT for real-time tracking and AI for predictive analytics and decision making, enterprises have redefined their supply chain strategies to thrive in an environment marked by ongoing disruptions and uncertainties ^[50][49]. Literally, these technologies have enabled organizations to achieve unprecedented levels of visibility, efficiency, and risk mitigation [19].

4.3. The Role of Internet of Things (IoT) and Artificial Intelligence (AI) in Supply Chain Strategies for Post-COVID-19 Impact

Global supply systems were affected by the COVID-19 epidemic, underscoring the necessity of robust, flexible, and effective supply chain policies ^[59][51]. The combination of artificial intelligence (AI) with the Internet of Things (IoT) has become more important in this setting ^[58][79]. Usually, the IoT concentrates on gathering and monitoring data in real time, whereas AI employs predictive analytics to improve decision making and optimize every supply chain step. Together, these technologies help to create a more flexible and effective supply chain ecosystem while addressing the issues brought forth by the epidemic.

5. Theoretical and Managerial Implications

The COVID-19 pandemic had a wide-ranging and multifaceted impact on supply chain and logistics operations, making a re-evaluation of current thinking and management strategies critical. This pandemic certainly brought attention to the limitations of conventional frameworks for supply chain risk management. Subsequently, supply chain resilience theories underwent changes that included not only risk acceptance and mitigation, but also adaptation through the use of dynamic and adaptive frameworks. Consequently, this theoretical model addresses how manufacturers, suppliers, and customers are interconnected and emphasizes the value of variety, redundancy, and flexibility. Given the significance of supply chain dynamics, insights developed from the multidimensional literature were incorporated into this theoretical framework to help predict and mitigate similar strategies and guidelines for the near future. The COVID-19 pandemic necessitates a reassessment of current concepts and managerial strategies due to its wide-ranging and diverse impact on supply chain and logistics operations. Undoubtedly, the pandemic has highlighted the shortcomings of traditional supply chain risk management frameworks. And then, the modification of supply chain resilience theories included not only risk acceptance and mitigation, but also adaptability through the use of dynamic and flexible frameworks. Thus, this theoretical model addressed the interconnections between suppliers, manufacturers, and customers, highlighting the importance of flexibility, redundancy, and diversity. With the importance of supply chain dynamism, this theoretical framework incorporated better insights from behavioral economics to better predict and mitigate such behavior in the near future as well. Supply chain managers wanting to bring production closer to the final consumer must diversify their supplier base to lessen their reliance on a single source through near-shoring or re-shoring. Consequently, real-time monitoring and data analytics investments are essential to watch inventory, demand, and any interruptions and enable faster responses. Once more, supply chains ought to be flexible enough to accommodate sudden distribution, transportation, and manufacturing changes in light of implementing solutions for just-in-case inventories. So, when demand exceeds supply, establishing tenacious supplier ties via cooperation and long-term collaborations may be helpful during disruptive periods and result in preferential treatment.