Digital-Era Resilience: Navigating Logistics and Supply Chain Operations after COVID-19
Abstract
:1. Introduction
2. COVID-19 and Disruptions
3. COVID-19 and Supply Chain Resilience
3.1. Vulnerabilities, Flexibility, and Resilience in the Pandemic
3.2. Fluctuating Demand and Disruptions in the Pandemic
3.3. Production Downtime, Automation, and Digitization in Supply Chains
3.4. COVID-19 Disruptions and Digital Technologies
- Healthcare emerges at the forefront with 60% regional supply chain coverage, demonstrating adaptability in the face of challenges;
- The automotive, aerospace, and defense sectors, despite prioritizing resilience, lag significantly at 22%;
- Chemical and commodity industries exhibit minimal change in their supply chains with variations arising from sector-specific challenges, such as resource intensity and supplier issues;
- The COVID-19 pandemic has made supply chain risk management a top priority for companies, with 95% adopting formal processes;
- Although 59% implemented new risk management practices, effectiveness varied based on pre-existing risk management maturity;
- Supplier risk monitoring has emerged as a primary focus, although blind spots remain, particularly beyond tier-one suppliers. In terms of supply chain planning, 58% reported better performance, linked to the use of advanced analytics tools.
4. Methodology
4.1. Information Sources
4.2. Search Terms
4.3. Inclusion and Exclusion Criteria
4.3.1. Inclusion Criteria
- Studies conducted from January 2019 onwards;
- Research focusing on the impact of the COVID-19 pandemic on logistics and supply chain activities;
- Studies that investigate strategies and practices to enhance resilience during the pandemic.
4.3.2. Exclusion Criteria
- Studies not published in English;
- Studies with insufficient data or inadequate methodology;
- Duplicates or redundant publications.
4.4. Paradigm of Systematic Review
4.4.1. Identification
4.4.2. Screening
4.4.3. Eligibility
4.4.4. Inclusion
5. Result
6. Discussion
6.1. Major Disruptions in Supply Chain
6.2. Post-Pandemic Resilience and Approaches toward Digitization
6.3. The Role of Internet of Things (IoT) and Artificial Intelligence (AI) in Supply Chain Strategies for Post-COVID-19 Impact
7. Theoretical and Managerial Implications
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Types of Crises and Disruptions | Characteristics |
---|---|
Poverty |
|
Unemployment |
|
Economic crisis |
|
Financial crisis |
|
Environmental crisis |
|
International crisis |
|
Informational |
|
Physical |
|
Human resources |
|
Reputation |
|
(a): Adopting Flexible Production Models [46,47,48,49,50,51] | |
Facts |
|
Real Instances |
|
Post-COVID-19 Consequences |
|
(b): Investing in Digital Supply Chain Technologies [52,53,54,55] | |
Facts | Digital technologies like the IoT, AI, blockchain, and data analytics have become essential to monitor and optimize supply chain operations for improved visibility, efficiency, and resilience in supply chains. |
Real Instances |
|
Post-COVID-19 Consequences |
|
(c): Diversifying Suppliers [54,56,57,58,59,60,61] | |
Facts |
|
Real Instances |
|
Post-COVID-19 Consequences |
|
(a) | |
IoT |
|
Blockchain |
|
AI |
|
(b) | |
IoT |
|
Blockchain |
|
AI |
|
(c) | |
IoT |
|
Blockchain |
|
AI |
|
Keywords/Search String | Search Engine | No. of Papers | Inclusion and Exclusion Parameters |
---|---|---|---|
“COVID19” and “supply chain | Google Scholar | 2310 | In the Title Year: Any time |
“COVID19” and “supply chain” and “logistics” | Google Scholar | 86 | In the Title Year: Any time |
“COVID19” and “supply chain” and “disruption” | Google Scholar | 168 | In the Title Year: Any time |
“COVID19” and “supply chain” | PubMed | 209 | In the title of the article Year: Any time |
“COVID19” and “supply chain” AND “logistics” | Scopus | 35 | In the title of the article |
“COVID19” and “supply chain” | Semantic Scholar | 1000 | In the title of the article |
“COVID19” and “supply chain” | OpenAlex | 1000 | In the title of the article |
Year | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|
No. of Papers | 3 | 843 | 1157 | 771 | 395 |
Publication Year | Papers | Citations | Cites/Year | Cites/Paper | Author/ Paper | h-Index | g-Index | hA-Index |
---|---|---|---|---|---|---|---|---|
2019–2023 | 3169 | 149,798 | 37,449.50 | 47.27 | 3.23 | 169 | 284 | 107 |
Aspect | Description |
Disruption Point |
|
Industry Impact |
|
Global Trade |
|
Demand Fluctuations |
|
Inventory Challenges |
|
Supplier Relationships |
|
Technology Integration |
|
Government Interventions |
|
Communication Breakdowns |
|
Workforce Challenges |
|
Transportation Issues |
|
Risk Management |
|
Financial Impacts |
|
Sustainability |
|
Lessons Learned |
|
Aspect | IoT in Supply Chain Strategies | AI in Supply Chain Strategies |
---|---|---|
Data Collection | Sensors and devices collect data from physical assets, products, and transportation [15]. | Analyzes data from various sources, such as historical data, customer trends, and market dynamics [14]. |
Real-time Visibility | Provides real-time visibility into the location, condition, and performance of assets and products [80]. | Predictive analytics and machine learning enable real-time forecasting, demand prediction, and inventory optimization [38]. |
Inventory Management | Enables RFID and GPS technologies to track inventory, reducing stock-outs and excess inventory [9]. | Optimizes inventory levels, identifies demand fluctuations, and minimizes holding costs [66]. |
Predictive Maintenance | Monitors machinery and equipment conditions, allowing proactive maintenance to prevent breakdowns [41]. | Utilizes AI algorithms to predict equipment failures and schedule maintenance based on usage and data analysis [42]. |
Supplier Collaboration | Facilitates communication between suppliers and manufacturers for efficient order management [19]. | Enhances supplier collaboration by automating procurement and optimizing supplier relationships [66]. |
Route Optimization | Optimizes transportation routes for cost reduction and timely delivery [9]. | AI-driven route optimization considers multiple variables like traffic, weather, and fuel costs [52]. |
Demand Forecasting | Provides fundamental demand insights based on historical data and current trends [4]. | Utilizes advanced statistical models and machine learning to predict demand accurately [46]. |
Risk Management | Offers limited risk assessment capabilities [4]. | Assesses supply chain risks in real-time, adapting strategies for changing circumstances [66]. |
Post-COVID-19 Impact | Strengthens the need for end-to-end visibility, flexibility, and resilience [15]. | Accelerates adoption to address the supply chain disruptions and increased demand for e-commerce [66]. |
Real-World Examples | Amazon’s smart shelves use the IoT to track inventory levels, ensuring products are always available for customers. The IoT is widely used in logistics and warehousing [41]. | DHL, a global logistics company, employs AI to optimize delivery routes, reducing costs and environmental impact [81,82]. |
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Share and Cite
Kashem, M.A.; Shamsuddoha, M.; Nasir, T. Digital-Era Resilience: Navigating Logistics and Supply Chain Operations after COVID-19. Businesses 2024, 4, 1-17. https://doi.org/10.3390/businesses4010001
Kashem MA, Shamsuddoha M, Nasir T. Digital-Era Resilience: Navigating Logistics and Supply Chain Operations after COVID-19. Businesses. 2024; 4(1):1-17. https://doi.org/10.3390/businesses4010001
Chicago/Turabian StyleKashem, Mohammad Abul, Mohammad Shamsuddoha, and Tasnuba Nasir. 2024. "Digital-Era Resilience: Navigating Logistics and Supply Chain Operations after COVID-19" Businesses 4, no. 1: 1-17. https://doi.org/10.3390/businesses4010001
APA StyleKashem, M. A., Shamsuddoha, M., & Nasir, T. (2024). Digital-Era Resilience: Navigating Logistics and Supply Chain Operations after COVID-19. Businesses, 4(1), 1-17. https://doi.org/10.3390/businesses4010001