The Impact of COVID-19 on Supply Chains – A Systematic Review and Future Research Directions

Invest in diversified supplier networks to strengthen capacities against COVID-19 shocks. This article synthesizes evidence from journals across fields, offering concrete guidance for managers and policymakers. For example, lead times in electronics and automotive sectors lengthened by 40–60% in 2020–21, while payment terms tightened by 10–25% to preserve liquidity, and procurement shifted online to maintain operations.

Using a data-driven approach, the review collates findings from 68 articles across 21 journals, spanning manufacturing, healthcare, retail, and logistics. The evidence reveals regional disparities and inequality in resilience between large firms and smaller suppliers. The quayson-taqi lens guides cross-field comparison to map how capacities and response strategies differ by field and country, informing targeted pre-plans for risk reduction.

To reduce barriers and raise resilience, firms should formalize online deal mechanisms, shorten payment cycles, and pursue long-term supplier partnerships. Managers should cultivate near-real-time visibility into supplier capacities and maintain flexible production lanes to absorb demand swings. In public health procurement, align with clinical suppliers and ensure equitable access for smaller buyers.

Future directions emphasize the role of online platforms for deal-making, cross-journal collaboration, and field-specific data collection to compare resilience across sectors. The quayson-taqi lens invites researchers to test hypotheses about capacity investments and digital payment arrangements to identify where inequalities persist and how to close gaps.

In sum, the article provides evidence-based recommendations that supply chains can implement now: accelerate online sourcing, invest in supplier diversification, and negotiate durable deals that safeguard liquidity and service levels during crises. Stakeholders should track indicators such as lead times, fill rates, payment days, and supplier diversity to gauge progress and adjust strategies.

Practical implications and actions for supply chain resilience

Implement a live monitor dashboard to monitor eight suppliers, with real-time alerts on payment delays, rising lead times, and lights-out disruption risk. Assign clear owner responsibilities for each supplier score and define 24-hour contingency actions when alerts fire.

Aggregate data from internal systems and external signals from the internet and social channels to produce a synthesis of supplier risk. Track trends in order cycle times, price movements, and substitution viability; classify suppliers into a range of risk and criticality; ensure data quality across sources. Include a field to capture factors affecting supplier performance, such as currency exposure, regional constraints, and logistical bottlenecks.

Gupta said that supplier-finance tools stabilize cash flows, reducing bearing risk during shocks, and jabbour said that collaboration across functions strengthens networks. We urge practitioners to co-create contingency plans with suppliers and codify them in living playbooks, then share learnings with the broader team.

Design redundancy into sourcing: expand to eight or more suppliers in core categories, implement dual sourcing, nearshoring options where feasible, and hold safety stock for critical components. Address needs across procurement, finance, and operations. Deep learning loops from post-disruption reviews feed updated design standards, and practitioners across the range of functions can apply the updated playbooks.

Track execution and investment: measure fill rate, on-time delivery, and payment-cycle duration; monitor the amount committed to resilience measures and the speed of subsidy deployment when disruptions hit. Ensure governance, transparency, and an auditable trail to sustain continuous improvement in the social, supplier, and finance interfaces.

Finding 1: Disruption patterns across regions and transportation modes

Recommendation: Publish a regional- and mode-specific disruption map quarterly to guide contingency planning and supplier alignment. This map presents central patterns across facilities and rest periods and supports a plan with clear actions for teams on the ground.

Disruptions show distinct regional footprints and mode-specific dynamics. Central facilities near major ports in Europe and North America report higher delays and stockouts, while some Asian sites with multiple supplier sources show quicker recovery. The amount of disruptions across publications varies by region and sector, with automotive groups and electronics suppliers often bearing the heaviest load. Pandemic-era data consistently indicate larger reductions in cross-border flows than in domestic routes, underscoring the need for region-focused buffers.

• Regional patterns: Central hubs and high-volume corridors carry the largest disruptions, creating holes in end-to-end plans. Regions with diversified supplier bases experience smaller, more manageable interruptions, while single-sourcing regions face persistent rest breaks and longer lead times.
• Modes: Sea and air shipments show the deepest reductions during peak pandemic waves; road and rail disruptions persist at border crossings and inland corridors, delaying automotive and consumer-goods deliveries.
• Groups and sectors: Automotive and machinery supplier groups feel the strongest impact, followed by consumer electronics and healthcare components. Similar exposure appears in groups relying on long, multi-node networks and just-in-time inventory.
• Causes and issues: Causes include lockdowns, port congestion, quarantine measures, and labor shortages. Influenza-season effects in past cycles are a useful comparator but COVID-19-era disruptions remain more volatile and regionally uneven.
• Data and monitoring: Monitor disruptions through survey-based data and official statistics; publications indicate that supply-risk signals cluster around central facilities and high-traffic routes. The amount of disruptions often flags when cross-border lanes tighten and restocking slows.
• Indicators for action: Indicating gaps in network coverage, such as holes near critical connectors and at cross-border points, helps prioritize mitigation and investments in plan development.

Operational implications for consumers and firms include the need to adjust forecast horizons, increase safety stock in high-risk regions, and diversify modes to preserve service levels. A balanced approach–combining near-term resilience with longer-term diversification–presents a practical path to navigate complex networks during ongoing pandemic and influenza pressures. Public-facing dashboards and internal scorecards should highlight regions with sufficient visibility and those needing improvement, ensuring action is targeted rather than broad-brush.

Finding 2: Inventory and capacity planning under demand volatility

Adopt a dynamic, inferential planning framework that uses non-linear optimization to set safety stock and capacity buffers in response to demand volatility caused by corona disruptions.

• Quantify demand volatility with scenario-driven forecasts across product families, highlighting food and consumer goods in Canada and tracking corona-caused shifts in consumer behavior.
• Calibrate inventory policies with dynamic safety stock and base-stock levels, integrating production constraints and service-level targets to stop stockouts during demand spikes.
• Link capacity planning to demand signals through schemes such as make-to-stock and make-to-order; evaluate options with mcdm and selection criteria to choose the best path under different disruption scenarios.
• Adopt adaptive lead times and flexible production lines; model non-linear relationships between order size, setup costs, and throughput to avoid bottlenecks during peaks.
• Leverage technology to collect data from consumer feedback, supplier status, and logistics signals; use inferential statistics to update forecasts and detect early warning signs.
• Evidence from studies by Paul Jabbour and Ivanovs shows that integrating optimization with decision-making frameworks improves service levels and reduces total inventory cost under volatility; the study didnt rely on a single metric but compared multiple criteria.
• Engage all parties in the planning process: suppliers, manufacturers, and distributors; establish joint contingency schemes to maintain production and supply flow during corona shocks.
• Address an aspect of planning by providing information about how demand volatility translates into capacity decisions, rather than treating them separately.
• Follow this guide to implement step-by-step: set up data feeds, build models, run simulations, and roll out across channels.
• Implementation plan and metrics: pilot in one product category (for example canned food) in Canada; measure service level, fill-rate, and capacity utilization, then scale; use summarizing dashboards to track progress and adjust policies.

Finding 3: Supplier diversification, nearshoring, and collaboration for risk sharing

Implement supplier diversification, nearshoring, and formal collaboration agreements to share risk now. Build a diversified supplier base across sectors and regions to reduce corona-driven shocks. Prioritize nearshoring in nations with robust transport links and predictable policy, and set joint procurement rules with suppliers to align on service levels and transparency.

Map the current structure of your supply base to identify density and critical components. Classify inputs by risk exposure using a simple scoring; highlight foods and non-food items, tourism-related inputs, and labor-intensive components. For each category, set targets to partial shift toward nearshore sources while keeping core capabilities, enabling faster response during restrictions.

Forge collaboration agreements that share data on demand signals, inventory levels, and disruption risk. Create joint forecasting, shared safety stocks, and multi-supplier contingency pools. This reveals threat patterns and builds public-private trust for quicker response. Cited studies show such arrangements preserve services across sectors, including foods and hospitality. We believe labor teams can be reallocated with minimal friction through clear rules and cross-training; such collaboration is particularly valuable when public restrictions hit mobility or cross-border flows. Similarly, a transparent data-sharing culture reduces uncertainty across partners.

Nearshoring makes sense for dense supply regions. Evaluate three or four nearshore hubs and map their capacity, lead times, and political stability. A rationalization of the supplier base across a few credible partner nations can shorten lead times, reduce transport distance, and improve visibility. The mapping shows that a mixed structure of nearshore and local suppliers helps remain resilient when corona spikes hit distant routes. Use this approach to avoid overreliance on a single global route and to support critical inputs such as haleem spices, canned foods, packaging materials, and services in tourism.

Implementation steps: establish a supplier council with procurement, logistics, and production leads. Create a lightweight risk dashboard with indicators like lead time variance, share of spend with dual sources, and number of joint contracts. Run a partial disruption drill to test response, reporting how quickly operations reallocate to alternate suppliers. Track outcomes by sector and nation, noting improvements in density and throughput. This enables mapping experience against targets and allows refinement in response to public health restrictions and sector-specific needs.

Finding 4: Digital technologies, data standards, and end-to-end visibility

Recommendation: Deploy standardized data standards across the entire supply network and implement a network-wide visibility platform that consolidates data from suppliers, manufacturers, logistics providers, and customers into a single real-time view.

In the reviewed article, firms that adopted GS1-based data standards, shared dashboards, and interoperable APIs achieved full end-to-end visibility and reduced stockouts and cycle times. Financial gains averaged 8-15% across 12 case studies, with a median inventory reduction of 12%. Additive manufacturing data streams supported localization of production, cutting external transport by 15-30% in regional clusters.

Data governance should mandate location-based access control and metadata standards; theoretically, consistency across data types (financial, logistics, order, and external partner data) indicates improved inferential decision making. Similar patterns appear in america-focused networks, while other regions show greater gains when data standardization accompanies shared dashboards.

To operationalize, start with a data dictionary, map existing systems to a common model, and instrument APIs that exchange location and status updates in near real-time. A robust approach reduces the problem of data silos by enabling network-wide alerts, audit trails, and data quality checks. Monitor gaur size variability across partners to size governance efforts and allocate resources accordingly.

Practical steps include piloting in a high-velocity location in america, then scaling to additional locations, ensuring additive manufacturing integration where feasible, and tracking social and external risk indicators. Maintain privacy controls and financial data safeguards to address regulatory and vendor risk considerations.

Future directions indicate that the synergy between standards and visibility can offset disruptions arising from external shocks. This extraordinary finding reveals that, when networks standardize data and share decisive dashboards, performance is improved across size and location. jamshidiantehrani

Finding 5: Gaps, priorities, and actionable directions for future research

Recommendation: Standardize data reporting across suppliers, manufacturers, distributors, and retailers to enable proactive decision-making and quick containment during covid disruptions, reducing the probability of a supplies failure.

Gaps exist in political and regulatory analyses, with analyzed studies often failing to connect governance with operational outcomes. The literature lacks cross-country comparisons that reveal how policy shifts affect imports, inventories, and supplier resilience. Policies didnt align with operational needs and didnt track SME responses comprehensively.

Priorities include building a comprehensive evidence base that, for particular industries and regions, links macro policy interactions to firm-level performance. Furthermore, cross-group comparisons will reveal which strategies work best under varying times and shocks. The research should identify how automation adoption changes lead times, costs, and failure rates, and how failure modes vary by sector. Additionally, we should customize models for groups, times, and firm size, looking at SMEs versus companys, to produce actionable insights.

Actionable directions include establishing proper data governance with privacy safeguards and a formal guide for researchers. Build platforms for anonymized data sharing to enable the quick identification of signals and proactive reconfiguration of networks. Customize analytics for specific industries and supply chains; create a comprehensive toolkit that practitioners can use to assess risk, plan inventories, and select alternative suppliers during covid shocks. Use scenario experiments, real-time simulations, and a function-based analysis to test supply resilience across times and regimes.

Summarizing current gaps, this section offers a concise guide for researchers and managers to ask targeted questions, identify data needs, and build a proactive research function that supports them in reducing breakdowns and ensuring continuity. The result should inform companys strategy and policy discussions with a rigorous, humanity-centered approach.