Open research questions in Supply Chain Resilience and Risk Management

What the literature leaves open

• These findings align with previous research [13,30], who emphasize the centrality of 3PLs in cyber risk landscapes, but also point out the limited research specifically focusing on these firms.

  Enhancing Cybersecurity in Logistics Services: A Study of Third-Party Logistics (3PL) Service Providers · 2026 · DOI

• Javad Feiz Abadi1 · David Gligor2 · Somayeh Alibakhshimotlagh3 · Dax Trejo Beltran1 Received: 15 October 2025 / Revised: 26 April 2026 / Accepted: 24 May 2026 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature…

  Reframing supply chain resilience as value creation: a business model perspective · 2026 · DOI

• Authors Enhance supply chain visibility through a (Technology–Organization– Environment) TOE-based approach to move from fragmented security practices toward a cohesive and productivity-driven CSCRM strategy. Use agency theory to align roles and responsibilities among supply chain partners, enhancing accountability and reducing cyber risk through governance, risk protocols, and incentives. Support this with capacity building and transparent communication to improve cybersecurity readiness and coordination. Embed supplier cybersecurity into logistics and procurement by using a process-driven framework, assigning clear accountability, and treating cyber risk as a core supply chain concern. (Gani and Fernando, 2024) (Firth and Srivastava, 2024) (Handfield, Earp and Sadeghi, 2025) STATISTICS IN TRANSITION new series, March 2026 69 2.6. Overview of the Moroccan automotive and aeronautical industries 2.6.1. Automotive industry Over the past decade, the Moroccan automotive industry has experienced remark- able and sustained growth, becoming the country’s leading export sector. It has created over 147,000 new jobs, attracted more than 250 companies, and established Morocco as the continent’s foremost automotive manufacturing hub (Ministry of trade and com- merce, 2023). 2.6.2. Aeronautic industry Morocco is now one of the most competitive and alluring bases on the global map of aviation construction, confirming the Kingdom’s great ambition in this high added value industry, after 20 years defined by a singular technological and human journey. Morocco is becoming a prime site and location, with 140 companies (Ministry of trade and commerce, 2023).

  The impact of Cyber Supply Chain Risk Management on Supply Chain 4.0 · 2026 · DOI

• Although the findings are promising, the study has limitations, notably an average response rate. Larger samples should be used in future research to ensure more representative and reliable results. Moreover, the research was limited in scope to samples from only two sectors: the automotive and aeronautic industries. It is recommended that this model be applied to other fields, such as textiles, agriculture, and others. Furthermore, given that this study concentrates on one specific enabling technology, subsequent research could examine a broader spectrum of technologies to validate and refine the proposed framework.

  The impact of Cyber Supply Chain Risk Management on Supply Chain 4.0 · 2026 · DOI

• Existing studies largely view supply chain finance (SCF) as a tool for easing liquidity constraints, while its role under political and institutional risk remains underexplored.

  Effect of Supply Chain Finance on Supply Chain Trade · 2026 · DOI

• Future studies should consider using longitudinal designs to trace the investments and the survival rates of the businesses in Copyright © 2026 by the authors 142 Journal of Contemporary Academic Research and Methodologies (JCARM) Volume 1 | Issue 4 | May 2026 other states and industries of Nigeria. Although the sample is geographically diverse, it is limited by the three cities in Nigeria, and may not fully reflect the experience of businesses in smaller cities or rural areas.

  Strategic Planning and Business Survival in a Volatile Economy: A Case Study of Nigeria · 2026 · DOI

• initiatives, construct systems for resilience concepts, and acknowledge and reward climate-smart inventions. These methods leverage the collective intelligence and creativity of the workforce, producing answers that may not arise from solely top-down commands.

  Improving operational resilience against climate change · 2026 · DOI

• Limited to agriculture; lacks industrial scalability Static model; weak realtime adaptability Data-intensive; high computational cost No AI integration; limited sustainability…

  Resilience assessment of global manufacturing value chains under the influence of the carbon border adjustment mechanism using machine learning and knowledge management · 2026 · DOI

• 3. Research Methodology 3.1 Research Design and Approach This study employs a sequential explanatory mixed-methods design (Creswell & Plano Clark, 2018), wherein quantitative data collection and analysis precede qualitative exploration to explain and contextualize statistical findings. This approach was selected to: (1) enable Page | 167 Humanities and Natural Sciences Journal Safa et al. June, 2026 www.hnjournal.net HNSJ Volume 7. Issue 6 systematic risk ranking across a representative sample; (2) provide depth and nuance through stakeholder perspectives; and (3) enhance validity through methodological triangulation. 3.2 Study Area and Population The research was conducted in Port Sudan city, Red Sea State, Sudan. The target population comprised construction professionals involved in materials management decisions, including contractors (site engineers, project managers, procurement officers), consultants (project supervisors, quantity surveyors, materials engineers), clients/owners (public sector representatives, private developers), and suppliers/logistics providers. 3.3 Data Collection Methods 3.3.1 Questionnaire Survey (Primary Quantitative Method) A structured questionnaire was developed based on literature review and preliminary stakeholder consultations. The instrument comprised four sections: • Section A (Demographic Information): Job position, years of experience, organization type, project type. • Section B (Risk Factor Assessment): Eighteen risk factors across four categories (procurement, logistics, storage/handling, external). Each factor rated two 5-point Likert scales: likelihood (1 = Very Unlikely to 5 = Very Likely) and impact (1 = Negligible to 5 = Catastrophic). Severity score = Likelihood × Impact (range: 1–25). • Section C (Project Performance Outcomes): Perceived impact of materials risks on cost overrun schedule delay, quality reduction, and material waste (5-point scales). • Section D (Current Practices and Mitigation): Use of formal management systems, tracking methods, perceived effectiveness; open-ended question on top mitigation strategies employed. The questionnaire was pilot-tested with 10 professionals for clarity and relevance, then refined accordingly. Distribution occurred via professional networks, industry associations, and direct site visits between January and March 2026. Of 120 questionnaires distributed, 80 completed responses were received (67% response rate), exceeding the minimum sample size of 30–50 recommended for RII-based studies (Ameyaw et al., 2012).

  Modeling and Analysis of Risk Factors Affecting Construction Materials Management in Port Sudan · 2026 · DOI

• • Policy Contribution: Informs the development of targeted interventions—such as supplier certification programs, storage infrastructure guidelines, and digital tracking adoption incentives—to enhance sectoral resilience. 1.5 Scope and Limitations The study focuses on building construction projects (residential, commercial, institutional) within Port Sudan city limits, excluding heavy civil infrastructure (roads, bridges, ports) that involve distinct supply chain dynamics. Data collection occurred between January and April 2026, capturing conditions during the third year of Port Sudan’s transformation as de facto capital. Limitations include: (1) potential response bias in self-reported survey data; (2) security constraints limiting field access and interview depth; (3) the rapidly evolving conflict context, which may alter risk profiles post-study; and (4) sample representativeness, though efforts were made to include diverse organizational types and project categories. Page | 165 Humanities and Natural Sciences Journal Safa et al. June, 2026 www.hnjournal.net HNSJ Volume 7. Issue 6 1.6 Paper Structure The remainder of this paper is organized as follows: Section 2 reviews relevant literature on construction materials management, risk identification, and conflict-zone infrastructure delivery. Section 3 details the research methodology. Section 4 presents quantitative and qualitative findings. Section 5 discusses implications and proposes a mitigation framework. Section 6 concludes with recommendations for practice and future research.

  Modeling and Analysis of Risk Factors Affecting Construction Materials Management in Port Sudan · 2026 · DOI

• integrates three specialized modules: In this study, we aimed to address the dual challenges of supply chain efficiency and economic sustainability in the food processing industry by introducing a novel methodological framework. The proposed framework, centered around the Probabilistic Agent the Constraint Planner, Guided Optimization Unit, the Event-driven Router, and the Bayesian Uncertainty Forecaster. These modules collectively tackle critical aspects of supply chain management, including resource allocation, dynamic routing, and uncertainty quantification. Through extensive simulations and real-world case studies, the framework demonstrated significant improvements in operational efficiency, reducing resource wastage and optimizing routing under varying demand conditions. Furthermore, the Probabilistic Supply Chain Decision Refinement strategy proved effective in enhancing decision-making robustness, ensuring adaptability to fluctuating market dynamics and supply chain disruptions. The results underscore the potential of probabilistic modeling and adaptive optimization in fostering sustainable practices within the food processing industry. the integrated modules, particularly the Bayesian Uncertainty Forecaster, poses challenges for real-time applications in large- scale supply chains. Future research should focus on developing more computationally efficient algorithms to ensure scalability without compromising accuracy. Second, while the framework incorporates sustainability principles, it does not explicitly account for environmental factors such as carbon emissions or energy consumption. Expanding the model to include environmental impact metrics could further align supply chain operations with global sustainability goals. This study establishes a robust foundation for improving supply chain efficiency and promoting economic sustainability, while offering significant potential for further refinement and wider application within the food processing industry and related sectors.

  Supply chain efficiency and economic sustainability in the food processing industry · 2026 · DOI

• Additionally, the limited focus on SMEs and cross-sectoral applications calls for targeted studies that examine AI integration in smaller firms and across different industries, while further research is needed to explore data quality, algorithm design, and human–AI interaction in shaping effective decision-making in supply chains.

  Unveiling the AI–SCM Nexus: A Bibliometric and PRISMA-Based Review of Trends, Technologies, and Future Directions (2021–2025) · 2026 · DOI

• Based on the findings, the following recommendations are proposed:  Adopt Integrated Risk Management Frameworks: Organizations should implement comprehensive risk assessment tools to identify potential vulnerabilities across their supply chains. 276 Ali et al...…....Int. J. Business & Law Research 14(2):269-277, 2026  Invest in Technology: Businesses must leverage predictive analytics, blockchain, and realtime tracking systems to enhance transparency and responsiveness.  Promote Supplier and Route Diversification: Establishing multiple supply sources and transportation routes reduces reliance on single nodes and increases flexibility.  Enhance Collaboration: Strengthening partnerships with suppliers, customers, and regulatory bodies fosters better communication and coordinated responses during disruptions. Build Agile and Adaptive Capabilities: Organizations should design flexible processes that allow quick adjustments in sourcing, production, and distribution.   Policy Advocacy: In Nigeria, businesses should collaborate with government agencies to advocate for improvements in infrastructure and regulatory consistency, which are critical for reducing systemic uncertainty. REFERENCES Adebayo, K., & Okonkwo, C. (2022). Managing logistics disruptions in Nigeria’s manufacturing sector: proactive resilience framework. Nigerian Journal of Supply Chain, 14(2), 45– 59. A Afolabi, O. O., & Okeke, U. (2023). Blockchain adoption in African logistics: Challenges and opportunities. African Review of Technology and Innovation, 10(2), 88–102. Ayodele, T., & Bello, R. (2021). Infrastructure and uncertainty in Nigerian logistics systems. Nigerian Journal of Infrastructure Studies, 6(1), 10–21. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive analytics and artificial intelligence in supply chain risk management: A review. Computers & Industrial Engineering, 137, 106024. Choi, T. M., Wallace, S. W., & Wang, Y. (2021). Big data analytics in operations management. Production and Operations Management, 30(3), 460–472. https://doi.org/10.1111/poms.13204 Christopher, M., & Peck, H. (2020). Building the resilient supply chain. The International Journal of Logistics Management, 31(1), 1–15. Deloitte. (2021). The ripple effect: How COVID-19 is reshaping global supply chains. Deloitte Insights. https://www2.deloitte.com/insights Ivanov, D., Dolgui, A., & Sokolov, B. (2020). The impact of digital technology and Industry 4.0 on ripple Research, 58(3), 829–846. the effect and supply chain risk analytics. International Journal of Production https://doi.org/10.1080/00207543.2019.1634854. Johnson, A., & Williams, D. (2021). Demand forecasting under uncertainty: A hybrid approach. Journal of Business Logistics, 42(1), 72–89.

  Effect of Uncertainty in Supply Change Management: A Review of its Sources, Impacts, and Strategies in Mitigating Uncertainty · 2026 · DOI

• A study such as this is not without some limitations. 1. The study may focus on selected uncertainties (e.g., demand and supply) and not capture all possible exogenous shocks (political, environmental) in equal depth. 2. Empirical testing depends on access to firm-level data on disruptions, costs, and response strategies, which are often proprietary or incomplete. 3. Findings may be context-specific (industry, geography, firm size), limiting direct application to all supply chains. 4. Optimization or simulation models used to represent uncertainty require simplifying assumptions about distributions and behaviors that may not hold in reality. 5. Supply chain uncertainty evolves rapidly; results may need updating as new technologies and risks emerge.

  Effect of Uncertainty in Supply Change Management: A Review of its Sources, Impacts, and Strategies in Mitigating Uncertainty · 2026 · DOI

• Next, while this study provides strong empiri- cal evidence on the impact of AI in controlling operational uncertainty in the Chinese logistics sector, the conclusions are limited by a single country’s geography and institutions.

  Artificial intelligence-driven strategies for enhancing operational performance and managing uncertainties in the supply chain management · 2026 · DOI

• UNSDG, which translates to the United Nations Sustainable Development Goals, is based on the same three characteristics: the economic system, the social system, and the natural environment, with a series of specific objectives: (Nazarian & Khan, 2024) Eradication of poverty, Eradication of malnutrition, Improvement of health status and standard of living, Increasing the quality of the education system, Gender equality, Adaptation Mechanisms to the Global Challenges of the Supply Chain. The most important functions of a supply chain are: flexibility, redundancy, speed of adaptation, visibility, and interconnection with other mechanisms and factors. All these functions allow the supply chain to adapt to global challenges: - Internal factors: risk management procedures, technological and IT capabilities and tools, the level of training of personnel managing the activity flow; - External factors: Natural disasters, armed conflicts, pandemics or social crises; - The complexity of supply chains: This represents both a favorable aspect by ensuring a multitude of alternative and back-up possibilities, but it can also be a potential risk factor such as supply blockages; - Norms, requirements, and regulatory policies We can provide a series of real examples: 1. The COVID-19 Pandemic: The period 2020 - 2022 demonstrated to all humanity the very low redundancy and the high degree of risk to which society is exposed, at least regarding supply chains. (Strengthening supply chain, 2023) 2. The War in Ukraine: On one hand, we have the state of war with its inherent consequences. On the other hand, there are also indirect consequences, such as the transit of grains harvested in Ukraine through neighboring countries like Romania following the Russian naval blockade at sea.

  UNITED NATIONS SUPPLY CHAIN RESILIENCE CASE STUDY ANALYSIS OF VULNERABILITIES IN CRISIS AFFECTED REGIONS · 2026 · DOI

• Based on the findings of this study, the following recommendations are proposed. Upstream petroleum firms in Rivers State should invest in real-time pipeline monitoring technologies, including satellite-based surveillance systems and automated leak detection, to enable early identification of vandalism and integrity breaches, minimizing production downtime associated with logistical vulnerability. Firms should also diversify their logistics networks by developing multi-modal transportation alternatives across road, marine, and pipeline modes, and by establishing strategic inventory buffers at key oilfield locations to reduce the impact of transport bottlenecks and import delays on production continuity. A preventive and predictive maintenance culture should be institutionalized across upstream petroleum operations, supported by digital maintenance management systems that schedule and track equipment servicing proactively, thereby reducing the frequency and severity of unplanned equipment failures. Upstream petroleum firms should further develop and regularly test comprehensive organizational resilience frameworks, incorporating risk assessment protocols, contingency response plans, and cross-functional crisis management teams, to strengthen their capacity to sustain oil production stability when supply chain disruptions occur. Finally, industry stakeholders and regulatory bodies should collaborate to establish a sectorwide supply chain vulnerability monitoring system that tracks logistical and operational disruption incidents, enabling data-driven risk management interventions at both firm and industry levels. Future research should extend this study by formally testing the moderating role of organizational resilience using hierarchical regression analysis, and by incorporating qualitative case studies to provide richer contextual understanding of disruption management practices across Rivers State upstream firms. ©2026 Global Publication House | International Journal of Educational Research | Vol. 9 No.

  SUPPLY CHAIN VULNERABILITY AND OIL PRODUCTION STABILITY OF UPSTREAM PETROLEUM FIRMS IN RIVERS STATE · 2026 · DOI

• The proposed model, Mod_A, puts into effect several advantages with respect to other models that have been traditionally implemented and that were merely based on improving sustainability levels. Furthermore, multiple insights can be gained that will help managers to take better decisions in order to improve business sustainability. First of all, in a dynamic environment -that is, if the improvement plan is designed over several periods of time, and the decisions are made in a sequential manner, and not all of them at the same time -the implementation of the proposed model may lead to superior sustainability performance and cost efficiency. Moreover, the recommendation is for the companies to develop long-term implementation plans to improve in a continuous manner the sustainability level of the company. This is because they can take advantage of existing implementation synergies between practices. An additional advantage of a sequential implementation is that the company can modify the sustainability objectives over time to better adapt to the ever-changing conditions that the company faces at any moment of time. This decision-making process is particularly relevant in the current situation, as companies try to adapt to the economic repercussions of geopolitical tensions. They are therefore lacking an instrument for budgetary control of the implementation of new strategies, while needing to improve their sustainability. In the aerospace sector, given the existing relationship between lean and resilient practices, we observe that by improving the environmental sustainability through lean and resilient practices, the other dimensions of sustainability, 1 3Supply chain strategies adoption for sustainability under budgetary pressure 31 Page 16 of 22 economic and social, are also improved at a higher rate. These results on how sustainability dimensions are related at the supply chain level complement the results previously reached at the company level (Uddin and Akhter 2022). Our study also highlights that environmental sustainability is increased through lean and resilient supply chain strategies. Concerning the sequential order in the practices’ implementation, the results of all the dynamic models coincide in the first practices, P1 and P7, that managers should start implementing. In subsequent periods, the results indicate that some resilient practices should be implemented as well, together with other lean practices. Thus, the results indicate that there is a clear preference to start implementation of the lean strategy in the first place. The synergies between both strategies are identified as critical for enhancing sustainable performance while operating under budgetary constraints.

  Supply chain strategies adoption for sustainability under budgetary pressure · 2026 · DOI

• The GAN-SAE synthetic data augmentation component is applied to the training set, but the paper does not report how synthetic data quality or distribution fidelity affects model robustness when applied to out-of-sample industries in the 2021-2024 external validation; ablation studies isolating GAN contribution versus SAE feature extraction are needed for green supply chain finance applications.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• Market environment and external shocks show pronounced variability in risk response (0.082 SHAP value) but the paper does not specify which macro-level conditions (interest rate volatility, commodity prices, policy changes) or types of upstream-downstream shocks should be prioritized for green supply chain finance risk monitoring applications.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• The composite risk state identification mechanism integrates financial health (0.212 SHAP importance), credit rating (0.189), and ESG factors (0.118), but the interactive and complementary effects among these dimensions lack explicit interaction term analysis; multiplicative or non-additive interactions between ESG performance and supply chain stability should be formally modeled in the DNN-SVM architecture.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• The SHAP feature importance analysis reveals that innovation intensity contributes only 0.057 importance value to risk prediction in the GAN-SAE + DNN-SVM model, but the paper acknowledges innovation's potential long-term effects on operational stability; this gap requires time-horizon-stratified analysis to distinguish short-term versus medium/long-term risk drivers in green supply chain finance.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• The external validation dataset spans 2021-2024 across agriculture, logistics, and retail sectors, but the model's performance variation across industries (most stable in agriculture at 0.922±0.009 accuracy) suggests that sector-specific risk identification mechanisms in green supply chain finance warrant separate calibration studies for resource-intensive industries versus service-oriented sectors.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• The GAN-SAE + DNN-SVM model's risk identification mechanism relies heavily on cross-sectional data, but the indirect and lagged effects of digital experience on cash flow volatility and supply chain coordination efficiency require longitudinal panel data analysis to capture temporal transmission pathways in green supply chain finance risk formation.

  A two-stage deep learning model for risk identification in green supply chain finance · 2026 · DOI

• The spoilage rate improved from 8.5% to 2.1% under FEFO-aware ordering, but the paper does not quantify how sensitive this gain is to temperature-excursion modeling, expiration-date accuracy, or FEFO enforcement fidelity across distributed warehouses. A sensitivity analysis decomposing spoilage reduction across these factors would clarify which cold-chain tracking investments provide the highest ROI.

  An autonomous pharmaceutical supply chain network for resilience and optimization: A multi-agent deep reinforcement learning framework · 2026 · DOI

Most-cited papers in Supply Chain Resilience and Risk Management

• Supply Chain Disruptions: Evidence from the Great East Japan Earthquake* · The Quarterly Journal of Economics · 2020 · 788 citations
• Bouncing Back: Building Resilience Through Social and Environmental Practices in the Context of the 2008 Global Financial Crisis · Journal of Management · 2017 · 417 citations
• SMEs and digital transformation during a crisis: The emergence of resilience as a second-order dynamic capability in an entrepreneurial ecosystem · Journal of Business Research · 2022 · 347 citations
• Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation · International Journal of Production Research · 2024 · 343 citations
• Industry 4.0 enables supply chain resilience and supply chain performance · Technological Forecasting and Social Change · 2022 · 263 citations
• Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers · Technology in Society · 2022 · 243 citations
• Global supply chains in the pandemic · Journal of International Economics · 2021 · 241 citations
• The key role of innovation and organizational resilience in improving business performance: A mixed-methods approach · International Journal of Information Management · 2024 · 235 citations
• Research at the Intersection of Entrepreneurship, Supply Chain Management, and Strategic Management: Opportunities Highlighted by COVID-19 · Journal of Management · 2020 · 220 citations
• Business networks and organizational resilience capacity in the digital age during COVID-19: A perspective utilizing organizational information processing theory · Technological Forecasting and Social Change · 2022 · 188 citations

Most recent work

• From infrastructure to insight: a systemic analysis of AI adoption barriers in supply chain forecasting · International Journal of Productivity and Performance Management · 2026
• From risk to resilience: a multi-layered framework for supplier risk assessment to strengthen supply chain resilience · International Journal of Physical Distribution & Logistics Management · 2026
• Impacts of risk transmission of SRDI enterprises on supply chain resilience · Journal of Business & Industrial Marketing · 2026
• Strategic Responses to Market Disruptions in the Shipping Industry · Pomorstvo · 2026
• The Impact of Artificial Intelligence and Sustainable Digitalisation on the Resilience of Logistics Chains in Romania · Amfiteatru Economic · 2026
• Resilience capability for the circular economy: a conceptual framework · Environment Systems & Decisions · 2026
• Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach · Black Sea Journal of Engineering and Science · 2026
• A two-phase machine learning-based framework for designing viable supply chains: a novel data-driven decision-making approach under incomplete data · Journal of Modelling in Management · 2026
• An Empirical Analysis of Delivery Delays in Supply Chain Management Using Business Intelligence Techniques · International Journal of Supply Chain Management · 2026
• Artificial intelligence-driven healthcare supply chain resilience: The mediating effect of organisational transparency · Journal of Strategy & Innovation · 2026

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