The link between environmental uncertainty and supply chain efficiency can theoretically be based on a number of soundly established frameworks in supply chain management (SCM). This theory is useful for understanding how firms update the processes within their supply chains in response to uncertain conditions surrounding sustainability and how external pressures work to turn into operational inefficiencies. Resource Dependence Theory (RDT), proposed by Pfeffer and Salancik (1978), Reitz (2007) contends that organisations are resource-dependent with the external determining resources that they control, and resource availability of uncertainty forcing strategic responses. Environmental uncertainty can be viewed as a type of environmental turbulence where the firms are facing ambiguous constraints regarding the resources as a result of changing sustainability requirements, changing expectations of investors and changing environmental standards. In such environments, firms may respond to uncertainty by relying more heavily on specific supply chain strategies to manage their operations efficiently and to hedge against supply chain disruptions and rising compliance costs.

Real Options Theory, which was first conceptualised by Myers (1977) and further developed by Dixit and Pindyck (1994), emphasises the managerial flexibility under uncertainty. Firms confronting uncertain situations see investments and operational strategies as ‘options’ that they can employ when they have more information. In the face of environmental uncertainty, firms may postpone their efforts towards adopting lean supply chain efficiency systems or sustainable supply chain practices owing to ambiguous cost structures and long-term benefits for the same. As stated in contingency theory, the applicability of a company’s structure is defined by such factors as the outside context of the company (Lawrence & Lorch 1967), the degree of unpredictability (Thompson 1967), operational technologies (Woodward 1958) and the rate of change that the company is facing (Burns & Stalker 1961). Contingency theory is contradictory to positivist views, which wish to formulate definitive scientific rules for maximising efficiency (Gonz´alez-Zapatero et al. 2024; Kessler, Nixon & Nord 2017). Thus, it is considered appropriate to use these well-established theories as a theoretical foundation for this study.

In the last decade, sustainability mandates, regulatory realignments and a growing chorus of stakeholder expectations – collectively broadly conceptualised as environmental imperatives have re-arranged the corporate landscape (Annesi et al. 2025). While environmental stewardship is becoming an increasingly entrenched strategic goal, the vagaries of environmental policy and compliance have been given relatively little scholarly attention. Such uncertainty represents a complex, evolving hindrance that seriously impedes organisational planning and execution (Tang et al. 2024). Environmental uncertainty is exacerbated by rapidly evolving environmental regulations, shifting investor expectations, and changing societal norms, all of which contribute to greater complexity in day-to-day organisational operations. This confluence of factors highlights the need to question the impact of environmental uncertainty on supply chain performance – a nexus that is largely uncharted in the body of literature. Indeed, environmental uncertainty has a dominant role in their motivation for rival firms to work together on common challenges (Seepana, Paulraj & Datar 2025). Accordingly, the skilful management of these uncertainties is of utmost importance, especially for operations management leaders who need to adapt in volatile business milieu and direct the flow of specialised knowledge within their organisations (Katsaliaki, Kumar & Loulos 2024; Meena, Dhir & Sushil 2023). These uncertainties arise from various areas, which include market dynamics, technological change etc. For example, the uncertainty in the market environment includes unexpected changes in the external environment – in terms of changing requirements and tastes of consumers – as well as variation in specifications of upcoming products (Seepana et al. 2025). Turning at the concept of technological uncertainty; this construct captures both the magnitude and the velocity of technological transformations permeating an industry’s technological infrastructure in that it encapsulates the rapid evolution of methodologies that are employed in the fabrication of products or services (Jiyao, Reilly & Lynn 2005). A basic difference between market and technical uncertainty lies in their origin: market uncertainty is rooted in the difficulty to understand the evolution of consumer tastes and demands within individual markets (Oriani & Sobrero 2008). Corporate players often have difficulties in defining customer classes as well as tracking changing expectations about product or service characteristics (Jiyao et al. 2005). This predicament is caused by exogenous forces, such as changing consumer trends and demographic transitions (Oriani & Sobrero 2008). Given that environmental contamination leads to the creation of an elevated likelihood of damage to ecological systems and economic structures (Dixit & Pinckney 1994), scholarly inquiry has shifted to finding the optimal temporal frame within which to frame policy by drawing on insights from real options theory. This analytical framework views that under stochastic conditions, the latitude to delay an investment till the volatility subsides represents a valuable flexibility premium that can be valued analogously to a financial derivative (Hussain et al. 2023). Existing scholarship has taken advantage of the real options construct in order to scrutinise investment prospects under such circumstances (Heidari & Heravi 2024; Zhao, Li & Liu 2024). Although the consequences of environmental uncertainty on firm performance and risk management have been widely studied, its positive or negative impact on operational factors such as supply chain efficiency is still concealed. While some preliminary inquiries have begun to shed some light on this relationship, no clear and empirically grounded connection has emerged to date, signalling the need for further empirical scrutiny.

A supply chain represents an integrated process of entities, persons, processes, data and assets needed for the production, movement and delivery of products and services to the end consumers (Huang et al. 2022a) approach to asset management and utilisation but it can also be a driver of organisational flexibility and agility in the fluid dynamics of the modern markets (Huang et al. 2022b; Wang, Huang & Fu 2025). Its basic role is to ensure that goods arrive at their destination, accurately, on time and in good condition. The administration of supply chains has become increasingly complex as a result of globalisation, the dispersion of supplier structures and the multiplicity of regional regulation. Moreover, consumers’ demand has changed in its way of finding more personalised choices and faster shipment speed (Jain & Mishar 2024; Awashreh et al. 2024). In the field of SCM, the idea of ‘efficiency’ is widely used and could be interpreted across different meanings. Within the field of SCM scholarship, efficiency is defined as the ability of an organisation to maximise its profits by optimising the operations of its supply chain (Negi 2021). On an internal level, the efficiency of a firm is often defined as the lowering of manufacturing costs and manufacturing cycle times. Externally, it includes elements such as on-time delivery, satisfying the order speed, service quality and competitiveness. As a result, supply chain efficiency is a key performance measure and it directly impacts the performance result and economic results (Haque et al. 2025). It is also a basic force for the progression of supply chain and a major source of competitive advantage. The efficiency of a supply chain acts as a very important indicator of a firm’s operational health and its ability to put resources to work. Contemporary studies of management and operations have identified it as one of the major areas of concern. A streamlined supply chain is not only a cost-efficient approach to asset management and utilisation but it can also be a driver of organisational flexibility and agility in the fluid dynamics of the modern markets (Huang et al. 2022b; Wang, Huang & Fu 2025). Extensive scholarly interest has attempted to outline the size and location of inefficiencies in supply chains around the world (Behzadi et al. 2018; Negi & Anand 2019; Suryawanshi & Dutta 2023), with the seminal work of Woodhead and Cotter (2017) providing a notable example of this form of work. It is commonly recognised that the anticipated growth of Australia’s horticultural export supply chain will become increasingly dependent on fundamental improvements in efficiency. In this and similar vein, Silvestri et al. (2024) have proposed the development of efficiency-oriented framework, which is designed to protect the long-term viability of the supply chain through improved performance metrics. The main difficulty is not limited to increasing output levels but requires the determination of approaches to make resource utilisation and operational processes more effective, both in the production phases and the consumption phase of this production process. Haque et al. (2025) go further to state that identifying the critical determinants of efficiency will be indispensable to the successful formulation of the export supply chain framework.

The concept of risk is invoked for the case of events where the probability of possible events can be estimated at some reasonable degree of confidence. In contrast, uncertainty defines situations in which information is incomplete or unreliable and no meaningful probabilities can be assigned. Scholars have broadened the concept of risk in the past few years and argue that uncertainty should replace probability as the defining feature in the domain of risk (Lee et al., 2024). The term ‘Supply Chain Risk’ usually encompasses uncertainties and vulnerabilities, both on the macro level and on the level of specific operational contexts, which disrupt the physical movement of goods and associated financial transactions in the fabric of a firm’s supply network (Ho et al. 2015; Zhu, Huang & Huang 2025). A sizable number of academic researches have explored the consequences and effects of such supply chain risks (Wu 2024). Supply chain risk could be best described as the potential for a risk of incidents that could significantly affect the effectiveness of a firm’s supply operations (Gonz´alez-Zapatero et al. 2024). Consequently, organisations need to embrace a three-phase strategy in dealing with it: firstly, identification of possible risks; secondly, assessment of the possible impact; and thirdly, implementation of plans to minimise their impact (Fan & Stevenson 2018; Ho et al. 2015). The first phase of detection aims to record the possible incidents that could negatively affect the supply chain by classifying them according to their origin (external and internal to the organisation). External incidents are caused by things performed in society as an example conflict, terrorism, political unrest or adverse legislation or by natural events such as disease outbreak, environmental disaster. Internal incidents, on the other hand, stem from the company or a member of its supply chain, examples of which are insufficient strategic planning activities, a lack of operational controls or self-interested activities. According to recent studies, businesses often have a reactive approach and take measures to reduce the negative effects of supply chain risks once they have occurred (Crosignani, Macchiavelli & Silva 2023; Manhart, Summers & Blackhurst 2020). Common measures to ensure that demand can be met in the event of disruptions are keeping buffer stock, also known as redundant inventory, reserving excess production capacity and lead time (Zhu et al. 2025) and having large cash reserves (Wu 2024). Such approaches are aimed mostly to protect the company from the consequences of such breakdowns. The academic literature on SCM represents a large number of empirical investigations; however, these have often tended to be narrow in scope, being restricted to isolated elements. This limitation is especially pronounced with respect to one of the critical factors, which is supply chain risk mitigation. This factor has considerable and significant influences, and it actively determines both of the performance and efficiency of supply chains, a relationship that is particularly prominent in the manufacturing firms (Chang et al. 2019). The focus on developing resilient and reliable supply chains has become important for most manufacturers. Such focus allows them to achieve several competitive advantages and achieve high operating efficiency in their supply chains, which can be attained and sustained for the sake of attaining strong and sustainable supply chain performance in the current unpredictable and fast-paced markets (AL-Shboul 2023). Previous research has been conducted addressing aspects such as risk mitigation, overall supply chain effectiveness and responsiveness, individually or combined together, for the goal of managing a large number of customer demands (Mwesiumo, Nujen & Buvik 2021). The current research focuses on two particular dimensions of supply chain risk, namely, logistics security and work level of risk, and investigates their role as a mediator in the relationship between environmental uncertainty and supply chain efficiency.

Building upon the literature examined, this study puts forward the following hypotheses:

In this framework, the direct effect of environmental uncertainty on supply chain efficiency, the direct effect of environmental uncertainty on supply chain risk, and the direct effect of supply chain risk on supply chain efficiency are presented, as well as the indirect effect through the proposed mediation mechanism.

Figure 1, demonstrated how the authors build their framework to make the study utilises descriptive research, in which methodology, designed to delineate phenomena and its interrelationships as they unfold in their natural context is utilised. The approach allows precision to be placed around the underlying problems and sharp focus is given to research inquiries. By using the holistic approach, this method of work creates holistic insights that offer substantial contributions to society’s comprehension of complex issues. The study is quantitative in orientation and makes use of structured questionnaire to get empirical data from the representative sample of the target community. The instrument was carefully created to cover the full range of dimensions of inquiry related to the subject matter. Its design drew on validated measures taken from prior scholarly studies that examined the key variables and thematic constructs of the study.

This study was conducted on the community of industrial and commercial companies in Sudan, where the study population is distributed between the cities of Port Sudan, River Nile, and Khartoum.

This study followed convenience sample of logistics operation managers, supply chain managers, operations managers, the sample consisted of (190) respondents. The convenience sample was selected because of the ongoing conflict in Sudan. Since April 2023 most of the companies either locked or moved out of the capital, therefore there are no accurate data about the exact numbers of the companies operating in Khartoum or other cities (Hamid & Eshag 2025; Hamid et al. 2022). We sent out an anonymous online questionnaire through various channels, including professional networks and online survey. By keeping the survey anonymous, we hoped to encourage honest and open responses. After collecting and cleaning the data, we were left with a solid set of responses to analyse.

The survey itself was built on well-respected and previously tested questions to make sure our findings were both reliable and valid. All questions were answered on a five-point scale, from ‘strongly disagree’ to ‘strongly agree’.

Ethical clearance for this study was granted by the National University Research Ethics Committee (NU-REC) (Approval Number NU-REC/05-001/25, Date 25 May 2025). Every procedure conducted in this research involving human subjects adhered strictly to the ethical guidelines set forth by the institutional and/or national research committees, as well as the 1964 Helsinki declaration and its subsequent amendments or equivalent ethical standards. All participants provided written informed consent.

Prior to their participation, written informed consent was acquired from each person. Participants were guaranteed that their responses would remain confidential and that they could opt out of the study at any moment without facing any repercussions.

In order to assess the constructs’ reliability and validity, several measurement assessments were conducted. Reliability is corroborated by Cronbach’s alpha and composite reliability, and both exceed the recommended levels. Discriminant validity for the constructs was assessed using the heterotrait–monotrait (HTMT) ratio. The presence of convergent validity is indicated by the average variance extracted (AVE) values being above the acceptable threshold. The recorded results for the above analyses are presented in Table 2 and the respective figures are summarised in Figure 2.

The measurement model was evaluated based on construct reliability and validity, and the findings indicate the study’s latent variables have met the minimum threshold requirements on internal consistency and convergent validity on average. For the construct Supply Chain Efficiency, all indicator loadings achieved strong reliability reflected in the Cronbach’s alpha of 0.777 and composite reliability (rho_c) of 0.858, which is above the 0.70 threshold, and therefore the construct sustained strong reliability (Table 2).

The AVE value of 0.604 also suggests reasonable convergent validity. The construct Environmental Uncertainty also demonstrated reasonable reliability wherein Cronbach’s alpha was 0.719 while composite reliability was 0.824, and the construct’s AVE of 0.540 further suggests that more than half the variance in the items is related to the construct. For the Supply Chain Risks construct, the Cronbach’s alpha and composite reliability, which stood at 0.662 and 0.795, respectively, were the lowest among the other constructs but was still in the reasonable range for preliminary research. The AVE value of 0.492, which is just below the recommended 0.50 threshold, does imply weaker convergent validity but is still considered acceptable. Lastly, the construct of Logistical Security demonstrated reasonable reliability with Cronbach’s alpha of 0.643 and composite reliability of 0.808. An AVE value of 0.585 indicates a reasonable level of convergent validity. In summary, the majority of the constructs exhibit adequate reliability and validity, which supports the continued evaluation of the structural model. Overall, these results confirm that the measurement model is statistically sound and suitable for further structural analysis. Despite minor limitations in the AVE of the risk construct, the overall reliability and validity indicators fall within acceptable ranges, indicating that the constructs are measured consistently and adequately capture the underlying theoretical concepts in the Sudan war context. This strengthens confidence in the robustness of the subsequent hypothesis testing and model estimation.

The HTMT ratios obtained and shown in Table 3 were used to assess the constructs in the model for the HTMT measure of discriminant validity. All of the constructs in the model were shown to meet the recommended criteria. The HTMT ratios for the constructs in the model ranged between 0.264 and 0.607, which is well below the more conservative value of 0.85, thus evidence of each construct being different from all the other constructs of the model, in being fully distinct and separate construct from each other. A value of 0.264 for the interconstruct HTMT value for ‘Efficiency and Environmental Uncertainty’ is an indication of being conceptually distinct and poles apart. Moderately correlated constructs, as is expected in the context of the supply chain, are ‘Efficiency and Supply Chain Risks’ (0.552) and ‘Efficiency and Logistical Security’ (0.607), which is evidence of the individual constructs for each being distinct and in being fully separate from one another. The interconstruct HTMT value of **Environmental Uncertainty and Supply Chain Risks** (0.560) is acceptable by all standards and is supportive of the construct of uncertainty in its contribution towards a risk construct, the separate constructs being fully different in their dimensions of the supply chain. The interconstruct HTMT value of **Environmental Uncertainty** (0.390) and **Logistical Security** (0.577) is interconstruct HTMT value and is supportive of the discriminant validity of the model. Overall, all HTMT findings vindicate their inclusion as unrelated latent variables in the structural model as each construct keeps its character and its own unique conceptual identity intact. In practical terms, these findings confirm that the constructs used in the study do not overlap conceptually and can be treated as independent dimensions of SCM. This is particularly important in the Sudan war context, where environmental uncertainty, risk, security and efficiency represent distinct yet interrelated challenges. The clear separation of these constructs enhances the credibility of the model and ensures that the relationships tested in the structural model are not affected by measurement redundancy or multicollinearity issues.

All relations postulated by the study are case relationships fit to the structural model and hypotheses testing. Results of the research are summarised in Table 4.

Within the framework of the structural model, the analysis of the direct hypotheses shows differences in significance among the relationships being studied. The Environmental Uncertainty – Efficiency path was non-significant (β = −0.007, p = 0.943), implying that external environmental uncertainty has no bearing on supply chain efficiency in the context of the Sudan war. This finding suggests that firms operating in conflict environments may have already internalised uncertainty as a normal condition, and therefore efficiency is maintained through adaptive operational practices rather than being directly disrupted by external instability. On the contrary, Environmental Uncertainty was found to have a large, statistically significant and positive effect on Supply Chain Risks (β = 0.402, p < 0.001), meaning that the higher the uncertainty, the greater the level of risks, which supply chain operations face. Practically, this indicates that increases in volatility, such as border closures, infrastructure damage or policy disruptions, translate directly into higher operational risks for firms, requiring continuous monitoring and mitigation strategies. For the relationships Environmental Uncertainty and Logistical Security, similar positive significant effect was reported (β = 0.260, p = 0.014), meaning that in times of uncertainty, there is a greater level of challenge related to the security of transport and logistics. This reflects the heightened need for secure transport routes, cargo protection and controlled logistics operations in war-affected regions such as Sudan. Both of the mediating constructs, Supply Chain Risks and Logistical Security had positive significant effect on Supply Chain Efficiency and these effects were sizeable, being β = 0.277 (p = 0.002) and β = 0.336 (p = 0.001), respectively. This means that, indeed, greater efficiency in supply chain is realised through positive risk management practices and enhanced logistical security. This highlights that efficiency in conflict settings is not achieved despite risk and security challenges, but rather through actively managing them as core operational priorities. In conclusion, the findings suggest that although there is environmental unpredictability, it does still not lead to the diminishing of effectiveness, but it does indirectly lead to effectiveness getting lowered as a result of the greater dangers as well as of the greater requirement for safety, emphasising the mediating role of operational weaknesses in the supply chains affected by conflict.

The mediation model contained in the indirect hypothesis (as illustrated in Table 5) shows substantial mediation effects. In this instance, the data indicate that Environmental Uncertainty has a statistically significant indirect effect on Supply Chain Efficiency via Logistical Security (β = 0.087, p = 0.034). This suggests that uncertainty in the external environment encourages the adoption of security measures that positively impact efficiency. This implies that firms responding to uncertainty by strengthening logistics security (e.g. route planning, cargo tracking or guarded transport) are better able to sustain operational performance. Furthermore, Environmental Uncertainty exhibits an indirect effect on Efficiency through Supply Chain Risks (β = 0.111, p = 0.010). This means that higher levels of uncertainty create operational risks, and the effective management of these risks results in improved efficiency in the supply chain. This finding reinforces the idea that risk management acts as a transformation mechanism, converting external instability into structured operational responses that sustain efficiency. Such results show that Environmental Uncertainty has no direct effect on Efficiency, suggesting that Environmental Uncertainty interrelates with the operational context of risk and security in conflict-ridden places such as Sudan.

Based on the R-square values (Table 6), the model shows that about 26.0% of the variance in Supply Chain Efficiency can be predicted from Supply Chain Risks and Logistical Security, which is understood as being within a moderate range of predictability. This is supported the adjusted R-square value of 0.248, which suggests valid stability of the estimate. Environmental Uncertainty, on the other hand, explains 16.1% of the variance in Supply Chain Risks (adjusted R² = 0.157), which indicates a steady significance of Uncertainty as a parameter to predict the levels of Risks associated in the Supply Chain. This level of explanatory power is reasonable given the highly volatile and unpredictable nature of conflict environments, where many external variables remain beyond managerial control. Last among the Environmental Uncertainty domains is the 6.8% of the variance in Logistical Security (adjusted R² = 0.063), which while being a weak predictability domain, sufficiently explains a significance of the relationship. This suggests that while uncertainty influences security, additional external factors such as military activity, infrastructure damage and regulatory disruptions may also play a significant role. In conclusion, the R-square values in the model point to there being a moderate capacity of explainability, while the presence of significant indirect effects indicates the value of indirect effects in high mediating environments to centre on as a framework in performance of the Supply chains in environments of high uncertainty and conflict.

See Table 7 to find that f² effect sizes show the predictive power of each construct in the model. The effect of environmental uncertainty (EUN) on efficiency (EFF) is 0.000, which shows the absence of a direct effect which is in line with the earlier observed insignificant path coefficient. This further confirms that efficiency is not directly disrupted by uncertainty but is instead shaped through intermediary mechanisms. On the other hand, EUN has a ‘Medium Effect’ on ‘Supply Chain Risks’ (f² = 0.192), which means that risks operationalised within the supply chain could stem from the uncertainties. This highlights risk as the primary channel through which environmental uncertainty influences supply chain dynamics. It has less direct effect on ‘Logistical Security’ (f² = 0.072), which shows a ‘small but meaningful’ influence of Uncertainty on security risks. As for the endogenous construct of Efficiency, there is ‘Supply Chain Risks (f² = 0.078)’ and ‘Logistical Security (f² = 0.128)’ which also indicate ‘small to moderate effect sizes’ which suggests that each of them has a great role in inflating the supply chain performance, and Logistical Security has a greater in bearing effect to the performance than risks. This indicates that investments in logistical security may yield relatively stronger improvements in efficiency compared to risk mitigation alone in conflict settings. All in all, the f-square values indicate that with the absence of direct effect of Environmental uncertainty (EUN) on EFF, there is also indirect effect via the other constructs of the model. This is also positive to the overall model to be in the escalated or in conflict scenario of the supply chain in Sudan (SUD).

Several recommendations have been proposed to improve supply chain efficiency in conflict-affected environments such as Sudan, among these is the development and implementation of comprehensive risk tracking and assessment systems to identify, monitor, and mitigate potential disruptions. For predictively dealing with the uncertainty posed by the environment, systems should be developed with real time event monitoring, scenario planning and environmental scanning. Logistical security is also an area where improvements are needed, especially with the implementation of controlled documents, security routing of transport and tracking of vehicles, goods and personnel security. To support the continuity of the organisation during disruptions, resilient structures should be put in place, including flexible sourcing and sustaining strategic inventory, operational decentralisation and pre- defined emergency response protocols. Building staff preparedness in these areas should be a priority: risk management, crises communication and rapid decision making. The imperative to fortify the collaboration of actors in the supply chain – private enterprises, humanitarian agencies, public authorities cannot be overemphasised if we are to have a robust and responsive system of data exchange, coordination of transport and security. In modern literature, the effectiveness of such collaboration is very much dependent on such mutual protocols, trust and a common governance system breaking down strict sectoral boundaries. Additionally, managers should prioritise supplier diversification to reduce dependency on single sourcing regions, invest in digital supply chain visibility tools (e.g. real-time tracking systems) and establish contingency contracts with alternative logistics providers to ensure operational continuity during disruptions. Managers are also encouraged to implement decentralised warehousing strategies in safer regions to minimise the risks associated with centralised distribution systems in conflict zones. In terms of policy, the improvement of road infrastructure, the creation of safe transport corridors, a decrease in bureaucratic delays, digitalisation of supply chain processes and provision of exhaustive humanitarian support emerge as important levers both for government and humanitarian actors. The promise of these interventions delivered synergistically is that they will increase the overall resilience and adaptability of the supply chain ecosystem. Furthermore, policymakers should develop crisis-responsive trade policies (e.g. temporary tariff reductions on essential goods), establish public and private coordination platforms for supply chain crisis management, and invest in secure logistics hubs to facilitate uninterrupted movement of critical supplies. Strengthening regulatory frameworks for emergency logistics and incentivising private sector participation in resilient infrastructure development are also recommended. Directors should adopt a risk-based method to supply chain strategy by inserting risk management into core decision-making procedures. This includes the use of performance system of measurement, pressure testing, resilience-focused and situation planning, to better anticipate and respond to disruptions.

The analysis discussed in this study outlines a number of important theoretical implications to contribute to the contemporary understanding of how supply chain dynamics are influenced in volatile environments. The study’s results also support RDT by showing that firms operating under high environmental uncertainty in Sudan adapt their supply chain processes to manage dependencies on scarce or volatile resources. This aligns with RDT’s premise that resource scarcity and uncertainty drive strategic responses to maintain operational performance (Pfeffer & Salancik 1978; Reitz 2007). First of all, the investigation goes further by attempting to apply contingency theory to this area by showing that the security posture and risk management framework of a firm are not static but need to be dynamically adjusted relative to the character and intensity of environmental turbulence. By demonstrating the importance of high-uncertainty environments requiring stronger security protocols in order to maintain the organisational integrity of things, the study is able to support the ‘fit’ between external complexity and internal defense mechanisms, thus challenging the traditional conception of security as a secondary concern in operation management and establishing it as a primary prerequisite for organisational survival in conditions of radical ambiguity. In the same context, the authors enriched the literature by placing the mediation-based frameworks in supply chain research. Additionally, the result shifts the theoretical focus from ‘fit with the environment’ to ‘capability in managing environmental disruptions’. Whereas the findings extended the theory of contingency by representing that in risky environments such as war contexts, organisational performance is not exclusively depending on external environmental factors, but rather on the organisation’s ability to manage the consequences of those factors Furthermore, the study demonstrates principles of Real Options Theory. The operational adjustments and flexible risk/security management strategies observed in Sudanese supply chains reflect managerial flexibility under uncertainty, where firms treat investments in security, risk mitigation and logistics adjustments as ‘options’ to be exercised when additional information or stability emerges. This aligns with the theory’s emphasis on the value of preserving flexibility under uncertain conditions (Dixit & Pindyck 1994; Myers 1977). While supply chain risk management (SCRM) is often thought of as a defensive or cost intensive function, modern scholarship highlights an important positive link between effective SCRM practices and supply chain efficiency. Effective risk management helps firms to identify potential bottlenecks and mitigate them before they evolve into disruption projects to ensure a smooth flow of goods and information. Empirical evidence exists to show the direct effect of SCRM capabilities in improving operational performance by promoting better resource deployment and organisational stability. Finally, the study contributes to the emerging body of knowledge on supply chains in conflict-affected, breakable and high-risk settings, as most of the existing studies are based on steady or mostly uncertain environments, so by conducting the analysis within the Sudan war context it expands the geographical and appropriate scope of supply chain theory where uncertainty is persistent and systemic rather than temporary (Ivanov & Dolgui 2021; Queiroz et al. 2022).

As with all research studies, this study has certain limitations. The cross-sectional nature of the data provides a cross section of the climate at the time but does not provide insight into longitudinal dynamics. Future investigations should more rigorously outline the development of supply chain risk and security strategies in the context of the Sudan. Comparative analyses between Sudan and other unstable jurisdictions would further define regional variations in supply chain risk and security. Future studies should include more factors in the supply chain, such as flexibility, digital transformation, etc., to implement cross conflict site comparative research, and more temporal dimension for the supply chain variables to be quantified in complex environments, and more accurate research results can be obtained.