How Data Analytics Turns Scattered Maritime Resources Into Supply Chain Strength

When a typhoon closes a major port, when a geopolitical conflict reroutes shipping lanes, or when port congestion strands a cargo of iron ore at sea, the companies that import bulk commodities face a stark question: can their supply chains keep functioning, and can they recover when they cannot? A new study published in Frontiers in Marine Science offers one of the most detailed answers yet, showing that the answer depends less on owning resources and more on how firms orchestrate them—and on whether they can turn scattered data into decisions.

The research, conducted by Shuyan Li, Jinge Yao, Xina Ji, Mingjie Fang, and Qiwei Pang, surveyed 214 Chinese firms deeply involved in the international seaborne import of bulk commodities, including energy resources, mineral products, and agricultural goods. Using a two-wave survey design separated by roughly three months and structural equation modeling, the team traced how three managerial processes—resource acquisition, resource bundling, and resource leveraging—feed into data analytics capability, which in turn supports two distinct disruption-response capabilities: supply chain robustness and supply chain resilience.

The theoretical foundation combines two complementary frameworks. Resource orchestration theory holds that possessing valuable resources is not enough; firms must actively structure, bundle, and leverage them to build capabilities. Organizational information processing theory adds that as environmental uncertainty rises, so do the demands on a firm to collect, interpret, and apply complex information. In bulk commodity maritime supply chains, where critical resources are dispersed across overseas suppliers, shipping companies, ports, logistics providers, and internal functions—many controlled by independent organizations—both theories point to the same conclusion: the ability to manage fragmented resources and fragmented information together determines whether a firm can withstand disruption.

The empirical results confirm a semi-sequential logic. Resource acquisition was positively associated with resource bundling (standardized β = 0.345, p < 0.001), and resource bundling was positively associated with resource leveraging (β = 0.354, p < 0.001). In other words, firms that secure access to transportation capacity, port facilities, sourcing channels, digital technologies, and professional expertise are better positioned to integrate those resources into coherent capabilities, and firms with well-integrated capabilities are better able to mobilize and deploy them in actual operations such as supplier assessment, route planning, port selection, and inventory adjustment.

All three orchestration processes were also positively associated with data analytics capability, but with striking differences in strength. Resource acquisition showed the strongest association (β = 0.422, p < 0.001), followed by resource leveraging (β = 0.233, p < 0.001) and resource bundling (β = 0.177, p = 0.010). Together, the three processes explained 36.8 percent of the variance in analytics capability. This pattern suggests that access to rich, timely information—vessel movements, freight rates, port congestion, weather conditions, overseas supply conditions—is the single most important ingredient in building the capacity to turn heterogeneous data into predictive and prescriptive insights.

Data analytics capability, in turn, proved to be a powerful predictor of both disruption-response outcomes. Its association with maritime supply chain robustness—the ability to maintain acceptable functioning during disruption—was strong (β = 0.529, p < 0.001), and its association with resilience—the ability to adapt, respond, and recover after disruption—was similarly strong (β = 0.514, p < 0.001). The model explained 28.6 percent of the variance in robustness and 27.0 percent in resilience. Notably, control variables such as firm size, total sales, and firm age showed no significant associations with either outcome, indicating that these relationships are not simply artifacts of larger or older firms being better prepared.

Bootstrap analysis with 5,000 resamples confirmed significant indirect effects: resource acquisition, bundling, and leveraging each influenced robustness and resilience through data analytics capability rather than through direct paths. This finding is theoretically important because it positions analytics as the information-processing mechanism that converts orchestrated resources into disruption-response capability. It also reinforces the empirical distinctiveness of robustness and resilience, which the study measured separately and found to be correlated but not interchangeable (HTMT = 0.469, well below the conservative threshold of 0.85).

The practical implications are substantial. Managers should treat resource acquisition, bundling, and leveraging as an integrated process rather than as separate activities, because obtaining resources alone does not strengthen disruption management unless those resources are integrated and deployed. Investment in analytics should extend beyond software to include the information sources, human expertise, and external relationships that make analytical tools useful. Firms should also embed analytical outputs into routine decisions about supplier selection, transportation capacity, route choice, discharge ports, shipping schedules, and contingency planning, rather than leaving them isolated within technical functions.

The study’s authors acknowledge limitations, including the focus on Chinese firms, the use of a single key informant per firm, and the concurrent measurement of analytics capability, robustness, and resilience at Time 2, which limits strong causal interpretation. Future research with multi-informant designs, objective performance measures, and longitudinal data could strengthen causal identification and test whether these mechanisms vary across different maritime economies and commodity sectors.

Nevertheless, the findings offer a clear message for firms dependent on seaborne bulk commodity imports: building robust and resilient supply chains requires more than accumulating resources or purchasing digital technologies. It requires connecting external resource acquisition with integration and operational deployment, while embedding data analytics into sourcing, shipping, port, inventory, and disruption-response decisions. In an era of climate extremes, geopolitical tension, and cascading trade network risks, that integrated capability may be the difference between a supply chain that buckles and one that bends, adapts, and recovers.

The distinction between robustness and resilience that underpins the study has deep roots in systems engineering and ecology before it migrated into supply chain management. In engineering contexts, robustness describes a system’s capacity to preserve performance when parameters change, while resilience in ecological science describes how a system absorbs disturbance and reorganizes while retaining function. Supply chain scholars adapted both ideas to capture a temporal asymmetry: robustness operates during a disruption, resilience after it. For a bulk commodity importer, this means robustness might involve maintaining delivery schedules when a typhoon delays vessels, whereas resilience might involve rebuilding procurement flows weeks later after a geopolitical rupture has severed a sourcing channel entirely. The finding that analytics capability predicts both outcomes with similar strength suggests that a single information-processing foundation can support what are otherwise organizationally distinct activities—static protection and dynamic reconfiguration.

Bulk commodities provide a particularly demanding setting for testing these ideas. Unlike containerized manufactures, bulk cargoes such as iron ore, coal, grain, and crude oil move in large, relatively homogeneous volumes through specialized ports and vessel classes, which means substitution across routes or carriers is often constrained by infrastructure rather than by demand. The geographic concentration of production and dispersion of consumption that characterizes many bulk commodities creates long, thin supply chains in which a single chokepoint—a mine outage, a canal blockage, a terminal closure—can propagate widely. This structural fragility makes the sector a useful natural laboratory for studying disruption response, and it explains why the authors chose firms substantially involved in seaborne imports rather than a general sample of traders.

The two-wave survey design deserves attention as a methodological safeguard. Much supply chain research collects all measures from a single questionnaire at a single moment, which invites common method bias—the risk that respondents’ general optimism inflates apparent relationships among variables. By measuring the resource orchestration processes several months before measuring analytics capability, robustness, and resilience, the study at least partially separates the predictor and outcome measurements in time, reducing this concern. Structural equation modeling then allowed the authors to estimate an entire network of relationships simultaneously, testing whether the data were consistent with a sequential process running from acquisition through bundling to leveraging, rather than evaluating each link in isolation.

Bootstrap analysis, used to test the indirect effects, is a resampling technique that repeatedly draws new samples from the observed data to estimate the uncertainty of a statistic without assuming a normal distribution. In mediation testing, this matters because indirect effects—the product of two pathway coefficients—often have skewed distributions that classical significance tests handle poorly. Five thousand resamples provide a reasonably stable empirical distribution from which confidence intervals can be constructed, giving the mediation findings more credibility than a simple significance test of individual paths would.

The theoretical integration is also noteworthy. Resource orchestration theory emerged from strategic management research on how managerial actions convert resource ownership into competitive advantage, while organizational information processing theory traces to classic work arguing that organizational design should match the information demands of the task environment. Combining them yields a specific, testable proposition: resources dispersed across organizational boundaries generate potential only when firms can process the information those resources produce. The empirical pattern fits this proposition. The strongest pathway ran from resource acquisition to analytics capability, consistent with the idea that analytics is fundamentally hungry for input data—vessel positions, freight indices, weather feeds, supplier signals—and that firms lacking access to such feeds have little to analyze regardless of their software investments.

Why resource bundling showed the weakest association with analytics capability invites further inquiry. One plausible interpretation is that bundling is an integrative activity whose value is realized downstream, in leveraging and in operational execution, rather than in information generation. Another is that bundling’s information content is largely internal—coordination routines, shared platforms, consolidated inventories—whereas the analytics capability measured here appears oriented toward external environmental scanning. Disentangling internal from external information processing could be a productive direction for subsequent work.

The study’s setting in China carries both advantages and limits for generalization. China is the world’s largest importer of many bulk commodities, so Chinese importers face acute exposure to maritime chokepoints and export-country concentration, making the context substantively important rather than merely convenient. At the same time, Chinese firms operate within a distinctive logistics infrastructure environment, including extensive state investment in port capacity and digital trade platforms, and within unique regulatory and financing conditions. Whether the same relationships hold for importers in other maritime economies, for exporters rather than importers, or for containerized rather than bulk trade remains an open empirical question.

The absence of significant effects for firm size, sales, and age is itself informative. If larger or longer-established firms were simply better at disruption management, the analytics pathway might be a proxy for organizational scale. The pattern instead suggests that capability-building processes are available to firms of varied profiles, which carries practical weight for mid-sized importers who cannot match the resource endowments of global majors but can still invest in acquisition relationships and analytical competence.

For policy observers, the findings also speak to broader debates about supply chain security. Governments increasingly emphasize reshoring, stockpiling, and diversification as responses to commodity supply risk. The study’s evidence suggests a complementary firm-level lever: because resilience depends on rapid reconfiguration of suppliers, routes, ports, and carriers, the private capability to sense and respond may amplify the value of any public diversification measure. A diversified sourcing base without the informational capacity to recognize when to switch is arguably as vulnerable as a concentrated one.

Finally, the study contributes to a maturing conversation about data analytics in supply chains by specifying a mechanism rather than merely documenting an association. Much prior work has asked whether big data analytics improves resilience; this research asks where analytics capability comes from and shows it arises through identifiable managerial processes. That reframing shifts the practical question from whether to adopt analytics to how to build the resource base and orchestration routines that make analytics consequential when disruption arrives.

Subject of Research: The role of data analytics capability in linking resource orchestration to robustness and resilience in bulk commodity maritime supply chains

Article Title: From resource orchestration to robustness and resilience: the role of data analytics capability in bulk commodity maritime supply chains

Article References: Li, S., Yao, J., Ji, X., Fang, M., & Pang, Q. (2026). From resource orchestration to robustness and resilience: the role of data analytics capability in bulk commodity maritime supply chains. Frontiers in Marine Science, 13, Article 1969423. https://doi.org/10.3389/fmars.2026.1969423

Image Credits: AI Generated

DOI: 10.3389/fmars.2026.1969423

Keywords: maritime supply chains, data analytics capability, resource orchestration, supply chain resilience, supply chain robustness, bulk commodities, organizational information processing theory, resource, orchestration, robustness, resilience, role