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This study aims to analyse whether the presence of supply chain complexity (SCC) influences firms to improve their supply chain (SC) resilience and SC robustness capability. This study also examines an important paradox: whether investing in both exploitation and exploration practices is conflicting or complementary to enabling SC resilience and robustness in the presence of SCC.

The authors used a survey-based approach to collect 242 useful responses from SC professionals of Pakistani firms, an important emerging economy context. The data were analysed with covariance-based structural equation modelling to statistically validate the model.

The analysis reveals several key findings: the presence of SCC has a direct, positive influence on SC resilience and SC robustness; while exploitation practices only partially mediate the nexus between SCC and SC resilience, they fully mediate the relationship between SCC and SC robustness; while exploration practices partially mediate the nexus between SCC and SC resilience, they do not mediate the relationship between SCC and SC robustness and SCC has a significant influence on SC resilience and SC robustness sequentially through exploitation and exploration (i.e. one after the other).

These findings help to reconcile the exploitation versus exploration paradox in cultivating SC resilience and SC robustness in the presence of SCC. The findings assist SC managers in determining how to deploy their limited resources most effectively to enhance SC resilience and SC robustness while facing SCC.

The authors devise and empirically validate a unique framework that demonstrates how the presence of SCC works as a stimulus to build SC resilience and SC robustness.

This paper’s main aim is to check the mediating effect of supply chain memory in the relationship between using digital technologies and both supply chain resilience and robustness. In addition, the impact of the COVID-19 disruption was tested as a moderator of the impact of supply chain memory on supply chain resilience and robustness.

Altogether, 257 supply chain managers answered the questionnaire, and data were analysed through structural equation modelling.

This paper contributes to theory and practice by demonstrating that the experience, familiarity and knowledge to deal with disruptions partially mediate the relationship between digital technologies, resilience and robustness. Moreover, our results show that memory is less efficient for the supply chain to maintain an acceptable level of performance in case of a new extreme disruptive event like COVID-19. The full model was able to explain 36.90% of supply chain memory, 41.58% of supply chain resilience and 46.21% of supply chain robustness.

The study helps to understand how to develop supply chain memory, positioning digital technologies as an antecedent of it. The impact of supply chain memory on supply chain resilience and robustness is proved. Knowledge about the impact of industry 4.0 technologies on disruption management is quantitatively improved. It demonstrates that digital technologies impact resilience and robustness mainly through supply chain memory. The study proves that supply chain memory is less efficient for the chain remains effective when a non-routine disruptive event occurs, but it is still imperative to recover from it.

In recent years, the COVID-19 pandemic has become one of the most impactful disruptions which has imposed high levels of uncertainty on supply chains around the world. Supply chain integration (SCI) is highly recommended as an underlying mechanism that can facilitate the development of resilience and robustness as two dynamic capabilities. They can in turn positively influence firm performance and success during the disruptive conditions of COVID-19 era. The study aims to examine whether SCI as an enabler of resilience and robustness can improve firm performance during COVID-19 pandemic.

A theoretical model is developed to elaborate the relationship between SCI dimensions, resilience and robustness and firm’s operational and financial performance during the COVID-19 pandemic. A survey method is then used to empirically examine the model using a sample of 94 companies in the food industry in the province of Tehran, Iran, during the COVID-19 pandemic. This study makes several contributions. It provides a novel theoretical model on the relationship between SCI, resilience and robustness and firm performance and tests this model in a less-studied yet critical context (i.e. Iranian food industry) and during a disruptive era (i.e. COVID-19 pandemic).

The results support the positive effect of three SCI dimensions of internal, product and process integration on operational and financial performance during corona virus pandemic. Furthermore, internal and process integration have positive effects on resilience. Internal, product and process integration have positive effects on robustness. In addition, resilience mediates the effects of internal and product integration on both operational and financial performance, whereas robustness mediates the effect of internal and product integration on financial performance.

This study was conducted in the Province of Tehran. To test and generalize the results, it is recommended to conduct this study in other places and countries.

These results highlight the importance of SCI dimensions as vital enablers of resilience and robustness and their consequent impact on firm’s performance during the COVID-19 pandemic.

The purpose of this paper is to present the probabilistic approach to a new robustness-based design strategy for thin-walled composite structures in post-buckling.

Because inherent uncertainties in geometry, material properties, ply orientation and thickness affect the structural performance and robustness, these variations are taken into account.

The methodology is demonstrated for the sake of simplicity with an unstiffened composite plate under compressive loading, and the probabilistic and deterministic results are compared. In this context, the structural energy and uncertainties are employed to investigate the robustness and reliability of thin-walled composite structures in post-buckling.

As practical implication, the methodology can be extended to stiffened shells, widely used in aerospace design with the aim to satisfy weight, strength and robustness requirements. Moreover, a new argument is strengthened to accept the collapse close to ultimate load once robustness is ensured with a required reliability.

This innovative strategy embedded in a probabilistic framework might lead to a different design selection when compared to a deterministic approach, or an approach that only accounts for the ultimate load. Moreover, robustness measures are redefined in the context of a probabilistic design.

Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to knowledge networks has important theoretical and practical significance. Despite the insights being offered by the growing research stream, few studies discuss the diverse responses of knowledge networks’ robustness to different target-attacks, and the authors lack sufficient knowledge of which forms of malicious attacks constitute greater disaster when knowledge networks evolve to different stages. Given the irreversible consequences of malicious attacks on knowledge networks, this paper aims to examine the impacts of different malicious attacks on the robustness of knowledge networks.

On the basic of dividing malicious attacks into six forms, the authors incorporate two important aspects of robustness of knowledge networks – structure and function – in a research framework, and use maximal connected sub-graphs and network efficiency, respectively, to measure structural and functional robustness. Furthermore, the authors conceptualize knowledge as a multi-dimensional structure to reflect the heterogeneous nature of knowledge elements, and design the fundamental rules of simulation. NetLogo is used to simulate the features of knowledge networks and their changes of robustness as they face different malicious attacks.

First, knowledge networks gradually form more associative integrated structures with evolutionary progress. Second, various properties of knowledge elements play diverse roles in mitigating damage from malicious attacks. Recalculated-degree-based attacks cause greater damage than degree-based attacks, and structure of knowledge networks has higher resilience against ability than function. Third, structural robustness is mainly affected by the potential combinatorial value of high-degree knowledge elements, and the combinatorial potential of high-out-degree knowledge elements. Forth, the number of high in-degree knowledge elements with heterogeneous contents, and the inverted U-sharp effect contributed by high out-degree knowledge elements are the main influencers of functional robustness.

The authors use the frontier method to expose the detriments of malicious attacks both to structural and functional robustness in each evolutionary stage, and the authors reveal the relationship and effects of knowledge-based connections and knowledge combinatorial opportunities that contribute to maintaining them. Furthermore, the authors identify latent critical factors that may improve the structural and functional robustness of knowledge networks.

First, from the dynamic evolutionary perspective, the authors systematically examine structural and functional robustness to reveal the roles of the properties of knowledge element, and knowledge associations to maintain the robustness of knowledge networks. Second, the authors compare the damage of six forms of malicious attacks to identify the reasons for increased robustness vulnerability. Third, the authors construct the stock, power, expertise knowledge structure to overcome the difficulty of knowledge conceptualization. The results respond to multiple calls from different studies and extend the literature in multiple research domains.

To study the effect of Knightian uncertainty – as opposed to statistical estimation error – in the evaluation of value‐at‐risk (VaR) of financial investments. To develop methods for augmenting existing VaR estimates to account for Knightian uncertainty.

The value at risk of a financial investment is assessed as the quantile of an estimated probability distribution of the returns. Estimating a VaR from historical data entails two distinct sorts of uncertainty: probabilistic uncertainty in the estimation of a probability density function (PDF) from historical data, and non‐probabilistic Knightian info‐gaps in the future size and shape of the lower tail of the PDF. A PDF is estimated from historical data, while a VaR is used to predict future risk. Knightian uncertainty arises from the structural changes, surprises, etc., which occur in the future and therefore are not manifested in historical data. This paper concentrates entirely on Knightian uncertainty and does not consider the statistical problem of estimating a PDF. Info‐gap decision theory is used to study the robustness of a VaR to Knightian uncertainty in the distribution.

It is shown that VaRs, based on estimated PDFs, have no robustness to Knightian errors in the PDF. An info‐gap safety factor is derived that multiplies the estimated VaR in order to obtain a revised VaR with specified robustness to Knightian error in the PDF. A robustness premium is defined as a supplement to the incremental VaR for comparing portfolios.

The revised VaR and incremental VaR augment existing tools for evaluating financial risk.

Info‐gap theory, which underlies this paper, is a non‐probabilistic quantification of uncertainty that is very suitable for representing Knightian uncertainty. This enables one to assess the robustness to future surprises, as distinct from existing statistical techniques for assessing estimation error resulting from randomness of historical data.

The concept of robustness in manufacturing is not easy to capture and even harder to quantify. This paper elaborates an approach to assess robustness in production systems from a holistic input-throughput-output perspective using a pragmatic robustness indicator.

First, in order to have a precise understanding of what needs to be measured, a concept of robustness in production systems is defined based on a literature overview. Three different aspects are considered to be essential to comprehensively describe robustness in production: the deviations of input resources, of performance and of output. These aspects are translated into an aggregated indicator based on developments of production costs, order delays and output volumes. The indicator-based assessment approach is eventually applied to a flow-shop scheduling case study in the chipboard industry.

The study shows that an assessment of robustness should not solely focus on a single aspect of a production system. Instead, a holistic view is required addressing the tradeoffs that robustness must balance, such as the one between the realized performance, the corresponding resource requirements and the resulting output. Furthermore, the study emphasizes that robustness can be interpreted as a superior system capability that builds upon flexibility, agility, resilience and resistance.

First, the paper is a call to further test and validate the proposed approach in industry case studies. Second, the paper suggests a modified understanding of robustness in production systems in which not only the deviation of one single variable is of interest but also the behavior of the whole system.

The approach allows practitioners to pragmatically evaluate a production system’s robustness level while quickly identifying drivers, barriers and tradeoffs.

Compared to existing assessment approaches the proposed methodology is one of the first that evaluates robustness in production systems from a holistic input-throughput-output perspective highlighting the different tradeoffs that have to be balanced. It is based upon a comprehensive concept of robustness which also links robustness to adjacent capabilities that were otherwise only treated separately.

A total of 264 elementary and secondary school teachers in a southeastern state completed the Robustness Semantic Differential for concepts focusing on their role, their principal's role and the role of students in school organization. They also completed the School Survey, a multidimensional measure of their attitudes toward their work environment. As hypothesized, results of multiple regression analyses produced significant positive correlations between the following variable sets: 1) the robustness of the teaching role and attitudes about professional performance and development, 2) the robustness of the principal's role and attitudes about supervisory relations, and 3) the robustness of the student role and attitudes toward the educational effectiveness of the school and its programs.

The purpose of this paper is to define and quantify the term robustness within the context of a Private Finance Initiative (PFI) project environment from the perspective of the Granting Authority. The paper is conceptual, based on conceptual generalisations.

The paper considers the theory of systems thinking within PFI procurement. This is further integrated with the concept of robustness and resilience used extensively in the ecology discipline. Combining the two, this paper presents a mathematical approach of quantifying robustness in PFI projects.

An analytical model is used to support the mathematical analysis to quantify and define robustness.

The quantifying of robustness is based on the principal assumptions presented in the paper. The principal assumptions provide an ideal situation which is necessary to pursue and develop the proposed approach to quantify robustness. Changes to the assumptions may affect the generalisability of the approach.

The paper provides a greater definition to robustness within PFI projects and the possibility of quantifying robustness to better monitor and manage the characteristic.

Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters.

Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade.

Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration.

Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.