Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

Spare-part management has a significant effect on the productivity of mining equipment. The required number of spare parts can be estimated using failure and repair data collected under the name of reliability data. In the mining industry, failure and repair times are decided by the operational environment, rock properties, and the technical and functional behavior of the system. These conditions are heterogeneous and may change significantly from time to time. Such heterogeneity can change equipment’s reliability performance and, consequently, the required number of spare parts. Hence, it is necessary for effective spare-part planning to check the heterogeneity among the reliability data. After that, if needed, such heterogeneity should be modeled using an adequate statistical model. Heterogeneity can be categorized into observed and unobserved caused by risk factors. Most spare-part estimation studies ignore the effect of heterogeneity, which can lead to unrealistic estimations. In this study, we introduce the application of a frailty model for modeling the effect of observed and unobserved risk factors on the required number of spare parts for mining equipment. Studies indicate that ignoring the effect of unobservable risk factors can cause a significant bias in estimation.

Maintenance cost is a significant part of production costs. Hence, logistics and spare part management should be considered early in the design and operational phase. The reliability characteristic of the item can be used effectively to spare part prediction (SPP). In mining industries, the reliability characteristic can be affected significantly by the operational conditions. In this study, to have an accurate SPP, covariate-based reliability models have been used to estimate the required number of tire spare parts for a dump truck fleet. The outputs represent a significant difference in the required spare parts’ management, when considering and ignoring covariates.

A comprehensive systemic approach is needed to make effective decisions for global sustainability. The system’s points of view introduced sustainable development (S.D.) and sustainability in prior years. Sustainable development is expressed as a desire followed by humanity to live in a better condition considering all the limits that nature could have. Social, environmental, and economic responsibilities are the wide-ranging developmental characteristics that form sustainability. In this paper, with the help of search engines like Scopus and Web of Science, several documents related to environmental sustainability in the mining industry were studied. The principal investigated problems were tailings dam failure, forestland use in mining operations, social and environmental issues in crushed stone mining industries, landfill mining challenges, climatic problems, economic problems, and fatalities in artisanal and small-scale mines. Also, a table was designed to categorise these problems and determine the solution and primary goal. This study investigates the severity of mining operation conditions and environmental issues in this industry. The common environmental problems in the mining industry include soil degradation, deforestation, land subsidence, acid mine drainage, waste production, natural landscape destruction, coal production, carbon footprint, dust pollution, greenhouse gas emissions and climatic problems. To have a more sustainable mining industry, all the mining stages, from the exploration to the post-closure stages, must minimise resource and energy consumption and waste products.