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As globalisation makes supply networks more complex, the risk of material disruptions increases. Many factors have been considered as affecting the reliability of supply networks. However, no empirical research has been carried out to assess and evaluate the impact of each of these factors on the reliability of supply networks. This paper aims to address this issue.

A gap in the literature was identified around the evaluation of the impact of supply network design characteristics on reliability. This gap is addressed by performing a full factorial experimental design considering all the factors described in the literature, and then analysing (by using analysis of variance and linear regression models), thousands of theoretical and extreme structures of supply networks, thus allowing the analysis of the influence of each factor on the overall network resilience.

Results show that network density, node criticality and complexity are significant factors in reducing the reliability of supply networks. In particular, node complexity (i.e. the total number of nodes in the network) was found to have the strongest negative effect on network reliability, while the strongest positive factor was sources criticality (i.e. the level of redundancy of suppliers).

The identification of these factors and their relative impacts on network reliability can serve as a guide for the design of more reliable networks, and to know which are the most important to consider when designed distribution networks.

The paper identifies, from the literature, key factors affecting supply network reliability and evaluates their relative impact. Given the number of factors identified, an extensive Monte Carlo simulation is used for the first time, by considering simple and very complex networks, to allow the testing of the role of each factor in supply network reliability.

This research aims to develop an approach to assess the reliability of integrated construction supply chains via an integrated model of building information modelling (BIM) and the lean supply chain (LSC). It reflects the synergistic workflow between BIM and LSC as a novel approach to improve the reliability of construction projects.

This research evaluates the reliability of the BIM-LSC approach through a combination of entropy theory, set pair analysis (SPA), and Markov chains (EESM). An exploratory survey was conducted to collect data from 316 industry professionals experienced in BIM and LSC. Subsequently, multiple cycles of calculations were performed with indirect data inputs. Finally, a reliability evaluation index is established for the BIM-LSC approach and potential applications are identified.

The results show that the EESM model of BIM-LSC developed in this study can handle not only supply chain reliability evaluation at a given state but also the prediction of reliability in supply chain state transitions due to changing project conditions. This is particularly relevant to the current environment of the construction project, which is characterised by an increasing level of complexity in terms of labour, technology, and resource interactions.

Future research could consider the accuracy and validity of the proposed model in real-life scenarios with by considering both quantitative and qualitative data across the entire lifecycle of projects.

The research offers a model to evaluate the reliability of the BIM-LSC approach. The accuracy of BIM supply chain reliability analysis and prediction in an uncertain environment is improved.

The BIM-LSC reliability evaluation and prediction presented in this study provides a theoretical foundation to enhance understanding of the BIM-LSC in the construction project context.

The purpose of this paper is to examine the status of the supply reliability of groundwater irrigation, and discuss how it is affected by climate change and tubewell density in rural China.

This study is based on a nine-province village survey and secondary climate data. A Tobit model (or censored regression model) was used to estimate the determinants of supply reliability of groundwater irrigation.

Results show that the supply reliability of groundwater irrigation was 89 percent on average in the past three years. The non-linear relationship in the econometric results revealed that the 30-year annual temperature significantly influenced the supply reliability of groundwater irrigation. When the temperature rises above the turning point (6.30°C), it shifts from a positive to a negative relationship with the supply reliability of groundwater irrigation. The 30-year annual temperature in eight of the nine provinces (i.e. except for Jilin Province) was higher than the turning point. If the temperature increases by 20°C in the future, other factors being constant, the supply reliability of groundwater irrigation will decline by 20 percent. However, if precipitation increases by 10 percent, the supply reliability of groundwater irrigation could improve by 3 percent, while reducing precipitation by 10 percent will lower the supply reliability of groundwater irrigation by 3 percent. Increasing the density of tubewells considerably improves the supply reliability of groundwater irrigation. However, although increasing the density of tubewells may yield enough groundwater for irrigation, this one-sided approach raises sustainability concerns.

Although increasing the density of tubewells may ensure that enough groundwater is available for irrigation, such a conclusion is one sided, and sustainability concerns should be raised in assessing this method of creating supply reliability.

This paper improves the understanding of the impact of climate variables on agriculture irrigation and water supply reliability in the micro scale, and provides a scientific basis for relevant policy making.

The purpose of this paper is to develop an approach for quantifying the operational value of IT‐enabled reliability improvement in a supplier network.

Specifically, the paper investigates an e‐procurement scenario involving emergency material purchases where web services provide for real‐time response, a dynamic supplier set, and the ability to perform cross‐enterprise purchase processes in an inter‐operable fashion. The paper proposes engineering reliability models for three network configurations as a basis for quantifying the value of web services, and develops a numerical illustration to both test its usefulness and to derive preliminary insights into supplier network design in this environment.

This research finds that a stand‐by model is descriptive of reliability in networks where suppliers are identified and managed using web services, and that this approach quantifies operational improvement in this type of e‐procurement system. The research also finds that the benefit derived from using web services for emergency purchases depends on the characteristics of the supply network itself, specifically the failure frequency of the existing suppliers and the time to restore operation after a failure occurs.

These findings are important to information system managers when assessing the financial and technical benefits of real‐time and dynamic information technologies, as well as supply chain managers seeking to implement a dynamically configured supply network. Future research directions include extensions of the reliability models, data acquisition for specifying their parameter values, and investigation into the usefulness of this approach in hardware decisions of web services applications in business.

The purpose of this paper is to investigate the distinction and relationships between the significant strategic capabilities for managing risks in supply chains. This intersectional review exposes a substantial conceptual contradiction between the perspectives reported by various researchers. Further, the current paper classifies the literature into four categories according to the broad objectives investigated by the research papers.

Initially, a bibliometric analysis aligned with the concepts of a systematic literature review is conducted followed by a descriptive review focusing on models and methods. The software called BibExcel is utilized to extract and analyze the bibliographic information in a textual form from the research articles associated with strategic capabilities of the logistics sector. The results are exported to the software known as Gephi to visualize keyword co-occurrence analysis as networks. A well-structured descriptive review is also conducted to identify avenues for future research.

Despite conventional supply chain capabilities like efficiency and effectiveness, eight significant strategic capabilities of supply chains for managing risks are identified from the literature. These capabilities with positive connotations include flexibility, reliability, resilience, robustness, agility, adaptability, alignment and responsiveness. Considering the vast literature on flexibility/reliability along with its numerous dimensions and scope, the authors found that resilience, robustness, agility, adaptability, alignment and effectiveness are achievable through flexibility/reliability. Accordingly, it is appropriate to state reliability and flexibility as supply chain capabilities to achieve the other six supply chain competencies. Furthermore, the entire literature in this domain can be classified into four genres according to the addressed objectives, namely, concept development/validation, capability assessment, network design and performance evaluation.

The information revealed from the keyword co-occurrence analysis along with the research implications provided in the penultimate section will assist budding researchers in framing novel and promising research objectives. Supply chain administrators and policymakers can utilize the literature classification and the notable references provided in this review for locating potential methods for assessing supply chain strategic capabilities, designing the supply chain and evaluating the performance of the supply chain.

An integrated bibliometric and descriptive literature review procedure is utilized in this paper. Furthermore, this critical review is the first work on comprehensively mapping the research relationships among various strategic capabilities required for mitigating supply chain risks.

This paper aims to present a supply chain performance assessment tool that measures the performance of key supply chain activities of a firm under different performance dimensions.

The tool was developed based on an extensive literature review. The nine key internal supply chain activities, as proposed by Grant et al. in 2006, constituted the backbone of the assessment framework, while performance was measured based on three dimensions: cost, time, and reliability. The tool was pilot‐tested on 44 local SMEs. The results were then compared with existing performance benchmark as well as within the benchmarked group itself and a high performing Thai multinational in order to see whether the developed tool could identify performance gaps in the trial group.

The results obtained from the tool provide a description of a firm's internal supply chain activity. The utilised supply chain performance framework can isolate each individual supply chain activity. The results are therefore precise enough for firms to identify individual areas of strengths and weaknesses. The tool is relatively simple and easy to use and understand.

Limitation is related to the availability of the required assessment data. The availability of data is a reflection of systematic data collection and storage procedures of the respondent firms.

The tool was developed on clearly defined theoretical foundations. The three dimensions of cost, time and reliability can balance both financial and non‐financial characteristics of supply chain performance. The data required are simple and objective.

Downstream supply chain (DSC) performance metrics provide a standard framework to assess internal performance. DSC performance metrics can also help balance performance tradeoffs among firms. The purpose of this paper is to develop a survey instrument to determine whether observed performance metrics correspond to the literature and to determine if performance metric systems are used to improve inter‐firm performance.

The survey instrument used in this study was based on SCOR performance attributes consisting of: delivery reliability, responsiveness, flexibility, costs, and asset management efficiency. The survey was completed by 73 members of the Council of Supply Chain Management Professionals (CSCMP) consisting of high‐level managers representing US companies.

One factor explained the underlying one‐dimensional structure of the surveyed Supply‐chain operations reference (SCOR) model as an internal metrics system but the authors did not find convincing support for the notion that external performance metrics are used to coordinate external, DSC inter‐firm activities.

A larger sample size would have allowed more insight into the inter‐relationships of the performance attribute variables. Moreover, the sampling plan limited generalization beyond US firms.

Firms used a standardized performance metric system and did not “pick” among metrics. In addition, firms used metrics independently of the decision to coordinate DSC activities. Perhaps they first learn to coordinate the internal performance and later extend to DSC members.

The paper describes one of the few empirical studies of the SCOR model in US industry.

The purpose of this paper is to demonstrate the potential of simulation approach for performance evaluation in a complex environment with a case of application from Indian Nuclear Power Plant.

In this work, stochastic simulation approach is applied to availability evaluation of AC Power supply system of Indian Nuclear Power Plant (INPP). In the presently followed test, maintenance policies on diesel generators and circuit breakers are considered to exactly model the practical scenario. System success logic incorporates the functional dependencies and dynamics in the sequence of operations and maintenance policies. In each iteration (random experiment), from simulated random behaviour of the system, uptime and down time are calculated based on system success logic. After sufficient number of iterations, unavailability and other required reliability measures are estimated from the results.

The subsystems of AC Power Supply System of NPP are having multi‐states due to surveillance tests and scheduled maintenance activities. In addition, the operation of DG involves starting and running (till its mission time) which is a sequential (or conditional) event. Furthermore, the redundancies and dependencies are adding to the complexity.

This paper emphasizes the importance of realistic reliability modelling in complex operational scenario with Monte‐Carlo simulation approach. Simulation procedure for evaluating the availability/reliability of repairable complex engineering systems having stand‐by tested components is presented. The same simulation model finds application in importance measures calculation, technical specification optimization and uncertainty quantification.

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

Additive Manufacturing offers much potential for industry, but at the same time is likely to have a significant impact on supply chain theory and practice. To-date there has been limited empirical work exploring the effect of Additive Manufacturing, and this study aims to provide a detailed appraisal of how supply chain integration, supply chain performance and firm performance may be affected by the adoption of Additive Manufacturing. These are critical factors for supply chain management, but have received little quantified attention to date.

A theoretical model is developed from a detailed review of the literature, from which a quantitative analysis is performed. Using data collected from 124 automotive manufacturers in European Union and the partial least square- structural equation modeling method, the research examines the relationships among different dimensions of supply chain integration, supply chain performance and firm performance from the perspective of Additive Manufacturing adoption.

The findings indicate that Additive Manufacturing adoption positively influences supply chain performance and as a consequence, firm performance. In addition, supporting the resource-based view perspective, the results show a positive indirect effect of supply chain integration on the supply chain and firm performance improvements, enabled by the Additive Manufacturing adoption. This helps to explain some inconsistent findings in previous research regarding the impacts of supply chain integration on performance.

The results of this study support the view that Additive Manufacturing can make a positive contribution to the supply chain, but this is not achieved solely by the technologies alone. Many of the traditional activities of supply chain management (i.e. integration) are still needed when using Additive Manufacturing, and research needs to understand whether Additive Manufacturing adoption will necessitate changes to the way these traditional activities are undertaken. Building on the findings of the current study, much more work is therefore needed to understand how operations within the supply chain may be changed, and how this may affect the integration and performance of the supply chain.

This study provides quantitative evidence to show that the adoption of Additive Manufacturing has the potential to affect both firm and supply chain performance. This is significant for those companies considering the adoption of Additive Manufacturing, and may serve as a valuable insight in the strategic decision-making process. For those already using Additive Manufacturing, this study serves to underline the potential for firm performance to be influenced, by focusing on improvements to their production strategies and policies.

This study provides an initial insight into some fundamental supply chain concepts within an Additive Manufacturing context, which have received very little research attention. It develops a novel conceptual model, and through a large-scale industry survey provides quantified evidence of the impact of Additive Manufacturing on the supply chain. To date, much of the supply chain research is exploratory and qualitative; the quantitative evidence presented in this work, therefore, makes an important and original contribution to both research and practice.