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Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

PurposeThis paper examines Supply Chain Risk Management (SCRM) from a holistic systems thinking perspective by considering different typologies that have evolved as a result of earlier research. The aim of the research reported in this paper is the identification of important strategic changes in the field and to outline future requirements and research opportunities in SCRM. Design/methodology/approachThe Systematic Literature Review (SLR) methodology employed by our research was used to evaluate and categorise a literature survey of quality articles published over a period of 10 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). Additionally, the findings from the SLR have been strengthened through cross validation against results obtained from an associated text mining activity. FindingsThe SLR methodology has provided a rich, unbiased and holistic picture of the advances in the field of SCRM. Consequently, important new research areas have been identified based on a multiperspective descriptive and thematic data analysis. In addition, our analysis based on evolved typologies indicates a growth of SCRM from a nascent to a fairly established activity over the past decade. Practical implicationsThe systematic approach undertaken for the literature review will provide future researchers and managers with an insightful understanding of the scope of the SCRM field. Also, the literature review provides important clues on new research directions for SCRM through identification of gaps in current knowledge. Originality/valueThe holistic approach to SCRM was found to be an important missing link in earlier literature surveys. The outcome of the Systematic Literature Review reported in this paper has provided critical insights into the present and future scope of the SCRM field. The identified research insights, gaps and future directions will encourage new research techniques with a view to managing the risks in the globalized supply chain environment. KeywordsSupply Chain Risk Management, Systematic Literature Review, Text Mining. Paper type Literature review INTRODUCTIONToday's e-world has led to an information explosion from the countless data sources that appear on a daily basis. Supply Chain Risk Management (SCRM) is an area that has recently been receiving a great deal of interest from academics and practitioners. SCRM is believed to be in an emerging and promising new field by researchers (Sodhi et al., 2012) but has a number of open-ended boundaries in its scope. Various authors have carried out a literature review on SCRM at various stages over the last 10 years (e.g. Juttner et al., 2003;Vanany et al., 2009;Rao and Goldsby, 2009) who provide a good platform for researchers and practitioners trying to make sense of the on-going research and identify the current state-of-art. However, narrative literature reviews are believed to lack thoroughness and rigour (Tranfield et al., 2003). On the contrary, evidence based reviews are considered to be more thorough and transparent as they provide...

Purpose -With increasing exposure to disruptions, it is vital for supply chains to manage risks proactively. Prediction of potential failure points and overall impact of these risks is challenging. In this paper, systems thinking concepts are applied for modelling supply chain risks. The purpose of this paper is to develop a holistic, systematic and quantitative risk assessment process for measuring the overall risk behaviour. Design/methodology/approach -A framework for supply chain risk management (SCRM) is developed and tested using an industrial case study. A systematically developed research design is employed to capture the dynamic behaviour of risks. Additionally, a system-based supply chain risk model is conceptualized for risk modelling. Sensitivity modelling results are combined for validating the supply chain risk model. Findings -The systems approach for modelling supply chain risks predicts the failure points along with their overall risk impact in the supply chain network. System-based risk modelling provides a holistic picture of risk behavioural performance, which is difficult to realise through other research methodologies commonly preferred in SCRM research. Practical implications -The developed framework for SCRM is tested in an industry setting for its viability. The framework for SCRM along with the supply chain risk model is expected to benefit practitioners in understanding the intricacies of supply chain risks. The system model for risk assessment is a working tool which could provide a perspective of future disruptive events. Originality/value -A holistic, systematic and quantitative risk modelling mechanism for capturing overall behaviour of risks is a valuable contribution of this research. The paper presents a new perspective towards using systems thinking for modelling supply chain risks.

This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge based support in a single coherent framework. Best practices are reused through the process model. rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry.

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Managing supply chain risks has received increased attention in recent years, aiming to shield supply chains from disruptions by predicting their occurrence and mitigating their adverse effects. At the same time, the resurgence of Artificial Intelligence (AI) has led to the investigation of machine learning techniques and their applicability in supply chain risk management. However, most works focus on prediction performance and neglect the importance of interpretability so that results can be understood by supply chain practitioners, helping them make decisions that can mitigate or prevent risks from occurring. In this work, we first propose a supply chain risk prediction framework using data-driven AI techniques and relying on the synergy between AI and supply chain experts. We then explore the trade-off between prediction performance and interpretability by implementing and applying the framework on the case of predicting delivery delays in a real-world multi-tier manufacturing supply chain. Experiment results show that prioritising interpretability over performance may require a level of compromise, especially with regard to average precision scores.