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This paper attempts to address three key objectives. The primary aim is to enhance sourcing strategy for a centralized and coordinated multitier multiple suppliers networks with uncertain demand and supplier failure risks. The second objective is to enumerate all possible practical supplier(s) failure scenarios and quantify expected loss of demand cost. Finally, the work illustrates statistical experimentation to identify “influential” variables that can significantly impact the expected supply network and loss costs.

A seven-step solution framework is proposed to derive an optimal sourcing strategy for the specific network configuration with varied supplier failure scenarios. Five distinct models are formulated to address all possible scenarios of supplier failure events. Mixed-integer nonlinear programming technique is used to derive expected supply network cost and loss cost. The solution framework is verified using a real-life case.

A cross-case analysis indicates that an increase in suppliers' failure risk (SFR) probabilities or customer demand rate increases the expected loss of demand costs for a multitier supply network. Besides, an increase in unit component prices increases the expected supply network cost.

A two-tier automotive supply network for a single product is considered for all case studies.

The enhanced strategy can facilitate practitioners enumerate different supply network failure scenarios and implement the best solution.

There is no evidence of earlier research to derive optimal sourcing strategy for a centralized, coordinated multitier multiple supplier's network, considering demand uncertainties and SFR.

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).

A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.

The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.

A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.

The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

The purpose of this paper is to present a generic framework to assess and simulate outsourcing risks in the supply chain.

This combination approach involves a qualitative risk analysis methodology termed as the supply chain risk‐failure mode and effect analysis (SCR‐FMEA) which integrates risk identification, analysis and mitigation actions together to evaluate supply chain outsourcing risk. The qualitative risk assessment will allow risk manager to provide a visual presentation of imminent risks using the risk map. Monte Carlo simulation (MCS) on the imminent risks of delivery outsourcing using the Milk‐Run system is adopted.

With basic statistical concepts, key performance variables and the risk of delivery outsourcing are analyzed. It is found that a newly implemented delivery outsourcing arrangement on the Milk‐Run system reduces the average customer lead‐time and total cost. However, a certain extent of risk or uncertainty can still be detected due to the presence of variation.

This paper reveals that company can manage the risk by adopting a systematic method for identifying the potential risks before outsourcing and MCS can be applied for examining the quantifiable risks such as lead time and cost.

The paper provides a generic guideline for practitioners to assess logistics outsourcing, especially for logistics management consultants and professionals for evaluating the risk and impact of outsourcing. It is believed that the proposed risk assessment framework can help to analyze the operational cost uncertainty and ensure the stability of the supply chain. However, the limitation of this research is that the full spectrum of outsourcing risk, especially the non‐quantifiable risk may not be analyzed by MCS.

This paper proposed an integrated framework which combines qualitative and quantitative method together for managing outsourcing risk. This research provides a standardized metric to quantify risk in the supply chain so as to determine the effectiveness of outsourcing.

The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk management in this area, this will be qualified as an important research area. This study aims to develop a framework for assessing the sustainable supply chain risk management in the realm of the food industry (confectionery and chocolate) with a case study of three generic companies denotes as A1–A3. The proposed risk management was evaluated in three aforementioned manufacturing companies, and these three companies were ranked by the Fuzzy-Weighted Aggregated Sum Product Assessment (F-WASPAS) method in EXCEL.

The evaluation was carried out using integrated multi-criteria decision-making methods Best-Worst method (BWM)-WASPAS. Via an extensive literature review in the area of sustainable supply chain, sustainable food supply chain and risks in this, 9 risk criteria and 59 sub-criteria of risk were identified. Using expert opinion in the food industry, 8 risk criteria and 39 risk sub-criteria were identified for final evaluation. The final weight of the main and sub-criteria was obtained using the F-BWM method via LINGO software. Risk management in the sustainable supply chain has the role of identifying, analyzing and providing solutions to control risks.

The following criteria in each group gained more weight: loss of credibility and brand, dangerous and unhealthy working environment, unproductive use of energy, human error, supplier quality, quality risk, product perishability and security. Among the criteria, the economic risks have the highest weight and among the alternatives, A3 has obtained first ranking.

Modeling of risk for the food supply chain is the unique contribution of this work.

The purpose of this paper is to address the increasingly important issue of the impact of product design on supply chain risk management in an era of global supply arrangements. The need to include product design considerations in the development of global supply chain strategies is highlighted.

The research methodology is based on an in‐depth longitudinal case study of a major UK retailer. Data collection tools included observation of supplier meetings/workshops, semi‐structured interviews and access to key company documentation and archives.

This paper provides a framework for design‐led supply chain risk management and thus presents a case for recognising design as more than a creative function but as a platform to manage risk in supply chains.

The empirical research reported in this paper is specific to the clothing manufacturing and fashion retail industry. Though the findings will most likely apply to all industries and supply chains where design has an integral role and plays an important part in the competitiveness of the final product, there would be benefit in extending the research into other sectors.

The increased trends to outsourcing and offshore sourcing and the elimination of trade barriers have added to the competitive pressures faced by clothing retailers, hence frameworks to manage supply chain risks are significant to the survival of companies from this sector.

Whilst there is a growing literature in the field of supply chain risk there is less empirical evidence providing practical examples of the impact of product design on risk. Design‐led risk management offers a novel approach to mitigating supply chain risk.

Most of the extant literature on resilience builds on normative, conceptual or silo approaches, thereby lacking an integrative approach to cold chain logistics risks (CCLRs) and resilience. The purpose of this paper is to bridge the current research gap by developing a model, based on broad empirical evidence, of the interplay between CCLRs, resilience and firm performance (FP) in perishable product supply chains (PPSCs).

A mixed method approach is used with qualitative data from interviews and quantitative data from a survey across the supply chain. The analysis is framed by contingency theory and resource-based theory.

Four significant sources of CCLRs and six resources used to build resilience are identified. Then, supply chain resilience (SCR) as a moderator of the negative relationship between CCLRs and FP is corroborated.

The findings will help improve managerial understandings of critical sources of risks in cold chain logistics and resources indispensable to build resilience. The scope of the research is cold chain logistics for PPSCs, which has relevance to other cold supply chains as well.

While some theoretical frameworks suggest resilience being a moderator in the negative relationship between cold chain risks and a firm’s performance, this study empirically tests this relationship using the survey across the entire supply chain. A new empirically and theoretically driven definition of SCR is also developed.

The purpose of this paper is to create a resilient supply chain (SC) plan to contain disruptions and risks in the overall operations of a business.

The study integrates resilience considerations in a business planning model that formulates resilience performance (RP) of SC functions in terms of flexibility, reliability, and similar system factors. It evaluates the RP of SC plans and determines their vulnerability considering required and planned resources. The model estimates the possible effects of disasters on vulnerable functions using a scenario-based analysis and plans containment options. It also includes decision options for deploying resources to achieve the expected levels of resilience by preventing potential vulnerabilities. The model takes optimum decision in a what-if approach by comparing performance of the existing business plan, with options for containing the vulnerabilities inherent in not considering potential risks when planning to fulfill market demand, and the performance of a resilient plan that includes decision options to prevent vulnerabilities where possible and mitigate them otherwise.

It is possible, for example, to evaluate RP of SC plans, identify vulnerable functions, and decide optimum option to create resilient business system.

The present study takes a generic approach and creates bases to explore its application in any industry-based case.

The research introduces formulations for RPs and vulnerability indices that can be included in a planning model to create a resilient SC.

In industry today the majority of managers appear to be aware of the importance of the purchasing function in manufacturing organisations and of the need to secure supplies of materials at the best available prices. What is not well recognised, however, especially by the senior non‐purchasing managers in the majority of manufacturing organisations, is the need to consider purchasing as a risk‐taking function and the need for supply management to be involved in strategic planning processes. Furthermore, there is a growing body of evidence to suggest an alarming ignorance of the need for senior executives in procurement operations to make strategic evaluations of their supply markets, to evaluate the risks involved in particular supply channels and to be able to relate these to their company's marketing objectives. The strategic planning requirements of purchasing are not well understood at a fundamental level and this may, in part, explain the view held widely in manufacturing organisations that purchasing is essentially an administrative function, concerned mainly with administrative procedures and tactical decisions, and need not be involved in the forward corporate strategy and planning processes of the organisation.

Supply chain risk management (SCRM) is of growing importance, as the vulnerability of supply chains increases. The main thrust of this article is to describe how Ericsson, after a fire at a sub‐supplier, with a huge impact on Ericsson, has implemented a new organization, and new processes and tools for SCRM. The approach described tries to analyze, assess and manage risk sources along the supply chain, partly by working close with suppliers but also by placing formal requirements on them. This explorative study also indicates that insurance companies might be a driving force for improved SCRM, as they now start to understand the vulnerability of modern supply chains. The article concludes with a discussion of risk related to traditional logistics concepts (time, cost, quality, agility and leanness) by arguing that supply chain risks should also be put into the trade‐off analysis when evaluating new logistics solutions – not with the purpose to minimize risks, however, but to find the efficient level of risk and prevention.

This study aims to consider the developing of strategic use of big data in association with long-linked physical goods supply focusing on risk management.

Analysis is grounded on a case study of organizing the import of machine parts from Shanghai, China, to Norway. An analytical framework is developed through a literature review on long linked supply chains, big data and risk management.

Analysis reveals that big data use in this scenario encompasses mainly around handling risks associated with transformations in the supply chain, a data-driven approach. Complexity is founded in transformation – the flows of goods and information. Supply chain dynamics represent an important source for data acquisition for big data analytics.

The qualitative nature of the study limits the aim of generalization. An alternative view of big data as process is discussed and proposed, adapted to supply chain management and industrial marketing functionality.

This is the first part in an ongoing research project aimed at developing a research approach to study information technology use in the inherently complex setting and scope of a long linked supply network. This scope of investigation enhances big data associated with operations dynamics providing foundation for future research on how to use big data to mitigate risk in long linked supply chains.