A hybrid risks-informed approach for the selection of supplier portfolio

In conventional supplier selection approaches, cost consideration is usually emphasised and it renders a vulnerable supply chain with various risks. This article aims to develop a quantitative approach for modelling both supply chain operational risks and disruption risks to support decision-making with regard to order allocation and risk mitigation. We introduce two types of risk evaluation models: value-at-risk (VaR) and conditional value-at-risk (CVaR). Specifically, VaR is used to measure operational risks caused by improper selection and operations of a supplier portfolio to the stochastic demand, which may frequently occur but result in relatively small losses to supply chains; CVaR is used to evaluate disruption risks that are less frequent and tend to cause significant damage. After incorporating risk factors into a probability-based multi-criteria optimisation model, different methods and parameters are compared and tested to determine the factors that may influence the supplier selection process. Computational examples by simulation are presented to illustrate the approach and how decision-makers make trade-offs between costs and hybrid risks.     

Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches

Supply chains are becoming more lengthy and complex due to globalisation and vertical integrations. In this context, adopting proactive approaches is needed for dealing with changing risks and vulnerabilities for securing supply chain systems. Supply chain risks are interlinked and thus, one mitigation strategy can reduce many of other supply chain risks. For example, aggregate or pooling demand reduces forecast risks, capacity risks and inventory risks. Also, some of the risk mitigation strategies have negative influences over certain supply chain risks as adding capacity has a negative influence on capacity risks. Twelve major supply chain risk categories and 21 risk mitigation strategies with typical focus on electronics manufacturing supply chains have been identified. A combination of grey theory and digraph-matrix methodologies has been used for quantifying various supply chain risk mitigation strategies and this approach is not seen in literature till date. The proposed model was also tested taking a case study of an Indian electronics manufacturing company. Obtained results were also subject to sensitivity analysis. The net positive influence values of risk mitigation strategies proposed in this research could effectively be used by top management for ascertaining their risk mitigation strategies for better management of supply chains as a whole.     

Supply chain risk modelling and mitigation

In today’s global competitive environment, supply chains are more susceptible to vulnerability due to the increasing occurrence of internal and external risk events. In addition, the trend associated with lean management, which involves reducing inventory, leads to more dependency of supply chain partners on each other which exacerbates risk exposure of companies in the supply chain. This creates the need for more effective management of supply chain risks. In this research, a methodology based on Bow-Tie analysis and optimisation techniques is proposed to quantify and mitigate supply chain risks. The proposed methodology takes into consideration risk interconnections, and it identifies the best combination of mitigation strategies under budget constraints. A real case study from a high-end server manufacturing environment is presented. Results from the case study showed that the proposed methodology for risk modelling and mitigation can effectively be used to quantify the risks and achieve the required risk reduction at minimum cost while considering risk correlations.     

Allocation flexibility for agribusiness supply chains under market demand disruption

This paper develops a multi-commodity multi-period optimisation model to analyse market demand disruption risk in agribusiness supply chains. It investigates the role of allocation flexibility and the effectiveness of multiple risk management strategies for achieving allocation flexibility. A robust optimisation formulation is used to obtain risk-averse solutions for an objective combining expected profit and risk. Numerical results are presented for a real-life case study of Zespri’s kiwifruit supply chain. The results show that allocation flexibility is effective for mitigating market demand disruption risk. Three proposed risk management strategies, namely diversified demand market, backup demand market and flexible rerouting, improve both expected profit and risk measures. While diversified demand market and backup demand market strategies are equally important for all decision-makers, flexible rerouting is especially significant for less risk-averse decision-makers.     

A two-period model for selection of resilient multi-tier supply portfolio

A two-period decision-making model is developed for selection of resilient supply portfolio in a multi-tier supply chain under disruption risks. The planning horizon is divided into two aggregate periods: before and after the disruption. The resilience of the supply chain is achieved by selection ahead of time primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously, recovery and transshipment portfolios are determined for each disruption scenario and decisions on usage the pre-positioned inventory are made to minimise expected cost or maximise expected service level. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. The findings also indicate that the developed portfolio approach with an embedded network flow structure leads to computationally efficient stochastic mixed integer program with a strong LP relaxation.     

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.     

A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model

In this paper, a general framework for child injury prevention and a multi-objective, multi-dimensional mixed 0-1 knapsack model were developed to determine the optimal time to introduce preventive measures against child injuries. Furthermore, the model maximises the prevention of injuries with the highest risks for each age period by combining preventive measures and supervision as well as satisfying budget limits and supervision time constraints. The risk factors for each injury, variable, and time period were based on risk priority numbers (RPNs) obtained from failure mode and effects analysis (FMEA) methodology, and these risk factors were incorporated into the model as objective function parameters. A numerical experiment based on several different situations was conducted, revealing that the model provided optimal timing of preventive measures for child injuries based on variables considered.     

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.     

Integrated production–distribution planning in two-echelon systems: a resilience view

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage’s objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.     

A quantitative risk assessment methodology for construction project

It is observed that most of the infrastructure projects fail to meet their cost and time constraints, which will lead to a low return on investment. The paper highlights that the present risk management tools and techniques do not provide an adequate basis for response selection in managing critical risks specific to infrastructure projects. This paper proposes a risk quantification methodology and demonstrates its application for an industrial construction project. A case study is used to present an application of the proposed risk management methodology to help organisations efficiently choose risk response strategy and allocate limited resources. The research adopts an integrated approach to prioritize risks using Group Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) and to quantify risks in terms of overall project delays using Judgemental Risk Analysis Process (JRAP), and Monte Carlo Simulation (MCS). A comparison between the results of qualitative risk analysis using GTOPSIS and quantitative risk analysis i.e., JRAP and MCS is presented. It is found that JRAP along with MCS could provide some powerful results which could help the management control project risks. The crux of this paper is that the risks are highly dependent on project schedule and the proposed methodology could give a better risk priority list because it considers slackness associated with the project activities. The analysis can help improve the understanding of implications of specific risk factors on project completion time and cost, while it attempts to quantify risks. In turn, this enables the project manager to devise a suitable strategy for risk response and mitigation.     

考虑土体参数空间变异性边坡失稳风险定量评估

边坡失稳风险定量评估是制定滑坡风险防治措施和建立滑坡风险预警体系的重要前提,然而目前方法不能有效地解决考虑土体参数二维空间变异性的边坡失稳风险定量评估问题,该文在蒙特卡洛模拟（MCS）和极限平衡分析框架下提出了一种有效的考虑参数二维空间变异性的边坡失稳风险定量评估方法,依次从土体参数二维空间变异性模拟、空间变异边坡稳定性分析、边坡代表性滑动面识别和边坡失稳风险定量分析这四个方面对提出方法进行了详细介绍。最后通过对3种工况下两层不排水饱和黏土边坡的失稳风险定量分析验证了提出方法的有效性。结果表明:该文提出方法具有以下优势:1）可以有效地识别空间变异边坡代表性滑动面,即边坡关键破坏模式;2）具有较高的边坡失稳风险评估计算效率,可为解决考虑参数二维空间变异性的低概率水平边坡失稳风险定量评估问题提供一个有效的工具;3）可以定量地计算每个关键破坏模式对边坡失效概率和失稳风险的贡献权重,从而可为制定有效的滑坡风险控制措施提供重要的参考依据。     

Cost-Variability-Sensitive Preventive Maintenance Considering Management Risk

Traditional preventive maintenance policies, such as age replacement, periodic replacement under minimal repair, and replacement policy N, are all studied based on the expected cost criteria without considering the management risk due to the cost variability. As a result, these policies could be significantly beyond the anticipated maintenance budget allocation and lead to crisis. In order to solve this problem, a new analysis methodology is proposed in this paper to consider the effects of both cost expectation and cost variability on the optimal maintenance policy. A new concept of the long-run variance of the cost is defined to represent the maintenance management risk, and then the objective function is revised accordingly to achieve an optimal cost-variability-sensitive maintenance policy. Based on the proposed framework, three traditional preventive maintenance policies have been reinvestigated and the effect of the variability sensitivity on the optimal policies is further analyzed, which reveals general management insights and explicates the search bound of the optimal solution. An example is given to illustrate the importance and the effectiveness of the proposed methodology. Compared with the traditional optimal maintenance policy, the numerical solution shows that the proposed variability-sensitive optimal policies can significantly reduce the maintenance management risk with only a small increase in the expected cost.     

Entropy-based model for the ripple effect: managing environmental risks in supply chains

Due to increasing diversity and growing size of modern industrial supply chains, today problems of identification, assessment and mitigation of disruption risks become challenging goals of the supply chain risk management. In this paper, we focus on environmental (ecological) risks in supply chains which represent threats of adverse effects on living organisms, facilities and environment by effluents, emissions, wastes, resource depletion, etc. arising due to supply chain’s activities. Harmful environmental disruptions may ripple through the supply chain components like a wave. The paper presents the entropy-based optimisation model for reducing the supply chain model size and assessing the economic loss caused by the environmental risks subject to the ripple effect. A main advantage of the suggested entropy-based approach is that it permits to essentially simplify the hierarchical tree-like model of the supply chain, at the same time retaining the basic knowledge about main risk sources.     

Models for supplier selection and risk mitigation: a holistic approach

According to a study conducted by PwC and the Business Continuity Institute in 2013, 75% of companies experience at least one major supply chain disruption a year and majority of the disruptions were caused by supply-related problems. With an increasing emphasis on upstream risk, risk management in supplier selection has become a critical issue faced by companies. Although previous studies proposed different methods and tools for effective and efficient supplier selection, only few approaches have attempted to incorporate risk mitigation strategies in supplier selection decisions. Our study aims to fill this gap by considering a wide range of quantitative and qualitative risk factors in supplier selection and evaluates the efficacy of alternative risk mitigation strategies in this context. Moreover, we suggest that both upstream and downstream strategies should be utilised simultaneously rather than relying on a single type of strategy. We further suggest that it is critical to align upstream and downstream risk mitigation strategies to reduce risk. We employ multi-objective optimization-based simulation in developing a decision model and consider data from an automotive parts manufacturer to demonstrate the application of our approach.     

Risk informed resource allocation policy: safety can save costs

During economic doldrums, decision making on investments for safety is even more difficult than it already is when funds are abundant. This paper attempts to offer some guidance. After stating the present challenge to prevention of losses in the process industries, the systematic approach of quantified risk assessment is briefly reviewed and improvements in the methodology are mentioned. In addition, attention is given to the use of a risk matrix to survey a plant and to derive a plan of action. Subsequently, the reduction of risk is reviewed. Measures for prevention, protection, and mitigation are discussed. The organization of safety has become at least as important as technical safety of equipment and standards. It is reflected in the introduction of a safety management system. Furthermore, the design process in a pro-active approach is described and the concept of inherent safety is briefly addressed. The concept of Layer of Protection Analysis is explained and also the reason why it is relevant to provide a cost-benefit analysis. Finally, after comments regarding the cost of accidents, the basics of costing and profitability are summarized and a way is suggested to apply this approach to risk-reducing measures. An example is provided on how a selection can be made from a number of alternatives.     

Managing supply disruption through procurement strategy and price competition

When facing supply disruptions, the emergency procurement strategy and the optimal allocation procurement strategy are widely used strategies to manage supply risks. In this paper, buyers use these types of procurement strategies under the threat of supply disruption and engage in price competition. The structural properties of the procurement strategies are characterised by their reliability thresholds. We find that reliability thresholds play a critical role in buyer procurement strategy choices, which are related to the sales price, underage cost and differentials in unit procurement cost. A solution procedure is proposed to determine the equilibrium strategy profile. The effects of reliability levels and costs on the equilibrium prices, expected profits and equilibrium strategy profiles are explored. We extend the basic model to investigate the case of symmetric competition where buyers can freely choose their procurement strategy. The results show that in most cases, the competing buyers will choose the same strategy, whether an optimal allocation strategy with single sourcing or an emergency procurement strategy with dual sourcing. In a special parameter setting, the buyers will choose either strategy because they yield identical expected profits; this leads to multiple equilibria. We also find the equilibrium to be Pareto efficient.     

Developing a resilient supply chain through supplier flexibility and reliability assessment

In this study, we examine the optimal allocation of demand across a set of suppliers in a supply chain that is exposed to supply risk and environmental risk. A two-stage mixed-integer programming model is used to develop a flexible sourcing strategy under disruptions. Our model integrates supplier selection and demand allocation with transportation channel selection and provides contingency plans to mitigate the negative impacts of disruptions and minimise total network costs. Finally, a numerical example is presented to illustrate the model and provide insights. The findings suggest that developing contingency plans using flexibility in suppliers’ production capacity is an effective strategy for firms to mitigate the severity of disruptions. We also show that flexibility and reliability of the suppliers and regions play a significant role in determining contingency plans for during disruption. Findings generally show that highly flexible suppliers receive less allocation, and their flexible capacity is reserved for disruptions. For firms that do not incorporate risk management into supplier selection and allocation, the recommendation is to source from fewer, more reliable suppliers with less risk of disruption. Our findings also emphasise that the type of disruption has important implications for supplier selection and demand allocation. This study highlights the supply chain risk management strategy of regionalising as a means for minimising the impact of environmental disruptions.     

Dual sourcing in managing operational and disruption risks in contract manufacturing

We study a US OEM that outsources its production to two contract manufacturers, a local manufacturer (e.g. in the US or Mexico) and a foreign manufacturer (e.g. in China). The local manufacturer is relatively reliable, but low margin. The foreign manufacturer offers high margin, but is subject to disruption risks. Both manufacturers experience some level of operational uncertainties, and the operational risks can be positively or negatively correlated. Disruption risks are modelled as a Poisson jump process at a random magnitude, and operational risks are modelled as correlated stochastic diffusion processes. We develop a stochastic dynamic programming formulation to characterise the OEM’s optimal capital allocation decision to contract manufacturers, and provide the necessary and sufficient conditions for each optimal decision. The objective of our study is to investigate how dual sourcing balances the risks and opportunities, when the OEM bears disruption risks and correlated operational risks. We find that the two manufacturers can be substitutes or complements to each other. Risk of disruption renders the unreliable foreign manufacturer less attractive, but has a moderating effect on the allocation to the local manufacturer. We also provide managerial implications of our study.     

Real option-based optimization for financial incentive allocation in infrastructure projects under public–private partnerships

Financial incentives that stimulate energy investments under public–private partnerships are considered scarce public resources, which require deliberate allocation to the most economically justified projects to maximize the social benefits. This study aims to solve the financial incentive allocation problem through a real option-based nonlinear integer programming approach. Real option theory is leveraged to determine the optimal timing and the corresponding option value of providing financial incentives. The ambiguity in the evolution of social benefits, the decision-maker’s attitude toward ambiguity, and the uncertainty in social benefits and incentive costs are all considered. Incentives are offered to the project portfolio that generates the maximum total option value. The project portfolio selection is formulated as a stochastic knapsack problem with random option values in the objective function and random incentive costs in the probabilistic budget constraint. The linear probabilistic budget constraint is subsequently transformed into a nonlinear deterministic one. Finally, the integer non-linear programming problem is solved, and the optimality gap is computed to assess the quality of the optimal solution. A case study is presented to illustrate how the limited financial incentives can be optimally allocated under uncertainty and ambiguity, which demonstrates the efficacy of the proposed method.