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.     

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.     

Two-period vs. multi-period model for supply chain disruption management

A novel two-period modelling approach is developed for supply chain disruption mitigation and recovery and compared with a multi-period approach. For the two-period model, planning horizon is divided into two aggregate periods: before disruption and after disruption. The corresponding mitigation and recovery decisions are: (1) primary supply and demand portfolios and production before a disruption, and (2) recovery supply, transshipment and demand portfolios and production after the disruption. In the multi-period model, a multi-period planning horizon is applied to account for a detailed timing of supplies and production. The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. While the simplified two-period setting may overestimate (for best-case capacity constraints) or underestimate (for worst-case capacity constraints) the available production capacity, it can be easily applied in practice for a fast, rough-cut evaluation of disruption mitigation and recovery policy. The findings indicate that for both two- and multi-period setting, the developed multi-portfolio approach leads to computationally efficient mixed integer programming models with an embedded network flow structure resulting in a very strong linear programming relaxation.     

供应中断风险下供应链恢复能力投资决策及协调

摘要： 〖HTSS〗为了减缓供应商生产运营中断对制造商带来的供应中断风险,制造商需要在突发事件前优化供应链恢复能力投资水平。引入CVaR来刻画供应链在突发事件下的应急目标,建立了一定置信水平控制下的供应链恢复能力投资的决策模型,进而揭示了制造商风险规避程度对供应链最优恢复能力投资水平的影响。分权供应链下,制造商运用基于绩效的契约来激励供应商投资合适的恢复能力,以协调供应链。结论表明,CVaR可以恰当描述供应链恢复的能力投资行为,当风险规避程度越高,供应链最优恢复能力投资越多;当单位中断时间的惩罚系数的条件期望值等于单位中断时间的商誉成本的条件期望值时,供应链进入协调状态。     

A four-phase AHP–QFD approach for supplier assessment: a sustainability perspective

Recently, companies have become increasingly aware of the need to evaluate suppliers from a sustainability perspective. Introducing the triple bottom line (economic, social, and environmental performance) into supplier assessment and selection decisions embeds a new set of trade-offs, complicating the decision-making process. Although many tools have been developed to help purchasing managers make more effective decisions, decision support tools, and methodologies which integrate sustainability (triple bottom line) into supplier assessment and selection are still sparse in the literature. Moreover, most approaches have not taken into consideration the impact of business objectives and requirements of company stakeholders on the supplier evaluation criteria. To help advance this area of research and further integrate sustainability into the supplier selection modelling area, we develop an integrated analytical approach, combining Analytical Hierarchy Process (AHP) with Quality Function Deployment (QFD), to enable the ‘voice’ of company stakeholders in the process. Drawing on the sustainable purchasing strategy development process, our AHP–QFD approach comprises four hierarchical phases: linking customer requirements with the company's sustainability strategy, determining the sustainable purchasing competitive priority, developing sustainable supplier assessment criteria, and lastly assessing the suppliers. An illustrative example is provided to demonstrate the application of the proposed approach.     

Supplier selection under risk of delivery failure: a decision-support model considering managers’ risk sensitivity

This paper studies the problem of supplier selection and order allocation in a retail supply chain (comprising suppliers, a central purchasing unit and outlets) under disruption risk. The final demand is deterministic. Suppliers are located in different geographic areas, and supplies are subject to a positive probability of disruption. Different capacity and failure probabilities for each supplier are considered. Our analysis focuses on the insurance versus profitability trade-off faced by a supply manager who buys from suppliers for the outlets. Instead of determining optimal decisions given an objective function and the risk sensitivity of the decision-maker, we use a mixed integer linear programming approach to provide decision-making support that shows a supply manager the ‘elasticity of (expected) losses versus (expected) profits’. Under this model, and depending on the profit-and-loss targets, a supply manager of known risk sensitivity (i.e. risk aversion and loss aversion) can make better decisions when choosing suppliers. Moreover, taking into account, the impact of the share of fixed costs that must be covered by the operation, we consider the net values of expected profit and loss. We discuss the potential influence of the level of the firm’s fixed costs on the supply strategy. In particular, we show how the minimum value of the gross margin needed for the strategy’s profitability affects that strategy. A numerical application is conducted to illustrate the contribution of our decision-making support mechanism, and several managerial insights are obtained.     

Integrated supply chain risk management via operational methods and financial instruments

Supply chain risk management (SCRM) is an emerging field that generally lacks integrative approaches across different disciplines. This study contributes to narrowing this gap by developing an integrated approach to SCRM using operational methods and financial instruments. We study a supply chain composed of an aluminium can supplier, a brewery and a distributor. The supply chain is exposed to aluminium price fluctuation and beer demand uncertainty. A stochastic optimisation model is developed for managing operational and financial risks along the supply chain. Using this model as a base, we compare the performance of an integrated risk management model (under which operational and financial risk management decisions are made simultaneously) to a sequential model (under which the financial risk management decisions are made after the operational risk management decisions are finalised). Through simulation-based optimisation and using experimental designs and statistical analyses, we analyse the performance of the two models in minimising the expected total opportunity cost of the supply chain. We examine the supply chain performance as a function of three factors, each at three levels: risk aversion, demand variability and aluminium price volatility. We find that the integrated model outperforms the sequential model in most but not in all cases. Furthermore, while the results indicate that the supply chain improves its performance by being less risk averse, there exists a threshold beyond which accepting a higher risk level is not justified. Managerial insights are provided for various business scenarios experimented with.     

Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations

While logistics research recently has placed increased focus on disruption management, few studies have examined the response and recovery phases in post-disaster operations. We present a multiple objective, integrated network optimisation model for making strategic decisions in the supply distribution and network restoration phases of humanitarian logistics operations. Our model provides an equity-based solution for constrained capacity, budget and resource problems in post-disaster logistics management. We conduct designed experiments for this NP-hard problem to analyse important aspects of the integrated problem for both small- and large-sized networks: full vs. partial restoration and pooled vs. separate budgeting approach. Finally, we apply the model to a Hazus-generated regional case study based on an earthquake scenario and generate efficient Pareto frontiers to understand the trade-off between the objectives of interest.     

Supply chain resilience for single and multiple sourcing in the presence of disruption risks

This paper investigates the use of sourcing strategies to achieve supply chain resilience under disruptions. The coping strategies considered are single and multiple sourcing, backup supplier contracts, spot purchasing, and collaboration and visibility. Collaboration and visibility, which affect suppliers’ recovery capabilities and a buyer’s warning capability, have not been similarly modelled in the past. A scenario-based mathematical model is developed such that it considers objectives under uncertainties including disruption risks and operational risks. A broad numerical study examines its output for various risk attitudes in a decision-maker, ranging from risk neutral to risk averse. The sensitivity of procurement strategies to other key parameters such as recovery and warning capabilities is examined. One of the major findings is that buyer’s warning capability plays a vital role in enhancing supply chain resilience. We seek to build on these efforts to further support disruption planning and mitigation and to obtain a deeper understanding of the relationship between supply chain characteristics and resilience.     

Bayesian belief network-based framework for sourcing risk analysis during supplier selection

Increasing trend in global business integration and movement of material around the world has caused supply chain system susceptible to disruption involving higher risks. This paper presents a methodology for supplier selection in a global sourcing environment by considering multiple cost and risk factors. Failure modes and effects analysis technique from reliability engineering field and Bayesian belief networks are used to quantify the risk posed by each factor. The probability and the cost of each risk are then incorporated into a decision tree model to compute the total expected costs for each supply option. The supplier selection decision is made based on the total purchasing costs including both deterministic costs (such as product and transportation costs) and the risk-associated costs. The proposed approach is demonstrated using an example of a US-based Chemical distributor. This framework provides a visual tool for supply chain managers to see how cost and risks are distributed across the different alternatives. Lastly, managers can calculate expected value of perfect information to avoid a certain risk.