What it takes to design a supply chain resilient to major disruptions and recurrent interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents an optimization model for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose probability of occurrence and magnitude of impact can be mitigated through fortification investments. Numerical results and managerial insights obtained from model implementation are presented. Our analysis focuses on how supply chain design decisions are influenced by facility fortification strategies, a decision maker’s conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints. Finally, examining the performance of the proposed model using a Monte Carlo simulation method provides additional insights and practical implications

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies

Supply Chain Risk of Obsolescence at Simultaneous Robust Perturbations

The earlier planning methods of supply chains (SC) in the skeleton of the extended material requirements planning (EMRP), where the time horizon of the reverse Laplace is infinite, were not convenient to estimate the impact of the technical obsolescence of the product or technology involved, which has economic, environmental, and social consequences. Therefore, the perturbations of timing are presented until the possible obsolescence, with parallel execution of the network simulation model (NSM) to evaluate the impact of the finite lifespan on the value of the chain. The EMRP, as well as the NSM, are based on the skeleton of the material requirements planning model, where delays and their perturbations are presented transparently. Contrary to the previous studies of the net present value (NPV) in the EMRP skeleton, where the infinite horizon is assumed, the impact of shortening the horizon of activities is shown here, in order to also evaluate the risk of financing investments in the SC with a shorter lifespan of products or technology. Owing to the simultaneous appearances of the stochastic variables, the parallel execution and exchanging of data, using NSM is advised. The procedures for estimation of correction factors of the NPV and their values are given.This research was partly funded by the Slovenian Research Agency, grant P5-0364 and J6-9396, and partly by the University of Padua

Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Resilient Aircraft Sustainment: Quantifying Resilience through Asset and Capacity Allocation

Decision makers lack a clear, generalizable method to quantify how additional investment in inventory and capacity equates to additional levels of resilience. This research facilitates a deeper understanding of the intricacies and complex interconnectedness of organizational supply chains by monetarily quantifying changes in network resilience. The developed Area under the Curve metric (AUC) is used to quantify the level of demand that each asset allocation can meet during a disruptive event. Due to its applicability across multiple domains, the USAF F-16 repair network in the Pacific theater (PACAF) is modeled utilizing discrete event simulation and used as the illustrating example. This research uses various levels of production capacity and response time as the primary resilience levers. However, it is essential to simultaneously invest in inventory and capacity to realize the greatest impacts on resilience. Real-world demand and cost data are incorporated to identify the inherent cost-resilience relationships, essential for evaluating the response and recovery capabilities across the developed scenarios. Results indicate that recovery capacity and response time are the greatest drivers of recovery after a disruption. Additionally, numerous network designs employing various levels of design flexibility are evaluated and recommended for future capacity expansion

An Optimized Resource Allocation Approach to Identify and Mitigate Supply Chain Risks using Fault Tree Analysis

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains

Containing Risk when Maximizing Supply-Chain Performance

The objective of this dissertation is to develop and test an approach that will quantify the level of risk in the supply chain, evaluate the cost and impact of risk mitigation strategies, validate event management protocols pre-implementation, and optimize across a portfolio of risk mitigation strategies. The research integrates a Mixed Integer Linear Programming (MILP) model and a Discrete Event Simulation model to investigate a production-inventory-transportation problem subject to risk. The MILP model calculates the optimal Net Profit Contribution of the supply chain in the absence of risk. Deviation risks are introduced as volatility in final demand and lead times, with lead time volatility affecting raw material lead times from suppliers to manufacturing plants and finished goods lead times from manufacturing plants to the warehouses. Disruption risks are modelled as temporarily impeding production at the manufacturing plants, in-bound distribution of raw materials from suppliers to the manufacturing plants, and out-bound distribution of finished goods from the manufacturing plants to warehouses. Computational experiments are run to examine the impact of risk on the supply chain. Further experiments explore the consequences of three risk mitigation strategies (inventory placement, expediting, and production flexibility) on supply chain performance in the presence of risk with the aim of discovering whether one strategy dominates or whether a portfolio approach to risk mitigation performs best. In sum, this research seeks to develop a framework that can inform efforts in understanding, planning for and controlling risk in the supply chain

Investigation of the Effect of e-Platform Information Security Breaches: A Small and Medium Enterprise Supply Chain Perspective

Many small and medium enterprises (SMEs) engage in dyadic information integration partnerships or partial integration with their direct suppliers and customers. They often utilise e-commerce or cloud computing technology platforms hosted by third-party providers to leverage such partnerships. However, information security breaches and disruptions caused by cyber-attacks are commonplace in the IT industry. The effects of said disruptions and breaches on e-commerce businesses under varied disruption conditions are still uncertain. Furthermore, the effect of security breaches on non-participating members of the supply chain is poorly understood, especially under various disruption profiles. Using discrete event modelling, this study explores the impact of disruption caused by information security breaches on supply chain performance and the externality effect of partial integration on non-participants. We also examine the impact of breach disruption frequency and remediation length on supply chain performance with varying levels of information sharing. These impacts were studied under two typical inventory replenishment policies for SMEs. It was determined that remediation length should be a prioritised factor in impact management and that flexibility in the inventory replenishment policy can help mitigate the impact of information disruption on the inventory performance of businesses, especially that of non-participants, in information-sharing partnerships