A contribution to supply chain design under uncertainty

Dans le contexte actuel des chaînes logistiques, des processus d'affaires complexes et des partenaires étendus, plusieurs facteurs peuvent augmenter les chances de perturbations dans les chaînes logistiques, telles que les pertes de clients en raison de l'intensification de la concurrence, la pénurie de l'offre en raison de l'incertitude des approvisionnements, la gestion d'un grand nombre de partenaires, les défaillances et les pannes imprévisibles, etc. Prévoir et répondre aux changements qui touchent les chaînes logistiques exigent parfois de composer avec des incertitudes et des informations incomplètes. Chaque entité de la chaîne doit être choisie de façon efficace afin de réduire autant que possible les facteurs de perturbations. Configurer des chaînes logistiques efficientes peut garantir la continuité des activités de la chaîne en dépit de la présence d'événements perturbateurs. L'objectif principal de cette thèse est la conception de chaînes logistiques qui résistent aux perturbations par le biais de modèles de sélection d'acteurs fiables. Les modèles proposés permettent de réduire la vulnérabilité aux perturbations qui peuvent aV, oir un impact sur la continuité des opérations des entités de la chaîne, soient les fournisseurs, les sites de production et les sites de distribution. Le manuscrit de cette thèse s'articule autour de trois principaux chapitres: 1 - Construction d'un modèle multi-objectifs de sélection d'acteurs fiables pour la conception de chaînes logistiques en mesure de résister aux perturbations. 2 - Examen des différents concepts et des types de risques liés aux chaînes logistiques ainsi qu'une présentation d'une approche pour quantifier le risque. 3 - Développement d'un modèle d'optimisation de la fiabilité afin de réduire la vulnérabilité aux perturbations des chaînes logistiques sous l'incertitude de la sollicitation et de l'offre

A Bi-Objective Programming Model for Reliable Supply Chain Network Design Under Facility Disruption

Supply chain networks generally are composed of four main entity types: supplier, production centers, distribution centers and demand zones that consist of facilities whose activities involve the transformation of raw material into finished products that are later delivered from the suppliers to the end customers. Supply chain network design as the most important strategic decision in supply chain management plays an important role in the overall environmental and economic performance of the supply chain. The nature and complexity of today’s supply chains network make them vulnerable to various risks. One of the most important risks is disruption risk. Disruptions are costly and can be caused by internal or external sources to the supply chain, thus it is crucial that managers take appropriate measures of responses to reduce its negative effects. A recovery time of disrupted facilities and return it to the normal condition can be an important factor for members of the supply chain. In this paper, a bi-objective model is developed for reliable supply chain network design under facility disruption. To solve this model, we have applied two approaches, i.e., ε constraint method as an exact method and non- dominated sorting genetic algorithm (NSGAII) as a meta-heuristic method

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

Feeding Ourselves Thirsty: How the Food Sector is Managing Global Water Risks

The global food sector faces extraordinary risks from the twin challenges of water scarcity and water pollution. Growing competition for water, combined with weak regulations, failing infrastructure, pollution and climate change impacts threaten the sector's water security and contribute to a water availability emergency that was recently ranked the world's "top global risk" by the World Economic Forum.This report examines how water risks affect the profitability and competitive positioning of 37 major food sector companies in four industries: packaged food, beverage, meat and agricultural products. It evaluates and ranks these companies -- the majority of which are U.S. domiciled and publicly-traded -- on how well they are positioned to anticipate and mitigate these risks, as well as contribute to improved water resource management.The report provides recommendations for how analysts and investors can effectively evaluate food sector companies on their water risk exposure and management practices. It also provides recommendations for how food companies can improve water efficiency and water quality across their operations and supply chains to reduce risks and protect water resources

Designing a robust supply chain network against disruptions

Supply chains are vulnerable to disruptions at any stage of the distribution system. These disruptions can be caused by natural disasters, production problems, or labor defects. The consequences of these disruptions may result in significant economic losses or even human deaths. Therefore, it is important to consider any disruption as an important factor in strategic supply chain design. Consequently, the primary outputs of this dissertation include insights for designing robust supply chains that are neither significantly nor adversely impacted by disruptions. The impact of correlated supplier failures is examined and how this problem can be modeled as a variant of a facility location problem is described. Two main problems are defined, the first being the design of a robust supply chain, and the second being the optimization of operational inspection schedules to maintain the quality of an already established supply chain. In this regard, both strategic and operational decisions are considered in the model and (1) a two-stage stochastic programming model; (2) a multi-objective stochastic programming model; and (3) a dynamic programming model are developed to explore the tradeoffs between cost and risk. Three methods are developed to identify optimal and robust solutions: an integer L-shaped method; a hybrid genetic algorithm using Data Envelopment Analysis; and an approximate dynamic programming method. Several sensitivity analyses are performed on the model to see how the model output would be affected by uncertainty. The findings from this dissertation will be able to help both practitioners designing supply chains, as well as policy makers who need to understand the impact of different disruption mitigation strategies on cost and risk in the supply chain

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

Static and dynamic fault tree analysis with application to hybrid vehicle systems and supply chains

One of the most challenging parts of reliability analysis is building a reliability model of the system. Reliability block diagram, Markov models, and fault tree analysis are some of the most common techniques for constructing a reliability model. Fault tree analysis provides a way to combine components, which together can cause system failure. This research uses both static and dynamic fault trees to quantify the reliability of a hybrid vehicle system and to analyze supply chain risk. The hybrid vehicle combines a mechanical power source, such as the internal combustion engine (gasoline engine or diesel engine), and an electric power source (electric motor) to take advantage of two power sources and compensate from each source. The hybrid system’s complexity and non-mature technology carry potential risks for the vehicle. This research uses a static fault tree to analyze the reliability of the 2004 Toyota Prius under different operational modes. We apply Bayesian analysis that combines survey data to estimate the reliability of the hybrid vehicle’s battery. Supply chain risk analysis is increasingly becoming an important field and supply chain risk models help identify significant risks that can occur and the consequences if those risks occur. We use dynamic fault trees, which are relatively new in reliability analysis, to understand the timing of potential failures in different types of supply chains. We estimate failure rates for each supply chain under different production scenarios and simulate delivery time for the supply chain

Effect of Continuity Rate on Multistage Logistic Network Optimization under Disruption Risk

Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disruption is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and operational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan