<p>This study examines several emerging topics in supply chain management including the dynamic product substitution, the joint optimization of price and order quantity with demand ambiguity, and the implementation of the environmental and social responsibility (ESR) programs.</p>

Increasing competition from traditional and emerging channels has placed new emphasis on rapid innovation and continuous differentiation in every aspect of supply chain, from earliest production stage to the final distribution steps. To bridge the gap between brilliant ideas and successful business initiatives, leading companies implement engineering simulation particularly in logistics and supply chain management (LSCM). Discrete event simulation (DES) and system dynamics (SD) are two modeling approaches widely used in this field. However there are not much done researches about the applications of these simulation approaches in supply chain context of Swedish Manufacturing Industries (SMI). This study explores the application of DES and SD in LSCM of SMI by looking at the nature and level of issues modeled. Journal papers and master theses that use these modeling approaches to study supply chains, published between 1990 and 2012 are reviewed. A total of 39 articles are analyzed to identify the frequency with which the two simulation approaches are used as modeling tools in LSCM of SMI. Our findings suggest that DES has been used more frequently to model supply chains in SMI. The results also show that not all the LSCM issues have been addressed evenly and generally tactical/operational issues have been modeled more frequently. The results of this study inform the existing literature about the use of DES and SD in LSCM of SMI.

E-commerce and retail companies are seeking ways to cut delivery time and cost by exploring opportunities to use drones for making last-mile deliveries. In recent years, drone routing and scheduling have become a highly active area of research. This research addresses the concept of a truck-drone combined delivery by allowing autonomous drones to fly from delivery trucks, make deliveries, and fly to delivery trucks nearby. The first part of the research considers the synchronized truck drone routing model by allowing multiple drones to fly from any truck, serve customers and immediately return to any available truck or depot in the system. The goal is to find the optimal routes of both trucks and drones which minimize the arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by the formulated Mixed Integer Programming (MIP) for the small-size problems and our proposed heuristic called Adaptive Insertion Heuristics (ADI) which is based on the insertion technique for the medium/large-size problems. The second part of the research extends the first studied problem by allowing drones to serve multiple customers before merging with trucks as well as considering the capacity requirement for both vehicles. The problem is mathematically formulated and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). In the third study, the goal is to study the potential benefits of combining different types of fleet vehicles to deliver packages to the customers. Three types of vehicles are considered in this study including large drones, traditional trucks and hybrid trucks. The problem can be optimally solved by a mathematical formulation on a small scale. Two efficient metaheuristics based on Variable Neighborhood Search (VNS) and Large Neighborhood Search (LNS) are proposed to solve for approximate solutions of the large-size problems. A case study and numerical analysis demonstrate the better delivery time of the proposed model when compared with the delivery time of other delivery models with a single fleet type.

The author investigated the condition under which competition effect and contagion effect impact the suppliers of the firm encountering data breach. An event study was conducted to analyze the stock price of 104 suppliers of Target after the large-scale data breach in 2013. The result showed that suppliers with high dependence on Target experienced negative abnormal return on the day after Target’s announcement, while those with low dependence experienced positive abnormal return. After regressing the abnormal return on some explanatory variables, the result showed that firms with better operational performance and high information technology capability were less negatively affected. This study suggested that suppliers who relatively highly rely on one customer company are susceptible for the negative shock from that customer because of contagion effect. Furthermore, maintaining good performance and investing in information technology can help firms reduce losses from negative events happened in customer companies.

<div>Food Banks play an important role in assuaging hunger and improving food security in many nations worldwide. These organizations provide food and services to people in need. Food banks rely on food and cash donations that occur infrequently, to meet their objectives. In a highly uncertain environment such as this, balancing the supply and demand of food is challenging considering the limited availability of resources and the complex system. This research addresses these challenges and presents and analyses several statistical and mathematical models to facilitate the distribution of food to the food insecure in a sustainable and effective manner. The objective of this research is to develop data-driven models and analytical techniques and developing decision support frameworks to assist the food bank administrators in understanding the dynamics of supply and demand of food donations and improve the prediction accuracies of the food supply and demand behavior at various levels of planning to ensure equitable and efficient distribution of food to the food insecure. </div><div> </div><div>First, a systematic review was conducted to research the evolving literature in the field of food bank logistics. Perusal of the literature shows that research in the field of food bank logistics is in evolving phase and issues pertaining to fairness, sustainability, cost reduction, food quality and nutrition, data uncertainty, and food waste study have not been reviewed extensively. Second, for understanding the food supply behavior, a novel hybrid model combining ARIMA and neural network autoregressive (NNAR) model was proposed for univariate analysis and the work was extended to conduct a multi-variate numerical analysis implementing machine learning algorithms with Random Forests (RF) best capturing the complex structure of the data. Thirdly, to understand the dynamics of the food demand behavior, a Gaussian Mixture Model (GMM) clustering method is implemented to identify the possible causes of food insecurity in a given region by means of understanding the characteristics and structure of the food assistance network in a particular region, and the clustering result is further utilized to explore the patterns of uncertain food demand behavior and its significant importance in inventory management and redistribution of surplus food thereby developing a two-stage hybrid food demand estimation model with the proposed method significantly improving the prediction accuracies. </div><div> </div><div>Finally, the results of the analytical methods implemented and developed to study the supply and demand of the food donations is extracted and used to develop a conceptual framework for designing a decision support system to apply visual analytics to a food bank’s decision-making process. </div>

<p>Principal agent theory is widely used to model supply chain relationship, in which a supplier is the agent and a manufacturer is the principal. Both the manufacturer and supplier can influence product quality and consequentially share costs of product failures. Rich theoretical results under the principal agent model framework have been accumulated in the last two decades, but empirical evidence on whether the Stackelberg’s leadership game truly imitates practical supply chain relationship remains unfound. We study the domestic automobile industry in the last decade and provides to our best knowledge the first empirical evidence to assess the validity and practicality of principal agent theory and draw the implications of principal agent theory on supply chain relationship costs. Our empirical results suggest that Japanese OEMs behave more like principal agent theory suggests than the US OEMs in general and thus gain significant benefits in terms of marginal effort costs in motivating suppliers’ quality improvement behaviors and reducing overall manufacturer’s quality costs. Specifically, Toyota behaves closest to the optimal solution in the principal agent theory and therefore has the lowest manufacturer effort costs in improving product quality and achieves the overall lowest manufacturer’s quality costs in supply chain. Honda and Nissan are ranked 2^nd and 3^rd in terms of principal agent behaviors, but their marginal quality improvement effort costs are 33% and 61% higher than Toyota, and their total manufacturer’s quality costs are both around 17% higher compared to industrial leader Toyota by our estimate. US OEMs GM, Ford and Chrysler are believed to behave inconsistent to principal agent theory suggest, and consequently suffer a much higher marginal effort cost in motivating supplier’s quality improvement than Toyota as well as the overall manufacturer’s quality costs. GM and Ford are estimated doubled marginal effort costs than Toyota, and Chrysler is even higher at 1.6 times. GM’s overall manufacturer’s quality cost is 24% higher than Toyota, Ford is around 31% higher and Chrysler is around 48% higher. Our analysis gives a new perspective from principal agent theory to explain why Japanese OEMs especially Toyota has a better supply chain quality costs than US OEMs as literature and consensus suggested. In addition, we contribute in literature by linking the principal agent theory with automotive industrial data and first ever empirically validate the legitimacy of principal agent theory in modeling manufacturer-supplier relationship and quantitatively derive practical conclusions on marginal effort costs and manufacturer’s total supply chain quality cost implications. To guarantee the robustness of the empirical results, various sensitivity analyses are conducted and our main conclusions remain unchanged. </p>

<div>As the manufacturing and healthcare systems becomes more complex, efficiently managing these systems requires cooperation and coordination between different parties. This dissertation examines the coordination issues in a supply chain problem and diagnostic decision making in the healthcare system. Below, we provide a brief description of the problem and results achieved. </div><div> </div><div>With supply chain becoming increasingly extended, the uncertainty in the upstream production process can greatly affect the material flow that aims toward meeting the uncertain demand at the downstream. In Chapter 2, we analyze a two-location system in which the upstream production facility experiences random capacities and the downstream store faces random demands. Instead of decomposing the profit function widely used to treat multi-echelon systems, our approach builds on the notions of stochastic functions, in particular, the stochastic linearity in midpoint and the directional concavity in midpoint, which establishes the concavity and submodularity of the profit functions. In general, it is optimal to follow a two-level state-dependent threshold policy such that an order is issued at a location if and only if the inventory position of that location is below the corresponding threshold. When the salvage values of the ending inventories are linear, the profit function becomes decomposable in the inventory positions at different locations and the optimal threshold policy reduces to the echelon base-stock policy. The effect of production and demand uncertainty on inventory levels depends critically on whether the production capacity is limited or ample in relation to the demand. Only when the capacity is about the demand, the upstream facility holds positive inventory; otherwise, all units produced are immediately shipped to the downstream. We further extend our analysis to situations with general stochastic production functions and with multiple locations.</div><div> </div><div> </div><div>In Chapter 3, we examine the two-stage supply chain problem (described in Chapter 2) under the decentralized control. We consider two scenarios. In the first scenario, the retail store does not have any supply information including the inventory level at the manufacturing facility. We show that the upstream and downstream can be dynamically coordinated with proper transfer payment defined on local inventories and their own value function in the dynamic recursion. In the second scenario, the demand distribution is unknown to the manufacturing facility as well as the retail store does not know the supply information. We characterize the optimal transfer contracts under which coordination can be achieved, and propose an iterative algorithm to compute the optimal transfer contracts in the decentralized setting. The total profit of the decentralized system under our algorithm is guaranteed to converge to the centralized optimal channel profit for any demand and supply distribution functions. </div><div> </div><div>In Chapter 4, we provide a case study for the framework developed in [1]. The authors study the evaluation and integration of new medical research considering the operational impacts. As a case study, we first describe their two-station queueing control model using the MDP framework. We then present the structural properties of the MDP model. Since multiple classes of patients are considered in the MDP model, it becomes challenging to solve when the the number of patient classes increases. We describe an efficient heuristic algorithm developed by [1] to overcome the curse of dimensionality. We also test the numerical performance of their heuristic algorithm, and find that the largest optimality gap is less than 1.50% among all the experiments. </div><div> </div>

Logistics and Supply Chain Management are known in a professional public awareness more and more as activities which give a competitive advantage to companies which handle it well. The main objective of my diploma was to create a coprehensive guideline for logistics projects. This guideline shows the way how to manager logistics and supply chain management projects. In the theoretical part, I tried to cover the main areas of logistics and supply chain management in conjunction with the project management.The aim of my analysis was to apply the logistics guideline in a specific project for Jednota, spotřebitelské družstvo Ceske Budejovice, from the perspective of consultancy firm which was responsible for the solution of this project.

This paper is directed at discussing some of the management issues, problems and solutions of logistics in the context of offshored productive activities The introduction includes a discussion of the logistics topics and an introduction of the economic logic of offshoring. The main part analyses the logistics topics with regard to the internationally fragmented production. The topics of logistics include: Information flow and integration, transportation, inventory management, warehousing and materials management, packaging management, customer service, risk management, logistics strategies and supply chain design. For each of the discussed topics a company or industry example is given to illustrate the applications. The analysis is based on a review of the existing academic literature in each of these fields.

<p>While the energy field has been primarily dominated by fossil fuels such as coal and oil, there is evidence that renewable energy sources are starting to gain a stronger foothold in the energy market to accommodate growth (Debbage, 2008; Intelligent, 2008; Sterzinger, 2006). This has been the result of greater social concern, as well as tax and other government incentives (Intelligent, 2008; Debbage, 2008). Due to these trends, a growing market opportunity exists for cities and states to increase their renewable energy component production (Intelligent, 2008; Regional, 2013; Debbage, 2008; IPCC, 2014). The primary purpose of this study was to survey existing manufacturers in the Bluegrass Region of Kentucky to obtain information and identify manufacturers who: were currently in the renewable energy market, interested in entering the renewable energy market, or have no interest in entering the renewable energy market. Respondents also addressed potential barriers to the growth of the renewable energy field including workforce development, government policy, and investment capital. A total of 25 companies responded to the survey. Correlation analysis was used and determined that no significant correlation existed between surveyed companies who identified themselves as suppliers of renewable energy components and those companies who were identified as possible suppliers of renewable energy components within the REPP (Renewable Energy Policy Project) standards. This study builds on previous methodology used by Debbage (2008) for North Carolina. </p>