Reverse logistics has become a part of many governmental agendas in developed countries. It offers enormous opportunities in reducing the negative impact on the environment from industrialization and the enormous quantities of waste produced by industry and households, as well as saving material resources. The recovery of used products and materials is receiving growing attention as a result of limited landfill and incineration capacity. However, the evolution of reverse logistics initiatives in developing countries is relatively slow, indicating that there are still huge obstacles and concerns that need to be explored. The main aim of this study is to investigate the reverse logistics practices in the Kingdom of Saudi Arabia (KSA). The key forces of reverse logistics in more developed countries are compared with those in the KSA context. In particular, the current legislative framework and reverse logistics operations are evaluated against best practices in order to identify opportunities for KSA industries to improve their reverse logistics operations. For this purpose, qualitative research using a case study approach has been designed to collect data from different government agencies and thirteen organizations in KSA. These organizations were drawn from the oil, petrochemical and metal processing industries, all of which provide potential for the closed loop supply chain applications which are widely practiced outside KSA. Secondary data collection, direct observation and semi-structured interviews were conducted in order to facilitate comparison with the literature. A conceptual framework was developed from the literature. A template analysis approach was employed to analyze the gathered data. The framework was then applied to the research context to establish an overall view of the reverse logistics process as well as the key forces that influence the implementation of reverse logistics in KSA. The results revealed that the implementation of reverse logistics in KSA is at an early stage, with the exception of the oil processing industry. Applications of reverse logistics appear to be driven mainly by economic factors and are highly dependent on the capability provided by third-party logistics service providers. Supply chains tend to operate as open loops with recycled/reusable products being supplied to secondary markets and/or locations outside KSA. Furthermore, the adoption of best practice is hindered by the country's logistics infrastructure and by local legislation that does not appear to encourage waste recovery or promote the awareness or control of the reverse logistics process. Both government and industry need to collaborate to implement best practice in reverse logistics in KSA. Economic and environmental benefits can be achieved, especially through improved infrastructure provision, legislation and reverse logistics design. This study makes several contributions to knowledge. First, the framework and strategies will make an important contribution to the literature of reverse logistics by identifying the key forces, represented in a force field analysis diagram; and to general reverse logistics operations in developing countries in general and in Saudi Arabia in particular. Secondly, the findings identify further studies for reverse logistics in KSA and, possibly, for other emerging countries with a similar context. Thirdly, this study is distinguished from existing empirical work on reverse logistics in developed countries as it examines the framework in the context of KSA, and may be applicable to other developing countries. Fourthly, the study motivates changes in practice as it provides recommendations and guidance for practitioners and leaders on how to apply the best practices of reverse logistics in different sectors.

In today’s extremely competitive marketplace, firms are facing the need to meet or exceed increasing customer expectations while cutting costs to stay competitive in a global market. To develop competitive advantage in this business climate, companies must make informed decisions regarding their supply chain. In recent years, supply chain networks have received increasing attention among companies. The decision makers confront the network design problem in different situations. In order to make decisions, especially in strategic supply chain management, decision makers must have a holistic view of all the components. Supply chain network design, particular facility location problems, is one of the most complex strategic decision problems in supply chain management The aim of this dissertation is to make an inquiry about the facility location problems and related issues in supply chain and logistics management, and the use of modelling approaches to solve these problems. The methodology is to construct a review protocol by forming a review panel, and developing a detailed search strategy with clear inclusion and exclusion criteria. In addition, the measurement for evaluating the quality of studies is presented with a strategy for extracting data and synthesising the methodologies. The search results show the background of the facility location problems, the importance and the basic questions of these problems. The taxonomy of facility location problems with eighteen factors is presented. The basic static and deterministic problems in facility location including the covering, centre, median and fixed charge problems are discussed. Also, the extension of facility location problems comprises of location-allocation, multi-objective, hierarchical, hub, undesirable and competitive problems. In terms of uncertainty, dynamic, stochastic and robust facility location problems are presented. Finally, strengths and weaknesses of different modelling approaches are discussed; importantly, gaps from the review process are indentified. Recommendations of future research are described; and the facility location problem to be addressed by the proposed research is shown. In addition, contributions of the proposed facility location problem are illustrated.

<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.

The thesis is to provide comprehensive efficiency analysis of road transport intermediaries in terms of international trade between the Commonwealth of Independent States and the European Union. The analysis establishes particular patterns and provides solutions of how to increase financial effectiveness of the supply chain in case freight forwarding companies are involved in the process.

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.

The importance of a satisfactory logistics system is becoming increasingly apparent as a consequence of growing competition and rising customer requirements regarding, amongst others, lead times, flexibility and delivery dependability. The increasing demands reduce profit margins and increase logistics costs, accelerating the  importance of a well-functioning logistics system. A way to meet the increased customer demands is to tailor the logistics solution by segmenting customers according to their unique needs. Segmenting the customers has the potential to reduce a company's logistics costs, partly because delivery service can be bespoke to the customers' different requirements, instead of offering all customers the best possible performance. When making changes to a logistics system, it is therefore also important to take customer satisfaction into account, as reduced customer satisfaction otherwise risks leading to lost sales. A company that faces challenges with increased customer demands regarding the logistics solution in parallel with a quest for reduced logistics costs is ASSA ABLOY, a company that manufactures and sells security solutions and security systems worldwide. Based on the above situation, the research purpose of the study is to \textit{through customer segmentation develop strategy proposals to reduce the total cost of ASSA ABLOY's logistics solution, taking customer satisfaction into account}. To create an understanding of the building blocks of the research purpose: logistical change, total cost analysis, customer segments and customer satisfaction, literature was examined in the mentioned areas. To link the study to ASSA ABLOY's unique situation, multiple unstructured and semi-structured interviews were conducted with people at, among others, ASSA ABLOY with good insight into the logistics solution. Based on the interview outcomes, a list of six strategy proposals for customer segmentation was produced. Based on additional interviews as well as the study's frame of reference, the list of strategic proposals was shortened to three strategies based on implementability. The total cost of the strategy proposals and the customer satisfaction impact were analyzed using empirical data in the form of data from interviews as well as order and cost data for 2021. During the analysis, constant discussions were held with supervisors at ASSA ABLOY to ensure the analysis' connection to reality. The results of the study were summarized into three strategy proposals that through data analysis could be demonstrated to lead to potential cost savings. The customer segmentation option \textit{accepted lead time} showed the by far largest potential cost saving, followed by \textit{geographical position} which also showed great savings potential. For both alternatives, customer satisfaction was estimated to slightly be improved. The third analyzed strategy proposal, customer segmentation based on \textit{average order value} indicated the lowest cost-saving and with a slight deterioration in customer satisfaction, which is why it was considered to be the worse of the three analysed strategic alternatives.

<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>