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Description
This dissertation is an exploratory study that examined the differences in perceptions about supply chain management strategy, topics, tools, and techniques between procurement professionals in public and private sector organizations. This was accomplished through a survey of procurement professionals in a Fortune 500 company and a municipality in Arizona. The data were analyzed to understand how perceptions of supply chain management differed within this sample and whether the differences in perceptions were associated with formal education levels. Key findings indicate that for this or similar samples, public procurement respondents viewed their organizations' approach to supply chain management as a narrow function within purchasing while private sector respondents viewed their organization's approach to supply chain management as a strategic purchasing perspective that requires the coordination of cross functional areas. Second, public procurement respondents reported consistent and statistically significant lower levels of formal education than private sector respondents. Third, the supply chain management topics, tools, and techniques seem to be more important to private sector respondents than the public sector respondents. Finally, Respondents in both sectors recognize the importance of ethics and ethical behavior as an essential part of supply chain management.
ContributorsHeller, Jacob (Author) / Cayer, Joseph (Thesis advisor) / Lan, Gerald (Committee member) / Eden, Catherine (Committee member) / Arizona State University (Publisher)
Created2013
Description
A semiconductor supply chain modeling and simulation platform using Linear Program (LP) optimization and parallel Discrete Event System Specification (DEVS) process models has been developed in a joint effort by ASU and Intel Corporation. A Knowledge Interchange Broker (KIBDEVS/LP) was developed to broker information synchronously between the DEVS and LP models. Recently a single-echelon heuristic Inventory Strategy Module (ISM) was added to correct for forecast bias in customer demand data using different smoothing techniques. The optimization model could then use information provided by the forecast model to make better decisions for the process model. The composition of ISM with LP and DEVS models resulted in the first realization of what is now called the Optimization Simulation Forecast (OSF) platform. It could handle a single echelon supply chain system consisting of single hubs and single products In this thesis, this single-echelon simulation platform is extended to handle multiple echelons with multiple inventory elements handling multiple products. The main aspect for the multi-echelon OSF platform was to extend the KIBDEVS/LP such that ISM interactions with the LP and DEVS models could also be supported. To achieve this, a new, scalable XML schema for the KIB has been developed. The XML schema has also resulted in strengthening the KIB execution engine design. A sequential scheme controls the executions of the DEVS-Suite simulator, CPLEX optimizer, and ISM engine. To use the ISM for multiple echelons, it is extended to compute forecast customer demands and safety stocks over multiple hubs and products. Basic examples for semiconductor manufacturing spanning single and two echelon supply chain systems have been developed and analyzed. Experiments using perfect data were conducted to show the correctness of the OSF platform design and implementation. Simple, but realistic experiments have also been conducted. They highlight the kinds of supply chain dynamics that can be evaluated using discrete event process simulation, linear programming optimization, and heuristics forecasting models.
ContributorsSmith, James Melkon (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Davulcu, Hasan (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
Despite the wealth of folk music traditions in Portugal and the importance of the clarinet in the music of bandas filarmonicas, it is uncommon to find works featuring the clarinet using Portuguese folk music elements. In the interest of expanding this type of repertoire, three new works were commissioned from three different composers. The resulting works are Seres Imaginarios 3 by Luis Cardoso; Delirio Barroco by Tiago Derrica; and Memória by Pedro Faria Gomes. In an effort to submit these new works for inclusion into mainstream performance literature, the author has recorded these works on compact disc. This document includes interview transcripts with each composer, providing first-person discussion of each composition, as well as detailed biographical information on each composer. To provide context, the author has included a brief discussion on Portuguese folk music, and in particular, the role that the clarinet plays in Portuguese folk music culture.
ContributorsFerreira, Wesley (Contributor) / Spring, Robert S (Thesis advisor) / Bailey, Wayne (Committee member) / Gardner, Joshua (Committee member) / Hill, Gary (Committee member) / Schuring, Martin (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
Description
Consumer goods supply chains have gradually incorporated lean manufacturing principles to identify and reduce non-value-added activities. Companies implementing lean practices have experienced improvements in cost, quality, and demand responsiveness. However certain elements of these practices, especially those related to transportation and distribution may have detrimental impact on the environment. This study asks: What impact do current best practices in lean logistics and retailing have on environmental performance? The research hypothesis of this dissertation establishes that lean distribution of durable and consumable goods can result in an increased amount of carbon dioxide emissions, leading to climate change and natural resource depletion impacts, while lean retailing operations can reduce carbon emissions. Distribution and retailing phases of the life cycle are characterized in a two-echelon supply chain discrete-event simulation modeled after current operations from leading organizations based in the U.S. Southwest. By conducting an overview of critical sustainability issues and their relationship with consumer products, it is possible to address the environmental implications of lean logistics and retailing operations. Provided the waste reduction nature from lean manufacturing, four lean best practices are examined in detail in order to formulate specific research propositions. These propositions are integrated into an experimental design linking annual carbon dioxide equivalent emissions to: (1) shipment frequency between supply chain partners, (2) proximity between decoupling point of products and final customers, (3) inventory turns at the warehousing level, and (4) degree of supplier integration. All propositions are tested through the use of the simulation model. Results confirmed the four research propositions. Furthermore, they suggest synergy between product shipment frequency among supply chain partners and product management due to lean retailing practices. In addition, the study confirms prior research speculations about the potential carbon intensity from transportation operations subject to lean principles.
ContributorsUgarte Irizarri, Gustavo Marco Antonio (Author) / Golden, Jay S. (Thesis advisor) / Dooley, Kevin J. (Thesis advisor) / Boone, Christopher G. (Committee member) / Basile, George M. (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsBurton, Charlotte (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-08
Description
Nowadays ports play a critic role in the supply chains of contemporary companies and global commerce. Since the ports' operational effectiveness is critical on the development of competitive supply chains, their contribution to regional economies is essential. With the globalization of markets, the traffic of containers flowing through the different ports has increased significantly in the last decades. In order to attract additional container traffic and improve their comparative advantages over the competition, ports serving same hinterlands explore ways to improve their operations to become more attractive to shippers. This research explores the hypothesis that lowering the variability of the service time observed in the handling of containers, a port reduces the total logistics costs of their customers, increase its competiveness and that of their customers. This thesis proposes a methodology that allows the quantification of the variability existing in the services of a port derived from factors like inefficient internal operations, vessel congestion or external disruptions scenarios. It focuses on assessing the impact of this variability on the user's logistic costs. The methodology also allows a port to define competitive strategies that take into account its variability and that of competing ports. These competitive strategies are also translated into specific parameters that can be used to design and adjust internal operations. The methodology includes (1) a definition of a proper economic model to measure the logistic impact of port's variability, (2) a network analysis approach to the defined problem and (3) a systematic procedure to determine competitive service time parameters for a port. After the methodology is developed, a case study is presented where it is applied to the Port of Guaymas. This is done by finding service time parameters for this port that yield lower logistic costs than the observed in other competing ports.
ContributorsMeneses Preciado, Cesar (Author) / Villalobos, Jesus R (Thesis advisor) / Gel, Esma S (Committee member) / Maltz, Arnold B (Committee member) / Arizona State University (Publisher)
Created2011
Description
Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
ContributorsShinde, Amit (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsDruesedow, Elizabeth (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
Description
Today's competitive markets force companies to constantly engage in the complex task of managing their demand. In make-to-order manufacturing or service systems, the demand of a product is shaped by price and lead times, where high price and lead time quotes ensure profitability for supplier, but discourage the customers from placing orders. Low price and lead times, on the other hand, generally result in high demand, but do not necessarily ensure profitability. The price and lead time quotation problem considers the trade-off between offering high and low prices and lead times. The recent practices in make-to- order manufacturing companies reveal the importance of dynamic quotation strategies, under which the prices and lead time quotes flexibly change depending on the status of the system. In this dissertation, the objective is to model a make-to-order manufacturing system and explore various aspects of dynamic quotation strategies such as the behavior of optimal price and lead time decisions, the impact of customer preferences on optimal decisions, the benefits of employing dynamic quotation in comparison to simpler quotation strategies, and the benefits of coordinating price and lead time decisions. I first consider a manufacturer that receives demand from spot purchasers (who are quoted dynamic price and lead times), as well as from contract customers who have agree- ments with the manufacturer with fixed price and lead time terms. I analyze how customer preferences affect the optimal price and lead time decisions, the benefits of dynamic quo- tation, and the optimal mix of spot purchaser and contract customers. These analyses necessitate the computation of expected tardiness of customer orders at the moment cus- tomer enters the system. Hence, in the second part of the dissertation, I develop method- ologies to compute the expected tardiness in multi-class priority queues. For the trivial single class case, a closed formulation is obtained. For the more complex multi-class case, numerical inverse Laplace transformation algorithms are developed. In the last part of the dissertation, I model a decentralized system with two components. Marketing department determines the price quotes with the objective of maximizing revenues, and manufacturing department determines the lead time quotes to minimize lateness costs. I discuss the ben- efits of coordinating price and lead time decisions, and develop an incentivization scheme to reduce the negative impacts of lack of coordination.
ContributorsHafizoglu, Ahmet Baykal (Author) / Gel, Esma S (Thesis advisor) / Villalobos, Jesus R (Committee member) / Mirchandani, Pitu (Committee member) / Keskinocak, Pinar (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The complexity of supply chains (SC) has grown rapidly in recent years, resulting in an increased difficulty to evaluate and visualize performance. Consequently, analytical approaches to evaluate SC performance in near real time relative to targets and plans are important to detect and react to deviations in order to prevent major disruptions.
Manufacturing anomalies, inaccurate forecasts, and other problems can lead to SC disruptions. Traditional monitoring methods are not sufficient in this respect, because com- plex SCs feature changes in manufacturing tasks (dynamic complexity) and carry a large number of stock keeping units (detail complexity). Problems are easily confounded with normal system variations.
Motivated by these real challenges faced by modern SC, new surveillance solutions are proposed to detect system deviations that could lead to disruptions in a complex SC. To address supply-side deviations, the fitness of different statistics that can be extracted from the enterprise resource planning system is evaluated. A monitoring strategy is first proposed for SCs featuring high levels of dynamic complexity. This presents an opportunity for monitoring methods to be applied in a new, rich domain of SC management. Then a monitoring strategy, called Heat Map Contrasts (HMC), which converts monitoring into a series of classification problems, is used to monitor SCs with both high levels of dynamic and detail complexities. Data from a semiconductor SC simulator are used to compare the methods with other alternatives under various failure cases, and the results illustrate the viability of our methods.
To address demand-side deviations, a new method of quantifying forecast uncer- tainties using the progression of forecast updates is presented. It is illustrated that a rich amount of information is available in rolling horizon forecasts. Two proactive indicators of future forecast errors are extracted from the forecast stream. This quantitative method re- quires no knowledge of the forecasting model itself and has shown promising results when applied to two datasets consisting of real forecast updates.
Manufacturing anomalies, inaccurate forecasts, and other problems can lead to SC disruptions. Traditional monitoring methods are not sufficient in this respect, because com- plex SCs feature changes in manufacturing tasks (dynamic complexity) and carry a large number of stock keeping units (detail complexity). Problems are easily confounded with normal system variations.
Motivated by these real challenges faced by modern SC, new surveillance solutions are proposed to detect system deviations that could lead to disruptions in a complex SC. To address supply-side deviations, the fitness of different statistics that can be extracted from the enterprise resource planning system is evaluated. A monitoring strategy is first proposed for SCs featuring high levels of dynamic complexity. This presents an opportunity for monitoring methods to be applied in a new, rich domain of SC management. Then a monitoring strategy, called Heat Map Contrasts (HMC), which converts monitoring into a series of classification problems, is used to monitor SCs with both high levels of dynamic and detail complexities. Data from a semiconductor SC simulator are used to compare the methods with other alternatives under various failure cases, and the results illustrate the viability of our methods.
To address demand-side deviations, a new method of quantifying forecast uncer- tainties using the progression of forecast updates is presented. It is illustrated that a rich amount of information is available in rolling horizon forecasts. Two proactive indicators of future forecast errors are extracted from the forecast stream. This quantitative method re- quires no knowledge of the forecasting model itself and has shown promising results when applied to two datasets consisting of real forecast updates.
ContributorsLiu, Lei (Author) / Runger, George C. (Thesis advisor) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2015