Matching Items (8)
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- All Subjects: Supply Chain Management
- Creators: Oke, Adegoke
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
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
This thesis focuses on the supply chain of the wine industry from a smaller scale operational perspective. A standard process from converting grapes to wine has been identified and confirmed. The sequential order of harvest, destemmer/crusher, fermentation, press, barrels, bottling, and distribution constitute the main tasks in the red wine conversion process. Variations in production between red and white wines are observed; but, the overall process is roughly the same with white wines switching the fermentation and press steps and eliminating the barrels task. In addition, it is established that supply chain considerations do effect overall quality such as taste, aroma, and smell. The ability to utilize a combination of diverse techniques, such as wooden barrels or stainless steel tanks for aging, is what contributes to the differentiation of each wine and makes it unique. While the production methodology and use of specific materials/inputs will alter the quality of wine, it must be recognized that the majority of wine quality is influenced directly by the grape itself. The use of technology and machinery in the wine making process is investigated and determined to be pivotal to the creation of wine and the survival of any size winery. Technology has facilitated the wine making process and the current creation path could not occur without it. Wine operations will adapt and incorporate new procedures to take advantage of growth in technology as it occurs, especially in automation. The information used to assess the wine supply chain was obtained from an extensive literature review, interviews with industry professionals, and onsite tours of production facilities. Given all the results and data, it is evident that the production of wine can greatly benefit from the use of supply chain practices and concepts. The ability to reduce variation in the process and determine which aspects contribute most to wine quality are vital for small scale winery operations to remain competitive and become successful.
ContributorsClarke, Tanya N (Author) / Oke, Adegoke (Thesis director) / Gopalakrishnan, Mohan (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor)
Created2014-05
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
Description
The surge of United States high-tech firms offshoring operations to China was driven by economic incentives of the early 1990s, low costs of labor, and ample access to an abundance of resources required in high-tech manufacturing. The dawn of the 21st century served as the advent of technological advancement and innovation in congruence with China’s rapid ascension as a prime high-tech manufacturing hub. However, increased allegations of foreign intellectual property (IP) infringement in outsourced research and development (R&D) and manufacturing on behalf of China’s state-owned enterprises (SOE) have evoked concern amongst international speculators, who allege China of weakened intellectual property enforcement and collusive tactics with state-owned enterprises in the cultivation of an anti-competitive marketplace. This thesis applies a trilateral approach to determine the optimal legal, supply chain management, and business strategies to safeguard the intellectual property of high-tech firms with outsourced operations in China.<br/><br/>Firstly, this thesis explores China’s rapid acceleration of manufacturing capabilities in tandem with nationalist initiatives, historical background, and subsequent influence cultural notions; aspirations in attaining global dominance as a high-tech innovator via nationalist programs and incentives. Succeeding is a comparative analysis of intellectual property between the United States and China, associations between intellectual property protection and economic development, and global intellectual property agreeance as set forth by the World Trade Organization (WTO). Following is a legal analysis of China, which assesses legislation, judicial structure, and litigation. Lastly, is an assessment of supply chain management in China, which assesses high-tech outsourcing practices, the vulnerability of intellectual property in research and development, instances of patent infringement, unfair licensing practices, and trade secret misappropriation.
ContributorsPlunkett, Nina (Author) / Collins, Gregory (Thesis director) / Oke, Adegoke (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this thesis paper is to discuss the life cycle curves of today’s innovation-driven digital services and how these life cycles can be extended. To decide on this subject, four supply chain management seminars were attended and summarized, and personal notes were added. After analysis of the seminars, the paper will dive deeper into the topic discussed by Professor Adegoke Oke of Arizona State University in his lecture on innovation-driven life cycle curves and their exemplification of the shark fin curve. This paper will analyze the nature of product life cycle curves, discuss three PC games and their life cycle curves, and evaluate the best methods for creating life cycle extension.
ContributorsDriscoll, Trevor (Author) / Oke, Adegoke (Thesis director) / Printezis, Antonios (Committee member) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The purpose of this honors thesis project is to gather information and expand upon current research within the field of Supply Chain Management. The first step taken to effectively learn about research within the field was the attendance of supply chain seminars geared toward PhD level students. After attending these research seminars, summaries and personal reflections have been created to synthesize the material and highlight the topics discussed. After an analysis of each of the research seminars, an in-depth literature review for the specific topic of “Production Process Moves” is given. This literature review will expand upon the areas of research from this seminar, as well as explore various topics with a focus on knowledge transfer that could have been incorporated into these studies.
ContributorsDonovan, Jasmine Marie (Author) / Oke, Adegoke (Thesis director) / Keane, Katy (Committee member) / Department of Supply Chain Management (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
My journey through Arizona State University did not start with Supply Chain Management. I began as a Finance Major, but something did not resonate with me. I eventually switched my major to Supply Chain Management in my Junior year, after taking Professor Eddie Davila’s SCM 300 course. The hands-on nature of supply chain management, as well as the broad range of industries it covered, got me hooked. For my alternative thesis, I had to attend four seminars for WPC supply faculty, then write summaries on them. I would then choose one of the four to further extrapolate on and do independent research. However, after attending only two, the COVID-19 outbreak occurred, leading to the cancellation of the remaining seminars. As a result, I was faced with two choices; abandon the thesis project entirely, or work with what I had. With the approval of my director, I decided on the following outline. The first part of this paper will summarize and reflect on the two faculty lectures I was able to attend. The second part of the paper will look at the way COVID-19 has impacted the supply chain of a local business, SilverTree Dentistry. I will use information gathered from the dental office, supplier sites, and online sources as well as from the two faculty lectures to develop a cohesive plan of potential action for SilverTree to better equip themselves and their supply chain to handle the COVID-19 pandemic, as well as how to respond as a small business in future times of crisis. This paper’s focus is on how small business supply chains are disrupted in their areas of supplier selection and supplier power, specifically how those areas affect the business’s spend.
ContributorsMladenov, Stefan Nikolaev (Author) / Oke, Adegoke (Thesis director) / Printezis, Antonios (Committee member) / Department of Supply Chain Management (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05