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- All Subjects: Supply Chain Management
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 article summarizes exploratory research conducted on private and public hospital systems in Australia and Costa Rica analyzing the trends observed within supply chain procurement. Physician preferences and a general lack of available comparative effectiveness research—both of which are challenges unique to the health care industry—were found to be barriers to effective supply chain performance in both systems. Among other insights, the ability of policy to catalyze improved procurement performance in public hospital systems was also was observed. The role of centralization was also found to be fundamental to the success of the systems examined, allowing hospitals to focus on strategic rather than operational decisions and conduct value-streaming activities to generate increased cost savings.
ContributorsBudgett, Alexander Jay (Author) / Schneller, Eugene (Thesis director) / Gopalakrishnan, Mohan (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of English (Contributor)
Created2015-05
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
Despite significant growth in research about supply chain integration, many questions remain unanswered regarding the path to integration and the benefits that can be accrued. This dissertation examines three aspects of supply chain integration in the health sector, leveraging the healthcare context to extend the theoretical boundaries, as well as applying supply chain knowledge to an industry known to be immature in terms of its supply chain practices.
In the first chapter, a supply chain operating model that breaks away from the traditional healthcare supply chain structures is examined. Consolidated Service Centers (CSCs) embody a shared services strategy, consolidating supply chain functions across multiple hospitals (i.e. horizontal integration) and disintermediating several key roles in healthcare supply chains such as the group purchasing organizations and national distributors. Through case studies, key characteristics of CSCs that enable them to reduce the level of supply chain complexity are examined.
The second chapter investigates buyer-supplier relationships in healthcare (i.e. supplier integration), where a high level of distrust exists between hospitals and their suppliers. This context is leveraged to study both enablers and barriers to buyer-supplier trust. The results suggest that contracting counteracts the negative effects of dependence on trust. Furthermore, the study reveals that hospital buyers may, in some situations, perceive dedicated resource investments made by suppliers as trust barriers, associating such investments with supplier upselling and entrenchment tactics. This runs contrary to how dedicated investments are perceived in most other industries.
In the third chapter, the triadic relationship between the hospital, supplier, and physician is taken into consideration. Given their professional autonomy and power, physicians commonly undermine hospital efforts in supply base rationalization and standardization. This study examines whether physician-hospital integration (i.e. customer integration) can drive physicians towards supply selection practices that align with the hospital’s sourcing strategies and ultimately result in better supply chain performance. This study utilizes theory on agency triads and professionalism and tests hypotheses through a random effects regression model applied to data about hospital financial performance and physician-hospital arrangements.
In the first chapter, a supply chain operating model that breaks away from the traditional healthcare supply chain structures is examined. Consolidated Service Centers (CSCs) embody a shared services strategy, consolidating supply chain functions across multiple hospitals (i.e. horizontal integration) and disintermediating several key roles in healthcare supply chains such as the group purchasing organizations and national distributors. Through case studies, key characteristics of CSCs that enable them to reduce the level of supply chain complexity are examined.
The second chapter investigates buyer-supplier relationships in healthcare (i.e. supplier integration), where a high level of distrust exists between hospitals and their suppliers. This context is leveraged to study both enablers and barriers to buyer-supplier trust. The results suggest that contracting counteracts the negative effects of dependence on trust. Furthermore, the study reveals that hospital buyers may, in some situations, perceive dedicated resource investments made by suppliers as trust barriers, associating such investments with supplier upselling and entrenchment tactics. This runs contrary to how dedicated investments are perceived in most other industries.
In the third chapter, the triadic relationship between the hospital, supplier, and physician is taken into consideration. Given their professional autonomy and power, physicians commonly undermine hospital efforts in supply base rationalization and standardization. This study examines whether physician-hospital integration (i.e. customer integration) can drive physicians towards supply selection practices that align with the hospital’s sourcing strategies and ultimately result in better supply chain performance. This study utilizes theory on agency triads and professionalism and tests hypotheses through a random effects regression model applied to data about hospital financial performance and physician-hospital arrangements.
ContributorsAbdulsalam, Yousef J (Author) / Schneller, Eugene S (Thesis advisor) / Gopalakrishnan, Mohan (Committee member) / Maltz, Arnold (Committee member) / Dooley, Kevin (Committee member) / Arizona State University (Publisher)
Created2016