Matching Items (8)
Filtering by
- All Subjects: management
- Creators: Fowler, John
- Resource Type: Text
Description
Economic and environmental concerns necessitate the preference for retrofits over new construction in manufacturing facilities for incorporating modern technology, expanding production, becoming more energy-efficient and improving operational efficiency. Despite the technical and functional challenges in retrofits, the expectation from the project team is to; reduce costs, ensure the time to market and maintain a high standard for quality and safety. Thus, the construction supply chain faces increasing pressure to improve performance by ensuring better labor productivity, among other factors, for efficiency gain. Building Information Modeling (BIM) & off-site prefabrication are determined as effective management & production methods to meet these goals. However, there are limited studies assessing their impact on labor productivity within the constraints of a retrofit environment. This study fills the gap by exploring the impact of BIM on labor productivity (metric) in retrofits (context).
BIM use for process tool installation at a semiconductor manufacturing facility serves as an ideal environment for practical observations. Direct site observations indicate a positive correlation between disruptions in the workflow attributed to an immature use of BIM, waste due to rework and high non-value added time at the labor work face. Root-cause analysis traces the origins of the said disruptions to decision-factors that are critical for the planning, management and implementation of BIM. Analysis shows that stakeholders involved in decision-making during BIM planning, management and implementation identify BIM-value based on their immediate utility for BIM-use instead of the utility for the customers of the process. This differing value-system manifests in the form of unreliable and inaccurate information at the labor work face.
Grounding the analysis in theory and observations, the author hypothesizes that stakeholders of a construction project value BIM and BIM-aspects (i.e. geometrical information, descriptive information and workflows) differently and the accuracy of geometrical information is critical for improving labor productivity when using prefabrication in retrofit construction. In conclusion, this research presents a BIM-value framework, associating stakeholders with their relative value for BIM, the decision-factors for the planning, management and implementation of BIM and the potential impact of those decisions on labor productivity.
BIM use for process tool installation at a semiconductor manufacturing facility serves as an ideal environment for practical observations. Direct site observations indicate a positive correlation between disruptions in the workflow attributed to an immature use of BIM, waste due to rework and high non-value added time at the labor work face. Root-cause analysis traces the origins of the said disruptions to decision-factors that are critical for the planning, management and implementation of BIM. Analysis shows that stakeholders involved in decision-making during BIM planning, management and implementation identify BIM-value based on their immediate utility for BIM-use instead of the utility for the customers of the process. This differing value-system manifests in the form of unreliable and inaccurate information at the labor work face.
Grounding the analysis in theory and observations, the author hypothesizes that stakeholders of a construction project value BIM and BIM-aspects (i.e. geometrical information, descriptive information and workflows) differently and the accuracy of geometrical information is critical for improving labor productivity when using prefabrication in retrofit construction. In conclusion, this research presents a BIM-value framework, associating stakeholders with their relative value for BIM, the decision-factors for the planning, management and implementation of BIM and the potential impact of those decisions on labor productivity.
ContributorsGhosh, Arundhati (Author) / Chasey, Allan D (Thesis advisor) / Laroche, Dominique-Claude (Committee member) / Fowler, John (Committee member) / Arizona State University (Publisher)
Created2015
Description
The main goal of this study was to understand the awareness of small business owners regarding occupational fraud, meaning fraud committed from within an organization. A survey/questionnaire was used to gather insight into the knowledge and perceptions of small business owners, while also obtaining information about the history of fraud and the internal controls within their business. Twenty-four owners of businesses with less than 100 employees participated in the study. The results suggest that small business owners overestimate their knowledge regarding internal controls and occupational fraud, while also underestimating the risk of fraud within their own business. In fact, 92% of participants were not at all familiar with the popular Internal Control \u2014 Integrated Framework published by the Committee of Sponsoring Organizations of the Treadway Commission. The results also show that small business owners tend to overestimate the protection provided by their currently implemented controls in regard to their risk of fraud. Overall, through continued knowledge of internal controls and occupational fraud, business owners can better protect their businesses from the risk of occupational fraud by increasing their awareness of fraud.
ContributorsDennis, Lauren Nicole (Author) / Orpurt, Steven (Thesis director) / Munshi, Perseus (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor)
Created2014-05
Description
In this dissertation research, I expand the definition of the supply network to include the buying firm’s competitors. Just as one buyer-supplier relationship impacts all other relationships within the network, the presence of competitor-supplier relationships must also impact the focal buying firm. Therefore, the concept of a “competitive network” made up of a focal firm, its competitors and all of their combined suppliers is introduced. Utilizing a unique longitudinal dataset, this research explores how the organic structural changes within the new, many-to-many supply network impact firm performance. The investigation begins by studying the change in number of suppliers used by global auto manufacturers between 2004 and 2013. Following the Great Recession of 2008-09, firms have been growing the number of suppliers at more than twice the rate they had been reducing suppliers just a few years prior. The second phase of research explores the structural changes to the network resulting from this explosive growth in the number of suppliers. The final investigation explores a different flow – financial flow -- and evaluates its association with firm performance. Overall, this dissertation research demonstrates the value of aggregating individual supply networks into a macro-network defined as the competitive network. From this view, no one firm is able to control the structure of the network and the change in structure directly impacts firm performance. A new metric is introduced which addresses the subtle changes in buyer-supplier relationships and relates significantly to firm performance. The analyses expand the body of knowledge through the use of longitudinal datasets and uncovers otherwise overlooked dynamics existing within supply networks over the past decade.
ContributorsHuff, Jerry (Author) / Fowler, John (Thesis advisor) / Rogers, Dale (Committee member) / Carter, Craig (Committee member) / Arizona State University (Publisher)
Created2016
Description
The following is a case study composed of three workflow investigations at the open source software development (OSSD) based Apache Software Foundation (Apache). I start with an examination of the workload inequality within the Apache, particularly with regard to requirements writing. I established that the stronger a participant's experience indicators are, the more likely they are to propose a requirement that is not a defect and the more likely the requirement is eventually implemented. Requirements at Apache are divided into work tickets (tickets). In our second investigation, I reported many insights into the distribution patterns of these tickets. The participants that create the tickets often had the best track records for determining who should participate in that ticket. Tickets that were at one point volunteered for (self-assigned) had a lower incident of neglect but in some cases were also associated with severe delay. When a participant claims a ticket but postpones the work involved, these tickets exist without a solution for five to ten times as long, depending on the circumstances. I make recommendations that may reduce the incidence of tickets that are claimed but not implemented in a timely manner. After giving an in-depth explanation of how I obtained this data set through web crawlers, I describe the pattern mining platform I developed to make my data mining efforts highly scalable and repeatable. Lastly, I used process mining techniques to show that workflow patterns vary greatly within teams at Apache. I investigated a variety of process choices and how they might be influencing the outcomes of OSSD projects. I report a moderately negative association between how often a team updates the specifics of a requirement and how often requirements are completed. I also verified that the prevalence of volunteerism indicators is positively associated with work completion but what was surprising is that this correlation is stronger if I exclude the very large projects. I suggest the largest projects at Apache may benefit from some level of traditional delegation in addition to the phenomenon of volunteerism that OSSD is normally associated with.
ContributorsPanos, Ryan (Author) / Collofello, James (Thesis advisor) / Fowler, John (Thesis advisor) / Pan, Rong (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2017
Description
Yield is a key process performance characteristic in the capital-intensive semiconductor fabrication process. In an industry where machines cost millions of dollars and cycle times are a number of months, predicting and optimizing yield are critical to process improvement, customer satisfaction, and financial success. Semiconductor yield modeling is essential to identifying processing issues, improving quality, and meeting customer demand in the industry. However, the complicated fabrication process, the massive amount of data collected, and the number of models available make yield modeling a complex and challenging task. This work presents modeling strategies to forecast yield using generalized linear models (GLMs) based on defect metrology data. The research is divided into three main parts. First, the data integration and aggregation necessary for model building are described, and GLMs are constructed for yield forecasting. This technique yields results at both the die and the wafer levels, outperforms existing models found in the literature based on prediction errors, and identifies significant factors that can drive process improvement. This method also allows the nested structure of the process to be considered in the model, improving predictive capabilities and violating fewer assumptions. To account for the random sampling typically used in fabrication, the work is extended by using generalized linear mixed models (GLMMs) and a larger dataset to show the differences between batch-specific and population-averaged models in this application and how they compare to GLMs. These results show some additional improvements in forecasting abilities under certain conditions and show the differences between the significant effects identified in the GLM and GLMM models. The effects of link functions and sample size are also examined at the die and wafer levels. The third part of this research describes a methodology for integrating classification and regression trees (CART) with GLMs. This technique uses the terminal nodes identified in the classification tree to add predictors to a GLM. This method enables the model to consider important interaction terms in a simpler way than with the GLM alone, and provides valuable insight into the fabrication process through the combination of the tree structure and the statistical analysis of the GLM.
ContributorsKrueger, Dana Cheree (Author) / Montgomery, Douglas C. (Thesis advisor) / Fowler, John (Committee member) / Pan, Rong (Committee member) / Pfund, Michele (Committee member) / Arizona State University (Publisher)
Created2011
Description
In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.
ContributorsWang, Shanshan (Author) / Wu, Teresa (Thesis advisor) / Fowler, John (Thesis advisor) / Pfund, Michele (Committee member) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Arizona State University (Publisher)
Created2010
Description
In the first chapter, I consider a capacity and price bounded profit maximization problem in which a firm determines prices of multiple substitutable products when the supply or capacity of the products is limited and the prices are bounded. This problem applies broadly to many pricing decision settings such as for hotel rooms, airline seats, fashion, or other seasonal retail products, as well as any product line with shared production capacity. In this paper, I characterize structural properties of the constrained profit maximization problems under the Multinomial Logit (MNL) model and the optimal pricing solutions, and present efficient solution approaches. In the second chapter, I consider a data-driven profit maximization problem in which a firm determines the prices of multiple substitutable products. This problem applies broadly to many pricing decision settings such as for hotel rooms, airline seats, fashion, or other seasonal retail products. A typical data-driven optimization problem takes a two-step approach of parameter estimation and optimization for decisions. However, this often returns a suboptimal solution as the estimation error due to the variability in data impacts the quality of the optimal solution. I present the relationship between estimation error and quality of the optimal solution and provide a possible way to reduce the impact of the error on the optimal pricing decision under the MNL model. In the last chapter, I consider a facility layout design problem of a semiconductor fabrication facility (FAB). In designing a facility layout, the traditional approach has been to minimize the flow-weighted distance of materials through the automated material handling system (AMHS). However, distance focused approach sometimes yields one major issue, traffic congestion, that there is a question if it is truly a good criterion to design a layout. In this study, I try to understand what makes such congestion by analyzing the system dynamics and propose another approach with a concept of ``balancing the flow" that focuses more on resolving the congestion. Finally, I compare the performance of the two methods through the simulation of semiconductor FAB layouts.
ContributorsYU, GWANGJAE (Author) / Li, Hongmin (Thesis advisor) / Webster, Scott (Thesis advisor) / Fowler, John (Committee member) / Arizona State University (Publisher)
Created2021
Description
This project aimed to find implementable solutions to the long flow times at the Starbucks locations on campus. Surveys of the consumers indicated a dissatisfaction rating of 29%, neutral rating of 29% and satisfaction rating of 42%. Showing room for improvement in satisfaction, respondents were asked if a decrease in flow time or if mobile ordering was implemented would affect their frequency, over 50% responded that it would increase their frequency. Implementation of a mobile ordering system into the ASU app or separating the register line into M&G only and then cash and card only, is recommended to decrease the flow time.
ContributorsLares, Bethany Linn (Author) / Munshi, Perseus (Thesis director) / Garverick, Michael (Committee member) / Samuelson, Melissa (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12