published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Predicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab. / text

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This thesis concerns the question, "Why is the performance of Six Sigma within The Ford Motor Company below that experienced in other companies, and what can be done to improve it?" The aim of the thesis was to make recommendations that would improve the performance of Six Sigma within the Ford Motor Company. Results from the literature were categorised according to headings found in the European Foundation for Quality Model (EFQM): strategy, people, process and leadership. The key factors identified from the literature review as being significant for a successful Six Sigma deployment were that projects were aligned to the strategy of the organisation, individuals were clear on their role and had appropriate skills, processes were well defined and understood and leadership team was committed to Six Sigma. The research started with a review of the results from two employee surveys. The first was given to Black Belts and asked questions concerning Six Sigma. The second was given to all the employees in the organisation. The survey data failed to identify the cause of lower than expected results, and so the investigation followed with a series of twelve interviews. When these also failed to identify the factor or factors responsible for deployment performance, the project database was reviewed. The Define, Measure, Analyse, Improve and Control steps (DMAIC) were then analysed using Gardner’s Model of Process Maturity. The thesis concluded that the main influence driving Six Sigma performance was the low process maturity of the project selection and scoping processes and this gave rise to variable project performance. The thesis then presents material to improve project performance including a process map, a process Failure Mode and Effects Analysis (FMEA) of the project selection and scoping process, a control plan that ensures that the projects are on track and a macro using Excel and Minitab that works within the Ford Motor Company system to provide automatic evaluation of projects.

M.Tech. Business Administration. Business School. / In their attempt to continuously improve their operations, Sasol Mining has since 1998 embarked on several different improvement initiatives including Operation Excellence with the improvement Lean Six Sigma in 2008. The main reason for choosing Lean Six Sigma was that the latter is a general, standard, well-documented improvement methodology, which is not dependent on any specific consultancy group to ensure successful and sustainable implementation. For sustainable Lean Six Sigma programme implementation, Sasol Mining has to ensure that human resources skills development and motivation enabling systems such as training and reward systems are in place. This research focuses on the assessment of the extent to which Operation Excellence employees perceive that there are benefits associated with their participation in the Lean Six Sigma programme during 2010 at Sasol Mining. In particular, how these perceptions could be effectively used by management as a basis for creating the environment where people are content and motivated to perform their best. The research investigates employee perceptions of various levels of Lean Six Sigma training within the context of categories of rewards most frequently associated with Lean Six Sigma; extrinsic, intrinsic, organisational and social rewards.

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Production data are the most abundant data in the field. However, they can often be of poor quality because of undocumented operational problems, or changes in operating conditions, or even recording mistakes (Nobakht et al. 2009). If this poor quality or inconsistency is not recognized as such, it can be misinterpreted as a reservoir issue other than the data quality problem that it is. Thus quality control of production data is a crucial and necessary step that must precede any further interpretation using the production data. To restore production data, we propose to use the capacitance resistance model (CRM) to realize data reconciliation. CRM is a simple reservoir simulation model that characterizes the connectivity between injectors and producers using only production and injection rate data. Because the CRM model is based on the continuity equation, it can be used to analyze the production corresponding to the injection signal in the reservoir. The problematic production data are then put into the CRM model directly and the resulting CRM output parameters are used to evaluate what the correct production response would be under current injection scheme. We also make sensitivity analysis based on synthetic fields, which are heterogeneous ideal reservoir models with imposed geology and well features in Eclipse. The aim is to show how bad data could be misleading and the best way to restore the production data. Using the CRM model itself to control data quality is a novel method to obtain clean production data. We can then apply the new clean production data in reservoir simulators or any other processes where production data quality matters. This data quality control process can help better understand the reservoir, analyze its behavior in a more ensured way and make more reliable decisions. / text

abstract / toc / Computer Science and Information Systems / Doctoral / Doctor of Philosophy