Magister Commercii - MCom / This research project focuses on the application of process improvement techniques in a clothing manufacturer to address delay problems in workflow in the factory. The objective of the research is threefold; investigate delays at the beginning of production and make suggestions, show the usefulness of continuous improvement techniques in improving activities in a clothing manufacturer and demonstrate how action research can be used in doing research in production and operation management. Using tools such as flow charts, check sheets, pareto analysis, fishbone diagrams, interviews and the "ask why five times" tool, an investigation into delays led to a second investigation into sewing defects. This established that these sewing defects are caused mainly by time constraints, the malfunctioning of machines, the wrong handling of garments, and previous operations. After an investigation using the above tools it was established that these defects could be addressed by setting realistic targets, doing regular maintenance on machines, cautioning operators to be more careful during their operations, and encouraging regular checks on garments before the next operation. The action learning methodology led to the following lessons; selecting a correct measuring tool is important, that not all tools need to be used, and that it takes time to do a research project using this method.

The thesis covers studies conducted during 1976-79 under a Science Research Council contract to examine the uses of reliability information in decision-making in maintenance in the process industries. After a discussion of the ideal data system, four practical studies of process plants are described involving both Pareto and distribution analysis. In two of these studies the maintenance policy was changed and the effect on failure modes and frequency observed. Hyper-exponentially distributed failure intervals were found to be common and were explained after observation of maintenance work practices and development of theory as being due to poor workmanship and parts. The fallacy that constant failure rate necessarily implies the optimality of maintenance only at failure is discussed. Two models for the optimisation of inspection intervals are developed; both assume items give detectable warning of impending failure. The first is based upon constant risk of failure between successive inspections 'and Weibull base failure distribution~ Results show that an inspection/on-condition maintenance regime can be cost effective even when the failure rate is falling and may be better than periodiC renewals for an increasing failure situation. The second model is first-order Markov. Transition rate matrices are developed and solved to compare continuous monitoring with inspections/on-condition maintenance an a cost basis. The models incorporate planning delay in starting maintenance after impending failure is detected. The relationships between plant output and maintenance policy as affected by the presence of redundancy and/or storage between stages are examined, mainly through the literature but with some original theoretical proposals. It is concluded that reliability techniques have many applications in the improvement of plant maintenance policy. Techniques abound, but few firms are willing to take the step of faith to set up, even temporarily, the data-collection facilities required to apply them. There are over 350 references, many of which are reviewed in the text, divided into chapter-related sectionso Appendices include a review of Reliability Engineering Theory, based on the author's draft for BS 5760(2) a discussion of the 'bath-tub curves' applicability to maintained systems and the theory connecting hyper-exponentially distributed failures with poor maintenance practices.

In recent decades, climate change has become one of the most crucial challenges for humanity. Climate change has a direct correlation with global warming, caused mainly by the green house gas emissions (GHG). The Environmental Protection Agency in the U.S. (EPA) attributes carbon dioxide to account for approximately 82\% of the GHG emissions. Unfortunately, the energy sector is the main producer of carbon dioxide, with China and the U.S. as the highest emitters. Therefore, there is a strong (positive) correlation between energy production, global warming, and climate change. Stringent carbon emissions reduction targets have been established in order to reduce the impacts of GHG. Achieving these emissions reduction goals will require implementation of policies like as cap-and-trade and carbon taxes, together with transformation of the electricity grid into a smarter system with high green energy penetration. However, the consideration of policies solely in view of carbon emissions reduction may adversely impact other market outcomes such as electricity prices and consumption. In this dissertation, a two-layer mathematical-statistical framework is presented, that serves to develop carbon policies to reduce emissions level while minimizing the negative impacts on other market outcomes. The bottom layer of the two layer model comprises a bi-level optimization problem. The top layer comprises a statistical model and a Pareto analysis. Two related but different problems are studied under this methodology. The first problem looks into the design of cap-and-trade policies for deregulated electricity markets that satisfy the interest of different market constituents. Via the second problem, it is demonstrated how the framework can be used to obtain levels of carbon emissions reduction while minimizing the negative impact on electricity demand and maximizing green penetration from microgrids. In the aforementioned studies, forecasts for electricity prices and production cost are considered. This, this dissertation also presents anew forecast model that can be easily integrated in the two-layer framework. It is demonstrated in this dissertation that the proposed framework can be utilized by policy-makers, power companies, consumers, and market regulators in developing emissions policy decisions, bidding strategies, market regulations, and electricity dispatch strategies.

In this thesis I dealt with the Six Sigma method and uses in practice in Miele technica s.r.o. company. The first part deals with the theoretical description of Six Sigma, DMAIC methodology and used tools as SIPOC, Ishikawa diagram, Pareto diagram, or regression or correlation. The second part describes the application of Six Sigma methods, which I had the opportunity to perform as a team leader in the assembly process of washing machines and dryers.

Computer simulation is a popular method that is often used as a decision support tool in industry to estimate the performance of systems too complex for analytical solutions. It is a tool that assists decision-makers to improve organizational performance and achieve performance objectives in which simulated conditions can be randomly varied so that critical situations can be investigated without real-world risk. Due to the stochastic nature of many of the input process variables in simulation models, the output from the simulation model experiments are random. Thus, experimental runs of computer simulations yield only estimates of the values of performance objectives, where these estimates are themselves random variables. Most real-world decisions involve the simultaneous optimization of multiple, and often conflicting, objectives. Researchers and practitioners use various approaches to solve these multiobjective problems. Many of the approaches that integrate the simulation models with stochastic multiple objective optimization algorithms have been proposed, many of which use the Pareto-based approaches that generate a finite set of compromise, or tradeoff, solutions. Nevertheless, identification of the most preferred solution can be a daunting task to the decisionmaker and is an order of magnitude harder in the presence of stochastic objectives. However, to the best of this researcher’s knowledge, there has been no focused efforts and existing work that attempts to reduce the number of tradeoff solutions while considering the stochastic nature of a set of objective functions. In this research, two approaches that consider multiple stochastic objectives when reducing the set of the tradeoff solutions are designed and proposed. The first proposed approach is an a posteriori approach, which uses a given set of Pareto optima as input. The second iv approach is an interactive-based approach that articulates decision-maker preferences during the optimization process. A detailed description of both approaches is given, and computational studies are conducted to evaluate the efficacy of the two approaches. The computational results show the promise of the proposed approaches, in that each approach effectively reduces the set of compromise solutions to a reasonably manageable size for the decision-maker. This is a significant step beyond current applications of decision-making process in the presence of multiple stochastic objectives and should serve as an effective approach to support decisionmaking under uncertainty

The aim of the thesis is to analyse the product range manufactured by the company TON, which is a traditional Czech producer of bent wood furniture and especially chairs. The company TON has managed to maintain its position in the market for long 150 years, mainly thanks to the sensitive perception of changes in customer needs and adapting its product portfolio meeting them. In the theoretical part of the thesis are defined the basic concept of the marketing mix, product mix and described the methods and analysis as SLEPT analysis, Porter analysis, Portfolio analysis and Pareto analysis, which are then used in the practical part of the thesis. Based on the analysis there are formulated the conclusions which give recommendations on optimizing the product range and on effective use of certain elements of marketing mix for placing the product portfolio on target markets.

The Paper is focused on risk analysis of composite bumper Arway S production process. The bumper is produced by Composite Components company for Iveco bus producer. According to discussions of expert team, several types of quality discrepancies were discovered. By using FTA analysis we discovered causes of those discrepancies. FMEA method and Pareto Analysis helped to set most dangerous discrepancies which cumulative seriousness reached 54 %. In discussion, the expert team set corrective action. In parallel with FMEA and Pareto Analysis 8D method was used. It's results were in line with other methods and complete the whole image of the issue. Arway S is a key product and so some of those proposed corrective actions were realized immediately. As a result, the appearance of rejections with specific defect almost vanished.

This Master's thesis is focused on the purchase of XYZ Company, Ltd. The first section provides theoretical knowledge necessary to understand the issue, in the second part is analysis of business processes performed. Based on the observed conditions were evaluated insufficient process – supplier evaluation. That is why a new complete procedure for the evaluation of the suppliers was suggested

The thesis is focused on analysis and optimization of processes in a company realizing software projects. Its aim is to find deficiencies in the process of realization of an individual customer requirement and to design improvements. According to the analysis of the process of requirement realization, the deficiencies were analysed from which the most important were selected and used for designing optimization of this process.

The master’s thesis describes and analyzes the current reservation system of testing devices in Honeywell, spol. s r.o. – HTS CZ, o.z. divize ACS. The system is analyzed by using Six Sigma methods and proposes for improving the current system are prepared. These improvements should improve the functionality of the reservation system and possible savings. Appropriate metrics for measuring and evaluating the functionality of the system are designed.