Improving Valve Industry Demand Forecasting

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dc.contributor.advisorSipos, Csanád
dc.contributor.authorBautz da Penha, Milena
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2024-06-19T12:06:23Z
dc.date.available2024-06-19T12:06:23Z
dc.date.created2024-05-28
dc.description.abstractThe thesis examines various quantitative forecasting techniques to enhance demand prediction in the valve industry. Analyzing historical sales data for five Flowserve products, the study employs methods such as Simple Average, Moving Averages, Exponential Smoothing, Holt-Winters Method, Linear Regression, and ARIMA. The performance of these techniques is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). Results indicate that sophisticated models like ARIMA and Linear Regression outperform simpler methods by better capturing data variability and trends. The findings suggest that advanced forecasting techniques are essential for improving inventory management and operational planning in the valve industry, offering valuable insights for industry practitioners and contributing to the broader field of demand forecasting research.
dc.description.courseEngineering Management
dc.description.degreeMSc/MA
dc.format.extent90
dc.identifier.urihttps://hdl.handle.net/2437/373965
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectForecasting
dc.subjectMethods Data Analysis
dc.subjectDemand Forecasting
dc.subjectTime series
dc.subjectValve Industry
dc.subjectInventory Management
dc.subjectOptimization
dc.subjectMarket Trends
dc.subject.dspaceEngineering Sciences
dc.titleImproving Valve Industry Demand Forecasting
dc.title.subtitleExploring Diverse Methodologies to Optimal Accuracy
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