Browsing by Author "Pinto, Maria Francisca de Lima Teixeira"
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- Defining an integrated framework for demand forecasting, due date assignment, scheduling, and performance evaluation in operations managementPublication . Teymourifar, Aydin; Pinto, Maria Francisca de Lima Teixeira; Moreira, José Maria Antunes Bento; Machado, Inês Filipa MedeirosThis study introduces an integrated framework for operations management, encompassing demand forecasting, capacity allocation based on estimated demand, due date assignment for incoming orders according to allocated capacity, and utilizing due dates as scheduling rules and performance evaluation criteria. We provide detailed insights into implementing this innovative approach, leveraging regression models and neural networks. Additionally, we provide a comprehensive review of alternative methods applicable to this framework. Our method offers practical applicability across diverse sectors of operations management, yielding tangible outcomes, which we elucidate in this study.
- Demand forecasting in a company : a case study from footwear industryPublication . Pinto, Maria Francisca de Lima Teixeira; Teymourifar, AydinDemand forecasting has been investigated for decades, in several areas, such as manufacturing, logistics, and finance, due to its importance in corporate planning and decision-making. Several methods have been tested in different industries, but there is still no consensus among authors, as to which method should be regularly applied since market characteristics differ from company to company. The purpose of this study is to identify the demand forecasting method with the highest accuracy for the characteristics of the data provided by the Portuguese footwear company 8000Kicks, and the reasons for this method have better results than the others tested. A quantitative study is carried out, in the form of problem-solving. The aim of this research is to help solve the company’s problem of lack of efficiency in the use of company resources, impacting its planning and decision-making. Time Series, Regression, and Artificial Intelligence models were selected and tested, to analyse their accuracy, according to the chosen performance measure, Mean Square Error (MSE). The Artificial Neural Network model revealed better accuracy, with the lowest MSE of the models tested, with a test value of 8,5865E-06, followed by Nonlinear Regression. It is concluded that, for this study, the nonlinear models appear to have better results when compared to the linear models, due to their characteristics of adaptability, better fit to the data, and ability to capture complex relationships and dynamic processes.