Evaluation of the Contemporary Issues in Data Mining and Data Warehousing

Over the past years data warehousing and data mining tools have evolved from research into a unique and popular business application class for decision support and business intelligence. This paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Theoretical and empirical literature was reviewed and various gaps in literature were identified. Besides the analysis of data mining and the business areas that can successfully apply it, the paper suggested and concluded that firms and scholars need to carry out more empirical research in the area of integrity of data mining and data warehousing since this will help eliminate marketing errors in operations and practice

Big data analytics for intra-logistics process planning in the automotive sector

The manufacturing sector is facing an important stage with Industry 4.0. This paradigm shift impulses companies to embrace innovative technologies and to pursuit near-zero fault, near real-time reactivity, better traceability, and more predictability, while working to achieve cheaper product customization. The scenario presented addresses multiple intra-logistic processes of the automotive factory Volkswagen Autoeuropa, where different situations need to be addressed. The main obstacle is the absence of harmonized and integrated data flows between all stages of the intra-logistic process which leads to inefficiencies. The existence of data silos is heavily contributing to this situation, which makes the planning of intra-logistics processes a challenge. The objective of the work presented here, is to integrate big data and machine learning technologies over data generated by the several manufacturing systems present, and thus support the management and optimisation of warehouse, parts transportation, sequencing and point-of-fit areas. This will support the creation of a digital twin of the intra-logistics processes. Still, the end goal is to employ deep learning techniques to achieve predictive capabilities, all together with simulation, in order to optimize processes planning and equipment efficiency. The work presented on this thesis, is aligned with the European project BOOST 4.0, with the objective to drive big data technologies in manufacturing domain, focusing on the automotive use-case

A Bayesian approach to demand forecasting

Demand forecasting is a fundamental aspect of inventory management. Forecasts are crucial in determining inventory stock levels, and accurately estimating future demand for spare part's has been an ongoing challenge, especially in the aerospace industry. If spare parts are not readily available, aircraft availability can be compromised leading to excessive downtime costs. As a result, inventory investment for spare parts can be significant to ensure down time is minimized. Additionally, most aircraft spare parts are considered "slow-moving" and experience intermittent demand making the use of traditional forecasting methods difficult in this industry. In this research, a forecasting method is developed using Bayes' rule to improve the demand forecasting of spare parts. The proposed Bayesian method is especially targeted to support new aircraft programs and is not intended to change how inventory is currently optimized. A case study based on a real aircraft program's data is performed in order to validate the use of the proposed Bayesian method. In the case study, three forecasting methods are compared: judgmental forecasting, a traditional statistical forecasting approach, and the proposed Bayesian method. The methods' impact on forecast accuracy, inventory costs, and fill rate performance (evaluated using simulation) are analyzed. The results conclude that the proposed Bayesian approach outperforms the other methods in terms of fill rate performance. Hence, the Bayesian method improves demand prediction and thus, more accurately estimates inventory needs allowing managers to make better inventory investment decisions

Hybrid system prediction for the stock market: The case of transitional markets

The subject of this paper is the creation and testing of an enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes, including the comparison with the traditional neural network backpropagation model. The objective of the research is to gather information concerning the possibilities of using the enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes focusing on transitional markets. The methodology used involves the integration of fuzzified weights into the neural network. The research results will be beneficial both for the broader investment community and the academia, in terms of the application of the enhanced model in the investment decision-making, as well as in improving the knowledge in this subject matter