Search

The increasing competition and rapid technological advancements in today's business world have raised customer expectations. People now expect quick delivery, low prices, and high-quality products. As a result, companies must adapt to this competitive environment to survive. Rework, which is a significant cost in production, increases expenses, reduces production efficiency, and can lead to customer attrition. Research shows various efforts across different sectors to reduce rework, although there is still a gap in the textile sector's fabric dyeing units. Common problems in these units include non-retentive colors, customer dissatisfaction with shades, and repeated dyeing due to environmental factors or dye vat issues. This study uses logistic regression and artificial neural networks models from machine learning to predict which fabrics will need rework, using data from a textile company in Bursa. The analysis indicates that artificial neural networks models perform better. [ABSTRACT FROM AUTHOR]

Abstract: Technological innovation process starts with technical discovery of new things or new ways of doing things, i.e., invention. With the commercialization of invention, the term, innovation, takes place instead of invention. The process ends with (duplicative and/or creative) imitation by competitors. It is expected that maximum profits can be achieved in the time interval from commercialization of invention to its imitation because of monopoly power. Therefore, assessing the emergence of invention in accordance with ability for commercialization and resistance for imitation can generate winning innovation intelligence. Besides, for achieving sustainability, disruptive technologies should also be taken into account through evaluation of trendiness of candidate technologies. This study presents a novel assessment process that aims to evaluate and prioritize candidate technologies according to their innovation potentials by considering commercialization, imitation and trendiness factors all together. According to authors’ best knowledge, the technology assessment process presented in this study is the first attempt in the literature that is dedicated to winning innovation intelligence and takes above mentioned factors together into account. Main input resources of the process are patents, scientific publications and market research reports. While trendiness of technologies is evaluated with the help of a fuzzy inference system that combines patent data and publication data, commercial and imitation potentials are evaluated with the help of some marketing indicators and determinants in the proposed assessment process. [Copyright &y& Elsevier]