ENBIS 2021 Spring Meeting

Session

DoE and ML for product and process innovation

2.2.1

18 May 2021, 11:15

Online

Conveners

DoE and ML for product and process innovation

• Riccardo Ceccato (University of Padova)
• Rosa Arboretti (University of Padova)

20. A permutation-based solution for Machine Learning model selection

Mr Riccardo Ceccato (University of Padova)

18/05/2021, 11:15

Data Science in Process Industries

In a regression task, the choice of the best Machine Learning model is a critical step, especially when the main purpose is to offer a reliable tool for predicting future data. A poor choice could result in really poor predictive performances.
Fast moving consumer goods companies often plan consumer tests to gather consumers’ evaluations on new products and then are interested in analysing...

36. Applications of Design of Experiments and Machine Learning in Product Innovation

Luca Pegoraro (University of Padova)

18/05/2021, 11:35

Data Science in Process Industries

This work consists in a collection of useful results on the topics of Design of Experiments and Machine Learning applied in the context of product innovation. In many industries the performance of the final product depends upon some objective indicators that can be measured and that define the quality of the product itself. Some examples are mechanical properties in metallurgy or adhesive...

42. Consumers’ satisfaction with a product analysed through the lens of fuzzy theory

Nicolò Biasetton (Università degli Studi di Padova)

18/05/2021, 11:55

Data Science in Process Industries

Consumer satisfaction, among other feelings, towards products or services are usually captured, both in industry and academia, by means of ordinal scales, such as Likert-type scales. This kind of scales generates information intrinsically affected by uncertainty, imprecision and vagueness for two reasons: 1) the items of a Likert scale are subjectively interpreted by respondents based on their...