Garbage in, Garbage Out: A Case Study on Defective Product Prediction in Manufacturing

No Thumbnail Available

Date

2023

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Research Projects

Organizational Units

Journal Issue

Abstract

Despite their potential business value and invest-ments, data science projects often fail owing to a lack of preparedness, implementation challenges, and poor data quality. This study aimed to develop a machine learning model for predicting defective products in the dyeing process within the manufacturing domain. However, inadequate importance given to data by the involved factory, insufficient data quality, and the lack of the necessary technical infrastructure for data science projects have hindered attaining desired results. This study emphasizes to academic researchers and industry experts the significance of data quality and technical infrastructure, highlights how these deficiencies can impact the success of a data science project, and provides several recommendations. © 2023 IEEE.

Description

Keywords

data quality, data science, imbalanced data, machine learning, manufacturing

Turkish CoHE Thesis Center URL

Citation

0

WoS Q

N/A

Scopus Q

N/A

Source

UBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering -- 8th International Conference on Computer Science and Engineering, UBMK 2023 -- 13 September 2023 through 15 September 2023 -- Burdur -- 193873

Volume

Issue

Start Page

282

End Page

287