IDEAS home Printed from https://ideas.repec.org/a/dbk/datame/v4y2025ip475id1056294dm2025475.html
   My bibliography  Save this article

Enhancing Operational Performance through Digitalization and Industry 4.0: A Comprehensive Model for Data Reliability and OEE Optimization

Author

Listed:
  • Abdelkarim Ait Brik
  • Ahmed En-nhaili
  • Anwar Meddaoui

Abstract

In today's industrial context, three key elements are guiding the course of small and medium-sized enterprises (SMEs) towards improved productivity, efficient operations, and sustainable growth. The introduction of Industry 4.0 signifies a groundbreaking shift, integrating state-of-the-art technologies into manufacturing processes and propelling industries towards heightened efficiency and competitiveness. This article deals with the crucial role of productivity measurement in SMEs and examines the impact of data reliability on operational performance assessment. It explores the strategic use of Industry 4.0 tools to enhance data reliability in processes like production, quality, and maintenance. The research focuses on designing a comprehensive model for data collection, reliability, and utilization, ultimately aiming to improve Overall Equipment Effectiveness (OEE) within SMEs. By showcasing the synergistic integration of Industry 4.0 advancements, the article provides practical insights for SME stakeholders to optimize operational performance. The proposed model contributes to the understanding and implementation of efficient methodologies for data management, fostering sustainable improvements using calculation of OEE within SMEs. The case study was conducted in a plastics manufacturing SME that produces components for various industries. These findings can be enhanced and improved through additional case studies to refine the proposed model.

Suggested Citation

Handle: RePEc:dbk:datame:v:4:y:2025:i::p:475:id:1056294dm2025475
DOI: 10.56294/dm2025475
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dbk:datame:v:4:y:2025:i::p:475:id:1056294dm2025475. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Javier Gonzalez-Argote (email available below). General contact details of provider: https://dm.ageditor.ar/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.