IoT Real Time Monitoring of a Pharma Production Environment for Product Quality

In the manufacturing sector, particularly in the pharmaceutical industry, adhering to stringent quality and compliance standards has been and continues to be crucial due to the direct implications on public health and safety. Traditional methods of monitoring production processes often lack the responsiveness and precision needed to detect and rectify deviations in real time, leading to inefficiencies and potential compromises in product quality. This project proposes implementing an IoT-based system, utilizing Raspberry Pi and Node-RED programming, to enhance the accuracy, efficiency, and reliability of production processes. By providing continuous, real-time monitoring and analysis, this system aims to ensure adherence to quality standards, mitigate economic risks associated with product recalls and brand reputation, and foster a transformative shift towards more adaptive and scalable manufacturing operations.

Authors

Institutions

Publication Details

Published
2024-09-09
DOI
https://doi.org/10.1109/atnt61688.2024.10719355
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

IoT Real Time Monitoring of a Pharma Production Environment for Product Quality

Rozeha A. Rashid, Joshua Kasirye, Lynnate Jane Nazziwa, Kenneth Edmond Ntende
Advanced Statistical Process Monitoring
article

IoT Real Time Monitoring of a Pharma Production Environment for Product Quality

Rozeha A. Rashid, Joshua Kasirye, Lynnate Jane Nazziwa, Kenneth Edmond Ntende
article en

Abstract

In the manufacturing sector, particularly in the pharmaceutical industry, adhering to stringent quality and compliance standards has been and continues to be crucial due to the direct implications on public health and safety. Traditional methods of monitoring production processes often lack the responsiveness and precision needed to detect and rectify deviations in real time, leading to inefficiencies and potential compromises in product quality. This project proposes implementing an IoT-based system, utilizing Raspberry Pi and Node-RED programming, to enhance the accuracy, efficiency, and reliability of production processes. By providing continuous, real-time monitoring and analysis, this system aims to ensure adherence to quality standards, mitigate economic risks associated with product recalls and brand reputation, and foster a transformative shift towards more adaptive and scalable manufacturing operations.

University of Technology Malaysia (MY)
Universiti Teknologi Malaysia
Openalex Percentile: Top 81%
Advanced Statistical Process Monitoring
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.