Utilising the Internet of Things concepts to improve the resource efficiency of food manufacturing
Jagtap, Sandeep LU
(2019)
- Abstract
- This thesis reports on the research undertaken to increase the sustainability of food
manufacturing by reducing the solid Food waste generation, as well as Energy and Water
(FEW) consumptions through applications based of Internet of Things (IoT) concepts. The
primary objective of this research is to develop an IoT-based framework, which identifies and
collects the key data regarding FEW within food manufacturing. The other objective is to
design and implement a decision support tool using appropriate and existing hardware and
software to aid the stakeholders with the choice of the most effective solution to reduce the
FEW within food manufacturing processes.
The research contributions are divided into four... (More) - This thesis reports on the research undertaken to increase the sustainability of food
manufacturing by reducing the solid Food waste generation, as well as Energy and Water
(FEW) consumptions through applications based of Internet of Things (IoT) concepts. The
primary objective of this research is to develop an IoT-based framework, which identifies and
collects the key data regarding FEW within food manufacturing. The other objective is to
design and implement a decision support tool using appropriate and existing hardware and
software to aid the stakeholders with the choice of the most effective solution to reduce the
FEW within food manufacturing processes.
The research contributions are divided into four main parts. The first part reviews the relevant
literature on the current state of FEW in food manufacturing, their environmental impacts, and
reasons behind their generation, opportunities to reduce FEW and applications of IoT within
food manufacturing. The second part introduces the IoT-based framework to address the
monitoring of FEW. This framework was developed since most of the food manufacturers are
not aware/or ignorant of their FEW and its environmental and financial value. The third part
describes the implementation of the framework through an architectural prototype developed
using a combination of software and hardware. The final part of the thesis demonstrates the
application of the IoT tool to monitor the FEW in real-time using case studies and thereby
support the management decisions aimed at reducing FEW.
The industrial application of the research concepts proposed in this thesis through four specific
case studies has highlighted the complexities as well as the opportunities available in using
digital technologies in improving the overall sustainability of the food sector. This is due to the
variety and specific nature of the food products as well as a large number of small to medium
scale enterprises that form a significant proportion of the supply chain in this sector.
In summary, this research has provided a practical and powerful tool based on IoT concepts
to address the FEW within food manufacturing. This research has concluded that the
consideration of the FEW reductions within the food industry requires an accurate, detailed
and real-time based understanding of FEW in order to allow stakeholders to take proactive
approaches in FEW reductions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/657b4628-c251-4c40-b3f1-6a2159632496
- author
- Jagtap, Sandeep
LU
- supervisor
- organization
- publishing date
- 2019
- type
- Thesis
- publication status
- published
- subject
- publisher
- Loughborough University
- DOI
- 10.26174/thesis.lboro.11180456.v1
- language
- English
- LU publication?
- yes
- id
- 657b4628-c251-4c40-b3f1-6a2159632496
- date added to LUP
- 2023-09-17 19:23:30
- date last changed
- 2023-09-19 08:54:22
@misc{657b4628-c251-4c40-b3f1-6a2159632496,
abstract = {{This thesis reports on the research undertaken to increase the sustainability of food<br/>manufacturing by reducing the solid Food waste generation, as well as Energy and Water<br/>(FEW) consumptions through applications based of Internet of Things (IoT) concepts. The<br/>primary objective of this research is to develop an IoT-based framework, which identifies and<br/>collects the key data regarding FEW within food manufacturing. The other objective is to<br/>design and implement a decision support tool using appropriate and existing hardware and<br/>software to aid the stakeholders with the choice of the most effective solution to reduce the<br/>FEW within food manufacturing processes.<br/>The research contributions are divided into four main parts. The first part reviews the relevant<br/>literature on the current state of FEW in food manufacturing, their environmental impacts, and<br/>reasons behind their generation, opportunities to reduce FEW and applications of IoT within<br/>food manufacturing. The second part introduces the IoT-based framework to address the<br/>monitoring of FEW. This framework was developed since most of the food manufacturers are<br/>not aware/or ignorant of their FEW and its environmental and financial value. The third part<br/>describes the implementation of the framework through an architectural prototype developed<br/>using a combination of software and hardware. The final part of the thesis demonstrates the<br/>application of the IoT tool to monitor the FEW in real-time using case studies and thereby<br/>support the management decisions aimed at reducing FEW.<br/>The industrial application of the research concepts proposed in this thesis through four specific<br/>case studies has highlighted the complexities as well as the opportunities available in using<br/>digital technologies in improving the overall sustainability of the food sector. This is due to the<br/>variety and specific nature of the food products as well as a large number of small to medium<br/>scale enterprises that form a significant proportion of the supply chain in this sector.<br/>In summary, this research has provided a practical and powerful tool based on IoT concepts<br/>to address the FEW within food manufacturing. This research has concluded that the<br/>consideration of the FEW reductions within the food industry requires an accurate, detailed<br/>and real-time based understanding of FEW in order to allow stakeholders to take proactive<br/>approaches in FEW reductions.}},
author = {{Jagtap, Sandeep}},
language = {{eng}},
publisher = {{Loughborough University}},
title = {{Utilising the Internet of Things concepts to improve the resource efficiency of food manufacturing}},
url = {{http://dx.doi.org/10.26174/thesis.lboro.11180456.v1}},
doi = {{10.26174/thesis.lboro.11180456.v1}},
year = {{2019}},
}