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Data-Driven Predictive Maintenance for Industries

Malagi, Swapna LU and Selvaraj, Sathya Ruba LU (2021) INFM10 20211
Department of Informatics
Abstract
Due to the industry 4.0 paradigm shift, manufacturing and pro-duction industries are focusing on Predictive maintenance (PdM) to increase production efficiency and maintenance strategies. Also, adopting data-driven approaches to implement PdM as a result of increased data usage and exchange. However, facing technical and non-technical challenges. Previous studies majorly concentrated on technological aspects concerning the implementation of predictive maintenance, providing limited insight into organizational and environmental aspects. Considering this knowledge gap, this study focuses on examining and de-scribing factors impacting data-driven PdM implementation in various aspects. To answer the research question, the TOE framework was... (More)
Due to the industry 4.0 paradigm shift, manufacturing and pro-duction industries are focusing on Predictive maintenance (PdM) to increase production efficiency and maintenance strategies. Also, adopting data-driven approaches to implement PdM as a result of increased data usage and exchange. However, facing technical and non-technical challenges. Previous studies majorly concentrated on technological aspects concerning the implementation of predictive maintenance, providing limited insight into organizational and environmental aspects. Considering this knowledge gap, this study focuses on examining and de-scribing factors impacting data-driven PdM implementation in various aspects. To answer the research question, the TOE framework was adopted. We conducted qualitative research and collected data by carrying five semi-structured interviews with data-driven PdM practitioners. Research findings show that the following factors: Data Management and Collaboration & Communication are the most influential, other factors: IT-Infrastructure, Organization Size & Type of Industry, Existing Knowledge, Awareness & Educational Efforts, Cost, Top Management Support, Model Selection, and Competitive Pressure has certain or least influence while implementing PdM. The influence of External Support is considered inconclusive. (Less)
Please use this url to cite or link to this publication:
author
Malagi, Swapna LU and Selvaraj, Sathya Ruba LU
supervisor
organization
course
INFM10 20211
year
type
H1 - Master's Degree (One Year)
subject
keywords
Data-driven Predictive Maintenance, TOE Framework, Implementation, Impacting Factor
report number
INF21-063
language
English
id
9065220
date added to LUP
2021-09-14 12:24:55
date last changed
2021-09-14 12:24:55
@misc{9065220,
  abstract     = {{Due to the industry 4.0 paradigm shift, manufacturing and pro-duction industries are focusing on Predictive maintenance (PdM) to increase production efficiency and maintenance strategies. Also, adopting data-driven approaches to implement PdM as a result of increased data usage and exchange. However, facing technical and non-technical challenges. Previous studies majorly concentrated on technological aspects concerning the implementation of predictive maintenance, providing limited insight into organizational and environmental aspects. Considering this knowledge gap, this study focuses on examining and de-scribing factors impacting data-driven PdM implementation in various aspects. To answer the research question, the TOE framework was adopted. We conducted qualitative research and collected data by carrying five semi-structured interviews with data-driven PdM practitioners. Research findings show that the following factors: Data Management and Collaboration & Communication are the most influential, other factors: IT-Infrastructure, Organization Size & Type of Industry, Existing Knowledge, Awareness & Educational Efforts, Cost, Top Management Support, Model Selection, and Competitive Pressure has certain or least influence while implementing PdM. The influence of External Support is considered inconclusive.}},
  author       = {{Malagi, Swapna and Selvaraj, Sathya Ruba}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Data-Driven Predictive Maintenance for Industries}},
  year         = {{2021}},
}