Prediktivt underhåll inom serviceverksamma industribolag
(2025) FEKH38 20242Department of Business Administration
- Abstract
- Title: Predictive maintenance in service-oriented industrial companies
Seminar date: 2025-01-16
Course: Business Administration: Degree Project in Business and Data Analytics, 15 credits
Authors: Carl Erneman, Ludvig Granath & William Lejon
Advisor: Magnus Johansson
Key words: predictive maintenance, analytics maturity, pilot projects, digital service, data-driven decision making
Research question: How do industrial companies manage the implications of predictive maintenance in their service-based business models, and why can it be beneficial to implement it?
Purpose: The purpose of the study is to explore the challenges that may arise during the implementation of predictive maintenance as a part of industrial... (More) - Title: Predictive maintenance in service-oriented industrial companies
Seminar date: 2025-01-16
Course: Business Administration: Degree Project in Business and Data Analytics, 15 credits
Authors: Carl Erneman, Ludvig Granath & William Lejon
Advisor: Magnus Johansson
Key words: predictive maintenance, analytics maturity, pilot projects, digital service, data-driven decision making
Research question: How do industrial companies manage the implications of predictive maintenance in their service-based business models, and why can it be beneficial to implement it?
Purpose: The purpose of the study is to explore the challenges that may arise during the implementation of predictive maintenance as a part of industrial companies service-based business models. Additionally, it aims to identify the value that predictive maintenance can bring to their business operations, both internally and externally.
Methodology: A case study with a qualitative methodological approach has been applied. The study follows an abductive approach and is based on four semi-structured in-depth interviews conducted with employees at the case company.
Theoretical perspectives: The theory is based on research in the areas of strategy analysis, big data, data-driven decision-making, and predictive maintenance from an organizational and strategic perspective.
Result: The study has identified that it primarily is organizational rather than technical challenges that hinder the implementation of predictive maintenance for the case company. It also highlights their use of pilot projects to demonstrate value and experiment with the technology. The empirical data shows that the case company lacks consensus in identifying and faces difficulties in quantifying the cost benefits of predictive maintenance, both from internal service efficiency improvements and external customer value.
Conclusion: The study shows that industrial companies can face both internal and external challenges when implementing predictive maintenance. Predictive maintenance within the service offering of industrial companies can create long-term value through improved customer relations, increased competitiveness and more efficient use of resources, compared to the more immediate cost savings presented by previous research mainly linked to
internal processes. (Less) - Abstract (Swedish)
- Titel: Prediktivt underhåll inom serviceverksamma industribolag
Seminariedatum: 2025-01-16
Kurs: FEKH38, Företagsekonomi: Examensarbete i Business and data analytics, 15 högskolepoäng
Författare: Carl Erneman, Ludvig Granath & William Lejon
Handledare: Magnus Johansson
Nyckelord: prediktivt underhåll, analytisk mognad, pilotprojekt, digital service, datadrivet beslutsfattande
Forskningsfråga: Hur hanterar industriföretag implementationen av prediktivt underhåll i deras tjänstebaserade affärsmodeller och varför kan det vara fördelaktigt att implementera?
Syfte: Syftet med studien är att undersöka vilka utmaningar som kan uppstå vid en implementation av prediktivt underhåll som en del av industriföretags tjänstebaserade... (More) - Titel: Prediktivt underhåll inom serviceverksamma industribolag
Seminariedatum: 2025-01-16
Kurs: FEKH38, Företagsekonomi: Examensarbete i Business and data analytics, 15 högskolepoäng
Författare: Carl Erneman, Ludvig Granath & William Lejon
Handledare: Magnus Johansson
Nyckelord: prediktivt underhåll, analytisk mognad, pilotprojekt, digital service, datadrivet beslutsfattande
Forskningsfråga: Hur hanterar industriföretag implementationen av prediktivt underhåll i deras tjänstebaserade affärsmodeller och varför kan det vara fördelaktigt att implementera?
Syfte: Syftet med studien är att undersöka vilka utmaningar som kan uppstå vid en implementation av prediktivt underhåll som en del av industriföretags tjänstebaserade affärsmodell. Även att identifiera vilket värde prediktivt underhåll kan ha på deras affärsverksamhet internt och externt.
Metod: En fallstudie med kvalitativ metodansats har tillämpats. Studien har en abduktiv ansats och bygger på fyra semistrukturerade djupintervjuer som genomförts med anställda på fallföretaget.
Teoretiska perspektiv: Teorin baseras på forskning inom områdena strategianalys, big data, datadrivet beslutsfattande och prediktivt underhåll ur ett organisatoriskt och strategiskt perspektiv.
Resultat: Studien har identifierat att det främst är organisatoriska och inte tekniska utmaningar som förhindrar implementation av prediktivt underhåll för fallföretaget. Även att pilotprojekt används för att visa värde och experimentera med tekniken. Empirin visar att fallföretaget inte är eniga om och har svårt att kvantifiera kostnadsfördelarna med prediktivt
underhåll från både intern serviceeffektivisering och externt kundvärde.
Slutsats: Studien visar att industriföretag vid implementationen av prediktivt underhåll kan stöta på både interna och externa utmaningar. Prediktivt underhåll inom industriföretagens tjänsteerbjudande kan skapa långsiktigt värde genom förbättrade kundrelationer, ökad konkurrenskraft och effektivare resursanvändning, jämfört med de mer omedelbara kostnadsbesparingar som tidigare forskning presenterat främst kopplat till interna processer. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9186341
- author
- Erneman, Carl LU ; Granath, Ludvig LU and Lejon, William LU
- supervisor
- organization
- course
- FEKH38 20242
- year
- 2025
- type
- M2 - Bachelor Degree
- subject
- keywords
- prediktivt underhåll, analytisk mognad, pilotprojekt, digital service, datadrivet beslutsfattande
- language
- Swedish
- id
- 9186341
- date added to LUP
- 2025-03-12 09:49:54
- date last changed
- 2025-03-12 09:49:54
@misc{9186341,
abstract = {{Title: Predictive maintenance in service-oriented industrial companies
Seminar date: 2025-01-16
Course: Business Administration: Degree Project in Business and Data Analytics, 15 credits
Authors: Carl Erneman, Ludvig Granath & William Lejon
Advisor: Magnus Johansson
Key words: predictive maintenance, analytics maturity, pilot projects, digital service, data-driven decision making
Research question: How do industrial companies manage the implications of predictive maintenance in their service-based business models, and why can it be beneficial to implement it?
Purpose: The purpose of the study is to explore the challenges that may arise during the implementation of predictive maintenance as a part of industrial companies service-based business models. Additionally, it aims to identify the value that predictive maintenance can bring to their business operations, both internally and externally.
Methodology: A case study with a qualitative methodological approach has been applied. The study follows an abductive approach and is based on four semi-structured in-depth interviews conducted with employees at the case company.
Theoretical perspectives: The theory is based on research in the areas of strategy analysis, big data, data-driven decision-making, and predictive maintenance from an organizational and strategic perspective.
Result: The study has identified that it primarily is organizational rather than technical challenges that hinder the implementation of predictive maintenance for the case company. It also highlights their use of pilot projects to demonstrate value and experiment with the technology. The empirical data shows that the case company lacks consensus in identifying and faces difficulties in quantifying the cost benefits of predictive maintenance, both from internal service efficiency improvements and external customer value.
Conclusion: The study shows that industrial companies can face both internal and external challenges when implementing predictive maintenance. Predictive maintenance within the service offering of industrial companies can create long-term value through improved customer relations, increased competitiveness and more efficient use of resources, compared to the more immediate cost savings presented by previous research mainly linked to
internal processes.}},
author = {{Erneman, Carl and Granath, Ludvig and Lejon, William}},
language = {{swe}},
note = {{Student Paper}},
title = {{Prediktivt underhåll inom serviceverksamma industribolag}},
year = {{2025}},
}