Industrial service transition through data-enabled business models
Mustapha, Misbahu (2021)
Diplomityö
Mustapha, Misbahu
2021
School of Engineering Science, Tuotantotalous
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2021070140787
https://urn.fi/URN:NBN:fi-fe2021070140787
Tiivistelmä
Purpose – The aim of this thesis research work focused on how recent advances in industrial service transition have brought profound shifts in the global manufacturing industries. As a result of commoditization, infinitesimal growth, and decreasing profitability in essential product markets, manufacturers are increasingly turning to service-based strategies to stay competitive. The objective of this research is to better comprehend the process of transitioning from products to services. The research focused on the theoretical and practical challenges and opportunities emerging from Servitization using data-enabled services and Internet of Things (IoT), as it allows for formation of new business models and strategies partly based on big-data analytics and improvements. Based on literature closely linked to our focus subject, we examined and deduced the significance of creating value through data-driven business models.
Design/methodology/approach – This research work employs a qualitative method built on a multiple-case study approach. A total of four companies from various industries were interviewed. It is worth noting that the study focused on issues raised by the companies; the information reflects their perspectives on issues they have faced or are currently facing.
Findings – This research identifies wide scope of benefits and challenges relating to servitization of manufacturing companies using data-driven business models. It was shown that data-driven business models create opportunities, thus enabling businesses to harness the use of data they generate on daily basis to improve their potentials. Additionally, utilizing data can create new services or products which has a great potential to create a steady and balanced revenue model for companies. Some of the companies in the case study already had data and IoT solutions in operation. While several companies predicted that the true potential of the technology will be realized in a few years, if not longer. The challenges faced by companies in adopting this business model were also presented in this work.
Limitations/implications of the research – A qualitative study built on a multiple case study. As a result of the nature of the research approach, the identified patterns cannot be used as a predicting tool, particularly in terms of the case teachings' transferability and generalizability.
Practical implications – A framework was provided in this research to understand, analyze, plan, and develop a company’s data-driven business models based on resources, expertise, and the unique settings in which it works by following a step-by-step reference process.
Design/methodology/approach – This research work employs a qualitative method built on a multiple-case study approach. A total of four companies from various industries were interviewed. It is worth noting that the study focused on issues raised by the companies; the information reflects their perspectives on issues they have faced or are currently facing.
Findings – This research identifies wide scope of benefits and challenges relating to servitization of manufacturing companies using data-driven business models. It was shown that data-driven business models create opportunities, thus enabling businesses to harness the use of data they generate on daily basis to improve their potentials. Additionally, utilizing data can create new services or products which has a great potential to create a steady and balanced revenue model for companies. Some of the companies in the case study already had data and IoT solutions in operation. While several companies predicted that the true potential of the technology will be realized in a few years, if not longer. The challenges faced by companies in adopting this business model were also presented in this work.
Limitations/implications of the research – A qualitative study built on a multiple case study. As a result of the nature of the research approach, the identified patterns cannot be used as a predicting tool, particularly in terms of the case teachings' transferability and generalizability.
Practical implications – A framework was provided in this research to understand, analyze, plan, and develop a company’s data-driven business models based on resources, expertise, and the unique settings in which it works by following a step-by-step reference process.