Establishing Predictive Maintenance Business in a Furniture Manufacturing Company
Rosenius, Aleksi (2020)
Rosenius, Aleksi
2020
Tietojohtamisen DI-tutkinto-ohjelma - Degree Programme in Information and Knowledge Management, MSc (Tech)
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2020-05-27
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202005155378
https://urn.fi/URN:NBN:fi:tuni-202005155378
Tiivistelmä
The objective of this study was to find out how to establish predictive maintenance business in a furniture manufacturing company. The target organization is starting the development work with maintenance business but needs external resources for that. Usually, there is no organized maintenance in furniture business, since the products are seen as consumables, which will be disposed or reused at the end of their life cycle. However, the target organization is producing products that include technological and technical solutions and wants to lengthen their lifetime.
This research was conducted as a case study, which intended to create a solution for one target organization’s problem. Maturity model thinking was used as a starting point for this study. According to that, developing maturity will simultaneously develop the comprehensive capabilities of an organization, which will assist in the thorough problem solving. In this study, a literature research was conducted, which created the theoretical background for the whole thesis. The basic ideas and different strategies for organizing maintenance are introduced in the theoretical background. Also, the basics of maturity model thinking and different already created maturity models for maintenance development are addressed. Based on the theoretical background, a customized maturity model was created in cooperation with the target organization. That maturity model includes different subjects called dimensions, which include the essential matters regarding predictive maintenance development. These dimensions were evaluated with interviews that were conducted with the employees of the target organization. The interviewees were asked to evaluate the current maturity state of maintenance, in order to see where the organization is at the moment. The target maturity state for the future was set in a workshop that was also held for the employees of the target organization. Based on the information gained, a roadmap was created for the predictive maintenance business development.
As a result from this study, the current maturity state and two different target maturity states were received. Based on the current maturity state, the target organization is at the beginning of establishing a maintenance business. They do not have the needed capabilities to offer predictive maintenance services to their customers. The major development areas were seen to be in data and analytics usage, technology implementation and skills acquisition. Two target maturity states for the future, short-term and long-term, were set in collaboration with the target organization. The short-term targets were set on a moderately high level, which means that the organization must start developing their capabilities as soon as possible and across the different organizational units. In order to achieve the target states, a roadmap for the development process was created. This roadmap includes recommendations for each dimension in the maturity model. By following the roadmap, the target organization is able to increase their maturity and thus, their capabilities regarding predictive maintenance.
This research was conducted as a case study, which intended to create a solution for one target organization’s problem. Maturity model thinking was used as a starting point for this study. According to that, developing maturity will simultaneously develop the comprehensive capabilities of an organization, which will assist in the thorough problem solving. In this study, a literature research was conducted, which created the theoretical background for the whole thesis. The basic ideas and different strategies for organizing maintenance are introduced in the theoretical background. Also, the basics of maturity model thinking and different already created maturity models for maintenance development are addressed. Based on the theoretical background, a customized maturity model was created in cooperation with the target organization. That maturity model includes different subjects called dimensions, which include the essential matters regarding predictive maintenance development. These dimensions were evaluated with interviews that were conducted with the employees of the target organization. The interviewees were asked to evaluate the current maturity state of maintenance, in order to see where the organization is at the moment. The target maturity state for the future was set in a workshop that was also held for the employees of the target organization. Based on the information gained, a roadmap was created for the predictive maintenance business development.
As a result from this study, the current maturity state and two different target maturity states were received. Based on the current maturity state, the target organization is at the beginning of establishing a maintenance business. They do not have the needed capabilities to offer predictive maintenance services to their customers. The major development areas were seen to be in data and analytics usage, technology implementation and skills acquisition. Two target maturity states for the future, short-term and long-term, were set in collaboration with the target organization. The short-term targets were set on a moderately high level, which means that the organization must start developing their capabilities as soon as possible and across the different organizational units. In order to achieve the target states, a roadmap for the development process was created. This roadmap includes recommendations for each dimension in the maturity model. By following the roadmap, the target organization is able to increase their maturity and thus, their capabilities regarding predictive maintenance.