This qualitative case study aims to examine the implementation of data-driven detection of unauthorized objects in the production line within the mining industry. With the purpose of contributing with an understanding of how the mining industry can work to create value in the operations as a result of an AI-implementation. Furthermore, the study aims to provide advice and recommendations to deal with identified challenges by highlighting the human factors. The method of collecting data was based on a pilot study, a visit to the organization, interviews with various stakeholders and regular check-in meetings with our contact persons. The data analysis has been data-driven and aimed to embed the empirical material in the project's real-life context. We have identified four challenges: concerns regarding the production stability, the process of information sharing, involvement as well as engagement of end users and stakeholders, and creating true value in the organization as a result of the implementation. Furthermore, based on the result and related research, the case study has formulated six recommendations that we believe can create great opportunities to refute the identified challenges. The result of this case study could be used to help the development of an implementation model for the mining industry and other similar industries.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-224904 |
| Date | January 2024 |
| Creators | Hellsten, Sofie, Falch, Julia |
| Publisher | Umeå universitet, Institutionen för informatik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Informatik Student Paper Bachelor (INFSPB) ; 2024:09 |
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