Northvolt is a Swedish battery manufacturing company that specializes in the production of sustainable lithium-ion batteries for electric vehicles and energy storage systems. Established in 2016, the company has experienced significant growth in recent years. This growth has presented a major challenge for the IT Support team, as they face a substantial volume of ITrelated inquiries. To address this challenge and allow the IT Support team to concentrate on more complex support tasks, a question-answering chatbot has been implemented as part of this thesis project. The chatbot has been developed using the Microsoft Bot Framework and leverages Microsoft cloud services, specifically Azure Cognitive Services, to provide intelligent and cognitive capabilities for answering employee questions directly within Microsoft Teams. The chatbot has undergone testing by a diverse group of employees from various teams within the organization and was evaluated based on three key metrics: effectiveness (including accuracy, precision, and intent recognition rate), efficiency (including response time and scalability), and satisfaction. The test results indicate that the accuracy, precision, and intent recognition rate fall below the required thresholds for production readiness. However, these metrics can be improved by expanding the knowledge base of the bot. The chatbot demonstrates impressive efficiency in terms of response time and scalability, and its user-friendly nature contributes to a positive user experience. Users express high levels of satisfaction with their interactions with the bot, and the majority would recommend it to their colleagues, recognizing it as a valuable service solution that will benefit all employees at Northvolt in the future. Moving forward, the primary focus should be on expanding the knowledge base and effectively communicating the bot’s purpose and scope to enhance effectiveness and satisfaction. Additionally, integrating the bot with advanced AI features, such as OpenAI’s language models available within Microsoft’s ecosystem, would elevate the bot to the next level.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-502878 |
| Date | January 2023 |
| Creators | Hjelm, Daniel |
| Publisher | Uppsala universitet, Signaler och system |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 23016 |
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