Abhijeet Pandey
ABSTRACT
In modern times, different software tools are used in facilities maintenance to record maintenance problems as tickets. When these tickets are resolved, technicians also document the steps they took to fix the issue as resolution notes. The increasing use of these systems helps us gather a lot of information about past tickets and their solutions. Many times, similar tickets are created that can be solved by following similar steps used before. This abundance of data makes it easier to transfer knowledge by recommending resolution steps to solve a new issue based on the solutions applied to similar tickets in the past. The prompt and accurate resolution can help to minimize downtime and maximize productivity. Traditional methods of resolution recommendation for facilities maintenance tickets rely on manual analysis of historical data and predefined rules. These methods are time-consuming and often yield suboptimal results. In this paper, we propose a novel approach for resolution steps recommendation using deep learning techniques. We first generate embeddings of tickets using a fine-tuned sentence transformer model and employ FAISS (Facebook AI Similarity Search) [9] to speed up the process of finding the most similar tickets. Our proposed resolution generator architecture first combines these similar tickets and then passes them to a summarization layer which clubs these resolutions into one, by removing redundancies and only considering actionable sentences. This summarized resolution is then passed to a text generation network which then converts these sentences into a series of steps that technicians can take to solve the current problem. Our approach was evaluated on a real-world facilities maintenance dataset, and the results show that our proposed method outperforms existing approaches.
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Index Terms
- SToRM: Smart ticket resolution steps recommendation in facilities management
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