Bachelor-Project
Oct. 2023 – Feb. 2024
(A) Tool for Learning Organisations
(A) Tool for Learning Organisations aims to support organisations in a rapidly evolving technological and market environment by improving the use, sharing and application of knowledge. It aims to transform traditional organisations into learning organisations where the learning and knowledge potential of employees is maximized.
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Introduction
We set our focus on developing an information platform that leverages the collective intelligence and knowledge of employees to solve organisational problems and increase efficiency. It was recognized that solutions to many problems already exist within the system and simply need to be systematically captured and utilized. The central question of our work is: "How can individual knowledge be effectively utilized to enhance the organisation's overall success?"
Problem Statement
Despite the availability of modern technologies and information, individual knowledge often remains unused, which inhibits valuable exchanges and improvements. Many organisations struggle to harness the collective knowledge of their employees, leading to inefficient processes and missed opportunities for improvements. This project project addresses this challenge by developing a tool that encourages employees to find and share information. This platform is intended to serve as a communication tool across all hierarchical levels, improving the decision-making process by directly addressing issues and effectively forwarding improvement suggestions, enabling organizations to respond effectively to changing environments and requirements.
Concept
'(A) Tool for Learning Organisations' is an intelligent system for effortless employee feedback and information access. The project's objectives are to create a user-friendly and powerful platform based on the principles of a learning organization. This platform aims to improve communication and collaboration, increase employee engagement, and promote continuous improvements.
The tool that collects, analyses and exchanges individual knowledge and feedback in organisations. This huge amount of information is processed using AI. It integrates data from the company's database and discussion threads, providing quick answers, like floor plans of specific rooms. If the AI's response is insufficient, it prompts the creation of discussion threads where employees can engage and share insights on the topic.
AI technologies play a central role in the platform. NLP is used to interpret human language and analyze feedback. Data Mining and Text Mining help extract relevant information from large datasets, while Deep Learning supports analysis and pattern recognition. The tool employs methods like dominant topic extraction to distill key insights from Searches or the Database, which are then visualized and made available for further exchange and application within the organization. Acting as an information hub, it simplifies workspace communication by connecting employees to company resources and facilitating communication across departments.
Long-term, the company evolves into a true learning organisation where continuous learning, adaptability, and collaborative innovation are standard.
Features
The tool comprises three key components: Threads and Database, Search, and Analytics.
Threads and Database: This component stores all organized information from the database and threads, ensuring easy access to stored information for future use.
Search: The starting point for retrieving information and insights. Employees can find information they need or want to know from the organisational database and threads.
Analytics: This component processes and analyzes collected data to identify patterns, trends, and insights, providing actionable insights and supporting strategic decision-making by interpreting data.
Together, these components create a continuous cycle of knowledge enhancement and utilization within the organisation.
Proof of concept
The tool was successfully tested in a proof of concept, demonstrating how feedback can be collected, grouped, and analyzed to gain valuable insights. These insights are stored in a database and integrated via an API into a user-friendly interface that enables visualization and interaction. The proof of concept uses advanced algorithms such as BERTopic for topic modeling and HDBSCAN for clustering, showcasing the tool's capability to process and utilize feedback effectively.