Revolutionizing Facility Maintenance: Unleashing the Power of LLM-Based Multi-Agent Chatbots

Brian Ho, Head of Data Analytics and AI at UEM Edgenta Berhad

Brian Ho, Head of Data Analytics and AI at UEM Edgenta Berhad

Facility maintenance involves intricate and crucial responsibilities, but it is currently encountering various challenges that impede productivity and efficiency. Organizations grapple with ineffective handling of maintenance requests due to reliance on manual procedures, resulting in delays and inaccuracies. The allocation of resources and management of workloads also require enhancement, as manual assignment of work orders fails to align with technicians’ skills and workloads efficiently. Furthermore, technicians often encounter difficulties promptly accessing pertinent knowledge or procedures for their tasks. Lastly, facility managers lack access to real-time data and essential insights necessary for informed, data-driven decision-making.

What our proposed solution does

Our proposed solution consists of a suite of AI-powered bots that optimize and simplify facility maintenance from start to finish.

For issue requestors, there’s the Requestor’s Helper Bot. This chatbot intakes maintenance requests in plain English, extracts details on assets and problems, prevents duplicate tickets, and logs everything so technicians can resolve problems faster. No more hassle submitting tickets!

For facility managers, there’s the Work Order Planner Bot. This chatbot automatically assigns work orders to technicians based on their skills and workloads. It updates your system and provides insights into key metrics by asking questions in natural language. You can now optimize assignments in a snap!

And for technicians, there’s your perfect troubleshooting mate — the Technician’s Helper Bot! Check new assignments, access repair procedures, and get help resolving tickets, all through regular conversation. With this bot by your side, resolving maintenance issues is easier than ever!

In summary, our proposed solution seamlessly connects requestors, managers, and technicians through AI-powered conversational agents. This suite of bots promises more efficient facility maintenance from beginning to end. Please refer to this YouTube link for our solution:

How we built it

Our team is excited to present Maintenance Tracker—an AI assistant that aims to streamline facility maintenance management. Through natural language understanding, Maintenance Tracker provides solutions tailored to three key user groups — the requestors, facility managers, and technicians.

• For requestors, it acts as a helper bot that automates ticket logging. You can simply tell it about an asset that needs repairs in plain English. The bot will check existing records to avoid duplicates and file a new ticket if it’s a fresh issue.

• As a facility manager, you can leverage the work order planner bot for smarter resource allocation. It suggests optimal matches based on skills and current workloads when assigning technicians. It also lets you analyze historical trends through conversational queries about metrics like ticket resolution times.

Maintenance Tracker aims to smoothen coordination between all stakeholders through AI assistance. Technicians can focus on repairs rather than manual tracking. Facility managers have enhanced oversight of work orders.

• Finally, for technicians, it delivers quick access to documentation required during repairs via chat, along with notifications about newly assigned tickets.

In essence, Maintenance Tracker aims to smoothen coordination between all stakeholders through AI assistance. Technicians can focus on repairs rather than manual tracking. Facility managers have enhanced oversight of work orders. The requestors have a simple way to report issues.

What’s next for LLM-Based Multi-Agent Facility Maintenance Chatbots

We aim to improve our service delivery by consolidating help desk and planner agents from three different bots into a unified, intelligent system. This will enhance user experiences and issue request and resolution workflows. We’re also considering integrating this unified system into an existing work order system to streamline the work order processes further.