LLM-Analytica: Facilitating Large Language Models for Industrial Analytics
Keywords: Large Language Models, Tool Usage, Industrial Analytics
TL;DR: LLM-Analytica is introduced as a framework to simplify industrial analytics and improve efficiency by integrating expert-designed modules and iterative prompting, addressing the challenges of using advanced tools in Industry 4.0.
Abstract: The rise of Industry 4.0 has led to significant advances in real-time process monitoring and predictive maintenance, aided by machine learning and deep learning tools developed over the past decade. However, on account of a steep learning curve, usage of these tools remains a prerogative of a limited set of users who are proficient in programming. There is a need for good and easy to use analytics platforms that can be used by practitioners in manufacturing industries. This need has unfortunately remained a challenge. The tool handling capability of LLMs holds a new promise, but their performance for manufacturing domain is often poor and largely untested. We introduce LLM-Analytica, a framework for developing end-to-end workflows for industrial analytics designed to perform tasks like process optimization, fault detection and diagnosis, and predictive maintenance for maintaining and improving the plant KPIs such as efficiency, productivity, product quality, reliability, etc. We have integrated 60+ expert-designed modules and used iterative prompting for pipelining to help LLM-Analytica augment the performance of LLMs for industrial analytics. The effectiveness of LLM-Analytica for automating a wide array of industrial analytics tasks is demonstrated and evaluated using expert feedback. This work is expected to accelerate industrial analytics activities and the development of digital twins thereby helping the industry in improving efficiency.
Submission Number: 11
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