How Generative AI Will Improve Incident Response Systems in the Manufacturing Industry

Incident Response (IR) systems in the manufacturing industry have significantly evolved over the decades. In the past, these systems were manual and reactive, relying on human observation and intervention. Incident detection was inefficient as it depended on physical inspections and manual reporting. Response to incidents was often delayed due to the time it takes to identify, report, and communicate an incident.

In contrast, modern IR systems are proactive, automated, and data-driven. They leverage advanced technologies such as sensors, IoT devices, and machine learning to detect incidents as they happen and even predict potential incidents based on patterns and trends. Proactive, instantaneous communication of incidents with automated alerts and notifications improves speed of resolution and customer satisfaction.

The most significant difference between legacy and modern systems is their ability to prevent incidents before they occur, their effectiveness, and user-friendliness. These aspects can be further improved by leveraging cutting-edge Generative AI to transform IR systems into interactive copilots that enable personnel to quickly address new incidents, learn from the past ones, proactively predict issues, and create powerful knowledge bases.

Retrieval Augmented Generation and Data Privacy

Generative AI can create human-like text by learning patterns from existing data. Models such as GPT-3 by OpenAI can write essays, answer questions, and summarize text by predicting the next word in a sentence based on the context provided by preceding terms.

Vector databases, on the other hand, are designed to store and query unstructured data (such as images, audio, and text) as high-dimensional vectors. It is achieved by embedding data into its vector representation, which machines understand. Vector Databases are particularly useful in machine learning and AI applications, where they can be used to find similarities between different pieces of data. Given a query vector, the database finds vectors that are closest to it in the embedding space. The similarity measure is typically based on a distance metric like Euclidean distance or cosine similarity.

Retrieval Augmented Generation (RAG) is a way to augment LLMs with additional data coming from a vector database. This can lead to the creation of powerful knowledge bases:

  1. The text is extracted from private data sources (GitHub, YouTube, PowerPoint presentations, text files, chats, etc.), split into chunks, and each piece is converted into an embedding vector by a Large Language Model (LLM) such as ChatGPT. It is graphically shown in the figure below:

  2. When it’s time to retrieve data from the knowledge base, the input prompt is embedded by the same LLM, the vector database retrieves similar vectors, and results are refined by the LLM model, which has access to conversation memory, taking into account what was previously asked. This is shown below:

However, leveraging this kind of system in the enterprise world was nearly impossible due to data privacy issues. Recently, solutions have been announced addressing multiple instances of someone leaking confidential data:

  1. Open-source models and C++ libraries can run these models locally. However, a big downside is their subpar performance compared to ChatGPT, as well as the price of hardware needed for their smooth run, especially on a large scale.
  2. Azure has incorporated OpenAI’s ChatGPT into their cloud offerings, specifically the OpenAI Service. They are now offering data privacy out of the box while keeping a pay-as-you-go payment system.

AI Copilot Systems

With cutting-edge technologies like Large Language Models (LLMs) and Vector Databases, it is possible to have an AI Copilot system, an intelligent assistant designed to aid and recommend actions in performing tasks and making decisions. Just like a copilot in an aircraft assists the pilot, an AI Copilot system assists users in navigating and completing complex tasks.

Microsoft is a clear leader on this front as they are standardizing the architecture for AI Copilots. They have incorporated this technology into their products, including GitHub and MS Office 365. We aim to include AI Copilot systems in our clients’ internal products to improve their offerings, increase productivity, and reduce the time of information flow throughout their organization.

Generative AI in Incident Response Systems

One of our clients, a leading product and service provider in the manufacturing industry, is involved in complex and potentially hazardous operations. A top-of-the-class IR system will bring significant benefits to the organization, such as improved safety, data-driven decision-making, and improved operations and safety practices by reviewing and learning from past incidents and their resolution.

In addition to a state-of-the-art IR system, an AI Copilot enables conversational experience with the current and past state of the system, thus streamlining day-to-day processes for engineers. Technologies like LangChain provide powerful ways to connect LLMs to various data sources, including SQL and NoSQL databases, and create powerful knowledge bases.

By leveraging Generative AI, our client’s IR system can recommend actions to engineers to resolve an incident based on responses to past incidents, predict the likely outcomes from different actions, and help them prioritize responses to multiple incidents. The IR system will include interactive dashboards that provide real-time information about incidents and will be voice-command powered, thus making them more user-friendly and efficient.

IR system will be able to analyze data from various IoT devices used in the railway industry, providing a comprehensive picture of an incident. In addition, it will personalize the user experience by learning individual users’ preferences and adapting its interaction style accordingly.

We will be able to connect complex data sources to knowledge bases that LLMs can query by using image-to-text models to describe the content of images and pair them with Computer Vision models to provide an in-depth analysis of incident images. Similarly, we plan on using video classification models to detect issues and accidents in real time and pair them with transcription models to get text data out of videos into vector databases.

Generative AI is not only a buzzword; it provides powerful tools to transform IR systems. With the integration of GenAI, engineers will streamline their response to minor incidents and major accidents and learn from past occurrences. These tools will transform IR systems into an interactive, personalized platform where engineers can find relevant information instantly, providing data-driven insights and recommendations on resolving incidents.