Unlocking all of the insights hidden within manufacturing data has the potential to enhance efficiency, reduce costs and boost overall productivity for numerous and diverse industries. Finding insights within manufacturing data is often challenging, because most manufacturing data exists as unstructured data in the form of documents, equipment maintenance records, and data sheets. Finding insights in this data to unlock business value is both a challenging and exciting task, requiring considerable effort but offering significant potential impact.
AWS Industrial IoT services, such as AWS IoT TwinMaker and AWS IoT SiteWise, offer capabilities that allow for the creation of a data hub for manufacturing data where the work needed to gain insights can start in a more manageable way. You can securely store and access operational data like sensor readings, crucial documents such as Standard Operating Procedures (SOP), Failure Mode and Effect Analysis (FMEA), and enterprise data sourced from ERP and MES systems. The managed industrial Knowledge Graph in AWS IoT TwinMaker gives you the ability to model complex systems and create Digital Twins of your physical systems.
Generative AI (GenAI) opens up new ways to make data more accessible and approachable to end users such as shop floor operators and operation managers. You can now use natural language to ask AI complex questions, such as identifying an SOP to fix a production issue, or getting suggestions for potential root causes for issues based on observed production alarms. Amazon Bedrock, a managed service designed for building and scaling Generative AI applications, makes it easy for builders to develop and manage Generative AI applications.
In this blog post, we will walk you through how to use AWS IoT TwinMaker and Amazon Bedrock to build an AI Assistant that can help operators and other end users diagnose and resolve manufacturing production issues.
Solution overview
We implemented our AI Assistant as a module in the open-source “Cookie Factory” sample solution. The Cookie Factory sample solution is a fully customizable blueprint which builders can use to develop an operation digital twin tailored for manufacturing monitoring. Powered by AWS IoT TwinMaker, operations managers can use the digital twin to monitor live production statuses as well as go back in time to investigate historical events. We recommend watching AWS IoT TwinMaker for Smart Manufacturing video to get a comprehensive introduction to the solution.
Figure 1. Components of the AI Assistant module
The Cookie Factory AI Assistant module is a python application that serves a chat user interface (UI) and hosts a Large Language Model (LLM) Agent that responds to user input. In this post, we’ll show you how to build and run the module in your development environment. Please refer to the Cookie Factory sample solution GitHub repository for information on more advanced deployment options; including how to containerize our setup so that it’s easy to deploy as a serverless application using AWS Fargate.
Prerequisites
For this tutorial, you’ll need a bash terminal with Python 3.8 or higher installed on Linux, Mac, or Windows Subsystem for Linux, and an AWS account. We also recommend using an AWS Cloud9 instance or an Amazon Elastic Compute Cloud (Amazon EC2) instance.