In today’s rapidly evolving manufacturing landscape, digital twins of factory processes have emerged as a game-changing technology. Digital twins serve as virtual replicas of physical manufacturing processes, allowing organizations to simulate and analyze their operations in a virtual environment. By incorporating artificial intelligence and machine learning, organizations can interpret and classify objects, leading to cost reductions, faster throughput speeds, and improved quality levels. Real-time data, especially inventory information, plays a crucial role in these virtual factories, providing up-to-the-minute insights for accurate simulations and dynamic adjustments.
By the end of this demo, you’ll learn how to create a virtual factory in five steps and implement a real-time computer vision inventory inference solution. The focus will be on connecting a physical factory with its digital twin using MongoDB Atlas, which facilitates real-time interaction between the physical and digital realms.
Healthcare: In healthcare, digital twins can be used to simulate and optimize hospital workflows, patient care processes, and resource allocation. These simulations can help identify bottlenecks, improve patient outcomes, and enhance overall operational efficiency.
Oil and Gas: In the oil and gas sector, digital twins can be used to simulate drilling operations, optimize oil rig layouts, and predict potential equipment failures. These simulations aid in reducing downtime and maximizing productivity.
Retail: In retail, digital twins can simulate store layouts, customer foot traffic, and product placements. These simulations can help retailers design store layouts that enhance the shopping experience and increase sales.
As you may know, any piece of data that can be represented in JSON can be natively stored in and easily retrieved from MongoDB. The MongoDB drivers take care of converting the data to BSON (binary JSON) and back when querying the database. Furthermore, you can use documents to model data in any way you need, whether it is key-value pairs, time-series data, or event data.
collections allow you to automatically store time series data in a highly optimized and compressed format, reducing customer storage footprint, as well as achieving greater query performance at scale.
Based on the previous business requirements, we created a 3D-model of the factory in a widely used game engine, Unity. This virtual model can be visualized using a computer, tablet, or any virtual reality headset. Download the project from our .
How do we make the Digital Twin aware of the stock in the warehouse of the Physical Factory so we make it possible to order within Unity? We will have a camera pointing to the warehouse of a physical factory and sending images to MongoDB and Sagemaker to infer the stock of the physical warehouse with Computer Vision. Once we've inferred the stock, we will make the Digital Twin Atlas Device SDK database aware of the state of it thanks to . Follow the steps in our .
Learn how to bring IoT, visualization, and AI together seamlessly to create a virtual factory.
MongoDB’s flexible, highly available, and scalable platform allows for end-to-end data integration using a four-step framework.