Digital Twin Webinar: Understanding the Retail Analytics Glow-Up
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Make faster, more accurate decisions on the ground with computer vision across your supply chain.
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Taking advantage of edge computing, we can give you the capability to provide real insights into what is actually happening in your warehouse or terminal.
We can provide the flexibility to model out a wide-variety of scenarios to get you the information you need.
With computer vision, you can run Machine Learning (ML) inference on video feeds to detect hazardous conditions in warehouses or distribution centers. For example, if hard hats are required for all forklift drivers, we can build a solution that detects when a driver is not wearing the required hard hat, send an alert immediately on-site, AND send data of the violation to your existing ERP system for later analysis.
Making sure that you are identifying the right product can dramatically accelerate your automation journey. Our computer vision capabilities can help you and your team make sure the right product is being picked through text and barcode scanning. Our model can identify that a package has a barcode and check that information against WMS tools to ensure the right action is taken on that tool.
Tracking stock and controlling the flow has never been more complex – and important to figure out. Unfortunately, human processes are just not cutting it. Computer vision can capture images of products and track the movement via barcodes, which results in less lost or mis-identified inventory. Whether you need to track product on a production line, going in and out of warehouses, or coming into your retail store, computer vision can nearly eliminate product tracking errors.(Psst…see how we solved this $60M problem for a manufacturing client here.)
Most measurements and tracking today are handled by frontline employees. These tasks are mundane, overly manual, and error prone. Through computer vision, you can measure pallets as they arrive and track those pallets from storage to shipping. With more accurate measurements and locations, you’ll have less wasted space in your DCs and trucks, and more accurate reporting to customers.
Running heavy data processing in the cloud isn’t possible or attainable, often due to a lack of network connection or high cost. In addition, only relevant data needs to be sent to the cloud to reduce cost of ingress and egress. Existing edge devices are usually limited in their computing power and teams lack visibility and management.
Edge computing simplifies data collection, analytics, and processing at far-edge locations. Your data is stored on the appliance, where containerized applications process the data locally using ML inference, aggregation, and custom logic to generate insights. Configurable transfer jobs easily move aggregated insights and relevant data to Cloud Storage when connectivity and bandwidth are available.
Learn more about our Google partnership here.
Computer Vision Proof of Concept
90-day proof of concept to validate the business value of your solution.
Business validation
Use case & success definition
Working initial model
Prototype of scaled solution
Ready to get started? We’ll kick things off by creating a proof of concept for your computer vision solution. This engagement will validate the need, prove ROI, and show what’s possible with a working prototype of the computer vision solution.
Local Data Processing
AI, analytics, and database solutions uncover insights and remove traditional constraints of scale, performance, and cost.
Scalability through MLOps
Gain benefits of high-powered computing across devices in multiple locations.
High-Powered Edge Computing
Run high-power functions at the edge to enable computer vision and Google AI to get real-time insights on location.
Centralized Management
Cloud-backed control plane powered by Anthos that provides a consistent management experience at scale.
One of the world’s leading food processors was losing millions of dollars annually because of an overly manual and error-prone process that resulted in a higher-than-tolerable instance of misidentified and lost inventory on the factory floor. Leaders knew the existing process was woefully inefficient and riddled with repeated patterns of human error. Nearly all agreed that significant change was needed. Namely, a much more efficient system that reduced the load on its factory staff while increasing the accuracy and reliability of the labeling process.
Computer vision focuses on creating digital systems that can process, analyze, and make sense of visual data similar to humans.
An edge appliance is a secure, high-performance appliance for edge locations. It provides local storage, ML inference, data transformation, and export.
Edge appliances are ideal for use cases where bandwidth and latency limitations prevent organizations from processing the data from devices like cameras and sensors back in cloud data centers. These appliances simplify data collection, analytics, and processing at remote locations where copious amounts of data coming from these devices need to be processed quickly and stored securely.
Enterprises from the supply chain, manufacturing, and automotive verticals with low-latency and high throughput requirements can benefit from edge computing.
We build it for your unique environment. Using existing camera systems, developing software that will plug seamlessly into your ERP, WMS, TMS, or existing platforms. It’s important to note, there is not a one-size fits all solution, so we will tailor to your exact needs.
We know how to jump-start your client’s Google Edge Device implementation and bridge the gap between business and technology.Whether your client needs help accelerating their internal effort, or have the whole project handled externally, our team can get it done. Faster.
Let’s talk about what computer vision can do for your business. We’ll walk you through some of the work we’ve done and figure out where you could get the most value from a computer vision solution.
Three key focus areas every leader should be thinking about right now in order to create a more collaborative, outcome-focused culture
Digital twin is not just a buzzword; it holds a lot of power and opportunity for retail organizations to improve their operations and get real-time information about the customer in ways that other data analytic strategies can't.
A closer look at how the emerging tech and analytics teams applied machine learning to Operational Technology, increasing product identification accuracy to over 90%
