Cloud Computing
Recent Posts
Big Tech eyes Industrial AI and Robotics
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An overview of Big Techβs in-roads into manufacturing and industrial AI. From bin picking to robotic wire arc additive manufacturing (WAAM) the pace of industrial technology advances continues to pick up as digital transformation takes hold.
Where Are the Industry 4.0 Third-Party APIs?
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Exposing and leveraging third-party APIs is critical to the success of Industry 4.0 just like Web 2.0 companies used APIs to create new business models and gigantic businesses. Also, the cloud manufacturing wars heat up between Microsoft and Amazon, and commentary on Italyβs n...
Assembly Line
NVIDIA Expands Omniverse Cloud to Power Industrial Digitalization
NVIDIA today announced that NVIDIA Omniverseβ’ Cloud, a platform-as-a-service that enables companies to unify digitalization across their core product and business processes, is now available to select enterprises. NVIDIA has selected Microsoft Azure as the first cloud service provider for Omniverse Cloud, giving enterprises access to the full-stack suite of Omniverse software applications and NVIDIA OVXβ’ infrastructure, with the scale and security of Azure cloud services.
Walmart Amps Up Cloud Capabilities, Reducing Reliance on Tech Giants
Walmart Inc. says it has developed the capability to switch seamlessly between cloud providers and its own servers, saving millions of dollars and offering a road map to other organizations that want to reduce their dependence on giant technology companies.
Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers
The new manufacturing solutions from Google Cloud give manufacturing engineers and plant managers access to unified and contextualized data from across their disparate assets and processes.
Manufacturing Data Engine is the foundational cloud solution to process, contextualize and store factory data. The cloud platform can acquire data from any type of machine, supporting a wide range of data, from telemetry to image data, via a private, secure, and low cost connection between edge and cloud. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage.
Manufacturing Connect is the factory edge platform co-developed with Litmus Automation that quickly connects with nearly any manufacturing asset via an extensive library of 250-plus machine protocols. It translates machine data into a digestible dataset and sends it to the Manufacturing Data Engine for processing, contextualization and storage. By supporting containerized workloads, it allows manufacturers to run low-latency data visualization, analytics and ML capabilities directly on the edge.
Nonlinear Static Analysis: Snap-Fit Assembly
Cloud-native engineering simulation enables engineers to test the structural performance and structural integrity of their designs earlier and with accuracy. Advanced solvers that account for thermal and structural behavior can be accessed to provide robust assessments of deformation, stresses, and other design critical output quantities. In this article, we analyze the structural performance and integrity of a casing snap-fit assembly using cloud-native nonlinear static analysis. The focus of this analysis was to detect the peak stress regions, and therefore better understand the likelihood of permanent deformations. After analyzing the structural behavior, the design goal was to ensure safe snap operations, while minimizing the material yielding.
Using Ventilation Simulation to Increase the Performance of HVAC Systems
For the first time, HVAC engineers are able to explore the full design space for HVAC product designs, not just at the component level but the spatial (room) level where the products are installed. This reduces cost and time by avoiding the trial-and-error characteristics typically seen in physical prototyping.
Announcing the Microsoft Cloud for Manufacturing preview
The Microsoft Cloud for Manufacturing brings the best outcome-driven solutions and capabilities from Microsoft and our partners to accelerate time-to-value for our customers in an end-to-end, holistic, and scalable way. By connecting intelligent, integrated cloud, and edge capabilities of the Microsoft stack to the highest value manufacturing scenarios, we are creating a flywheel of innovation that helps businesses increase asset and frontline worker productivity in safe and secure factories, enable remote selling and always-on service, and unlock cloud-based innovationβall with the utmost trust, compliance, privacy, and transparency.
I am particularly excited about how we are integrating Microsoft Teams frontline workers and mixed reality across these capabilities. This will increase productivity in hybrid work scenarios, and allow insights from securely connected IoT assets and products to be integrated into workflows and business processes in Microsoft Dynamics 365 Business Applications and partner solutions.
Western Digitalβs Journey To Build Business Resiliency Through Cloud And ERP Transformation
In 2019, Western Digital started the most crucial part of the transformation journey. This fourth and final phase would transform manufacturing, inventory operations, and intercompany finance for 10 manufacturing plants across five countries, contract manufacturers and end users in a future-ready platform. Infosys was engaged to bring in an outside-in industry view to challenge current business practices and identify opportunities to harmonize process across the sites and standardize by eliminating custom practices.
The program was divided in multiple sub-phases. First sub-phase involved transforming manufacturing operations and intercompany transfers between component factories alongside payroll consolidation, reporting consolidation in Oracle BI. Second sub-phase had as many as 12 parallel projects for bringing hard disk drive manufacturing operations to cloud and consolidating all shipping and revenue operations, making way to retire two out of three legacy ERPs.
Forecast Anomalies in Refrigeration with PySpark & Sensor-data
A refrigeration has four important components: Compressor, Condenser Fan, Evaporator Fan & Expansion Valve. Loosely speaking, together they try to keep the pressure at a reasonable level so as to maintain the temperature within (Remember, PV = nRT). In Walmart, we collect sensor data for all of these components (eg. pressure, fan speed, temperature) at a 10 minutes interval along with metrics like if the system is in defrost or not, compressor is locked out or not etc. We also capture outside air temperature as it impacts the condenser fan speed and in turn, the temperature.
The objective is to minimize the number of malfunctions and suggest probable resolutions of the same to save time. So, we leveraged this telemetry information in order to forecast anomalies in temperature, which would help in prioritizing issues and be proactive rather than reactive.
Visual Inspection AI: a purpose-built solution for faster, more accurate quality control
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
We built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. By combining ease of use with a focus on priority uses cases, customers are realizing significant benefits compared to general purpose machine learning (ML) approaches.
Total Cost of Ownership Guide: No-Code App Platforms vs Traditional MES
Youβve found a no-code, IIoT native application platform that can replace your MES partially or fully. You are excited about augmenting human workflows, flexible deployments, and continuous improvements β but you have to do your due diligence and prove ROI.
We get it! No-Code App Platforms are new to the Industrial and Manufacturing technology landscape. Even though they were developed for a different era, Manufacturing Execution Systems (MES) are a tried and tested means of coordinating, executing, and tracking manufacturing processes.
What Walmart learned from its machine learning deployment
As more businesses turn to automation to realize business value, retailβs wide variety of ML use cases can provide insights into how to overcome challenges associated with the technology. The goal should be trying to solve a problem by using ML as a tool to get there, Kamdar said.
For example, Walmart uses a ML model to optimize the timing and pricing of markdowns, and to examine real estate data to find places to cut costs, according to executives on an earnings call in February.
Strategic Analytics Help Intertape Polymer Shrink Inefficiencies
For Intertape Polymer Group (IPG), a global manufacturer of packaging and protective solutions for industrial and e-commerce applications, the digital transformation process has always been about embracing technology with a keen eye on extracting the overall business value. As such, IPG is currently at different levels of maturity across the portfolio of digital technology deployments, including additive manufacturing, AR/VR training, IoT-based predictive downtime and robotic process automation.
IPG has taken advantage of the unique data modeling capabilities of the Sight Machine platform, which continuously transforms all data types generated by factory equipment and manufacturing software into a robust data foundation for analyzing and modeling a plantβs machines, production processes and finished products.
AWS Announces General Availability of Amazon Lookout for Vision
AWS announced the general availability of Amazon Lookout for Vision, a new service that analyzes images using computer vision and sophisticated machine learning capabilities to spot product or process defects and anomalies in manufactured products. By employing a machine learning technique called βfew-shot learning,β Amazon Lookout for Vision is able to train a model for a customer using as few as 30 baseline images. Customers can get started quickly using Amazon Lookout for Vision to detect manufacturing and production defects (e.g. cracks, dents, incorrect color, irregular shape, etc.) in their products and prevent those costly errors from progressing down the operational line and from ever reaching customers. Together with Amazon Lookout for Equipment, Amazon Monitron, and AWS Panorama, Amazon Lookout for Vision provides industrial and manufacturing customers with the most comprehensive suite of cloud-to-edge industrial machine learning services available. With Amazon Lookout for Vision, there is no up-front commitment or minimum fee, and customers pay by the hour for their actual usage to train the model and detect anomalies or defects using the service.
Introducing Microsoft Cloud for Manufacturing
What makes the Microsoft Cloud for Manufacturing unique is our commitment to industry-specific standards and communities, such as the Open Manufacturing Platform, the OPC Foundation, and the Digital Twins Consortium, as well as the co-innovation with our rich ecosystem of partners.
Industrial DataOps: Unlocking Data and Analytics for Industry 4.0
As an approach to data analytics, DataOps is all about reducing the time to high-accuracy analyses using automation, statistical process control, and agile methodologies so that manufacturers are able to use the data they collect quicker and with a higher degree of confidence.
The role of DataOps in Industry 4.0 is to take all of the info created and collected by machines, like IIoT devices, and effectively condense them into refined, usable business βfuelβ to drive decision-making, rather than be left to sit in a data warehouse, unexamined.
Advantages of Migrating to Cloud for Enterprise Analytics Environment
We are a data team. We spend the bulk of our efforts building out data pipelines from operational systems into our Decision Support infrastructure. We synthesize the analytical data assets from operational data flow and publish these assets for consumption across the enterprise. Our ETL pipelines are built using an in-house ETL framework with workflows that run on Map Reduce and tuned with TEZ parameters and some workloads using Apache Spark. Data flows through a series of logical stages from various sources across the organization into a βRaw Zoneβ,β Cleansedβ, and βTransformedβ to build multiple fact tables suitable for the Enterprise teamβs use-cases. The data is then flattened and loaded to the consumption layers for ease of business analysis and reporting. These works might be common among most of the companies today, and we hope that our story about overcoming a series of challenges through a cloud migration resonates with you and your teams.
Facilitating IoT provisioning at scale
Whether youβre looking to design a new device or retrofitting an existing device for the IoT, you will need to consider IoT provisioning which brings IoT devices online to cloud services. IoT provisioning design requires decisions to be made that impact user experience and security for both network commissioning and credential provisioning mechanisms which configure digital identities, cloud end-points, and network credentials so that devices can securely connect to the cloud.
Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
While robots are usually singled out as a key technology in studies of automation, the overall diffusion of robotics use and testing is very low across firms in the U.S. The use rate is only 1.3% and the testing rate is 0.3%. These levels correspond relatively closely with patterns found in the robotics expenditure question in the 2018 ASM. Robots are primarily concentrated in large, manufacturing firms. The distribution of robots among firms is highly skewed, and the skewness in favor of larger firms can have a disproportionate effect on the economy that is otherwise not obvious from the relatively low overall diffusion rate of robots. The least-used technologies are RFID (1.1%), Augmented Reality (0.8%), and Automated Vehicles (0.8%). Looking at the pairwise adoption of these technologies in Table 14, we find that use of Machine Learning and Machine Vision are most coincident. We find that use of Automated Guided Vehicles is closely associated with use of Augmented Reality, RFID, and Machine Vision.
AI Solution for Operational Excellence
Falkonry Clue is a plug-and-play solution for predictive production operations that identifies and addresses operational inefficiencies from operational data. It is designed to be used directly by operational practitioners, such as production engineers, equipment engineers or manufacturing engineers, without requiring the assistance of data scientists or software engineers.