An overview of Big Tech’s 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.
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 Italy’s n...
OT-IT Integration: AWS and Siemens break down data silos by closing the machine-to-cloud gap
AWS announced that AWS IoT SiteWise Edge, on-premises software that makes it easy to collect, organize, process, and monitor equipment data, can now be deployed directly from the Siemens Industrial Edge Marketplace to help simplify, accelerate, and reduce the cost of sending industrial equipment data to the AWS cloud. This new offering aims to help bridge the chasm between OT and IT by allowing customers to start ingesting OT data from a variety of industrial protocols into the cloud faster using Siemens Industrial Edge Devices already connected to machines, removing layers of configuration and accelerating time to value.
Customers can now jumpstart industrial data ingestion from machine to edge (Level 1 and Level 2 OT networks) by deploying AWS IoT SiteWise Edge using existing Siemens Industrial Edge infrastructure and connectivity applications such as SIMATIC S7+ Connector, Modbus TCP Connector, and more. You can then securely aggregate and process data from a large number of machines and production lines (Level 3), as well as send it to the AWS cloud for use across a wide range of use cases. This empowers process engineers, maintenance technicians, and efficiency champions to derive business value from operational data that is organized and contextualized for use in local and cloud applications, unlocking use cases such as asset monitoring, predictive maintenance, quality inspection, and energy management.
The Blueprint for Industrial Transformation: Building a Strong Data Foundation with AWS IoT SiteWise
AWS IoT SiteWise is a managed service that makes it easy to collect, organize, and analyze data from industrial equipment at scale, helping customers make better, data-driven decisions. Our customers such as Volkswagen Group, Coca-Cola İçecek, and Yara International have used AWS IoT SiteWise to build industrial data platforms that allow them to contextualize and analyze Operational Technology (OT) data generated across their plants, creating a global view of their operations and businesses. In addition, our AWS Partners such as Embassy of Things (EOT), Tata Consulting Services (TCS) Edge2Web, TensorIoT, and Radix Engineering have made AWS IoT SiteWise the foundation for purpose-built applications that enable use cases such as predictive maintenance and asset performance monitoring. Through these engagements with customers and partners, we have learned that the main obstacles in scaling digital transformation initiatives include project complexity, infrastructure costs, and time to value.
With newly added APIs, AWS IoT SiteWise now allows you to bulk import, export, and update industrial asset model metadata at scale from diverse systems such as data historians, other AWS accounts, or – in the case of AWS Independent Software Vendors (ISV) Partners – their own industrial data modeling tools.
To collect real-time data from equipment, AWS IoT SiteWise provides AWS IoT SiteWise Edge, software created by AWS and deployed on premises to make it easy to collect, organize, process, and monitor equipment at the edge. With SiteWise Edge, customers can securely connect to and read data from equipment using industrial protocols and standards such as OPC-UA. In collaboration with AWS Partner Domatica, we recently added support for an additional 10 industrial protocols including MQTT, Modbus, and SIMATIC S7, diversifying the type of data that can be ingested into AWS IoT SiteWise from equipment, machines, and legacy systems for processing at the edge or enriching your industrial data lake. By ingesting data to the cloud with sub-second latency, customers can use AWS IoT SiteWise to monitor hundreds of thousands of high-value assets across their industrial operations in near real time.
Automate plant maintenance using MDE with ABAP SDK for Google Cloud
Analyzing production data at scale for huge datasets is always a challenge, especially when there’s data from multiple production facilities involved with thousands of assets in production pipelines. To help solve this challenge, our Manufacturing Data Engine is designed to help manufacturers manage end-to-end shop floor business processes.
Manufacturing Data Engine (MDE) is a scalable solution that accelerates, simplifies, and enhances the ingestion, processing, contextualization, storage, and usage of manufacturing data for monitoring, analytical, and machine learning use cases. This suite of components can help manufacturers accelerate their transformation with Google Cloud’s analytics and AI capabilities.
Bringing Scalable AI to the Edge with Databricks and Azure DevOps
The ML-optimized runtime in Databricks contains popular ML frameworks such as PyTorch, TensorFlow, and scikit-learn. In this solution accelerator, we will build a basic Random Forest ML model in Databricks that will later be deployed to edge devices to execute inferences directly on the manufacturing shop floor. The focus will essentially be the deployment of ML Model built on Databricks to edge devices.
Machine Learning Platform at Walmart
Walmart is the world’s largest retailer, and it handles a huge volume of products, distribution, and transactions through its physical stores and online stores. Walmart has a highly optimized supply chain that runs at scale to offer its customers shopping at lowest price. In the process, Walmart accumulates a huge amount of valuable information from its everyday operations. This data is used to build Artificial Intelligence (AI) solutions to optimize and increase efficiencies of operations and customer experience atWalmart. In this paper, we provide an overview of the guiding principles, technology architecture, and integration of various tools within Walmart and from the open-source committee in building the Machine Learning (ML) Platform. We present multiple ML use cases at Walmart and show how their solutions leverage this ML Platform. We then discuss the business impact of having a scalable ML platform and infrastructure, reflect on lessons learnt building and operating an ML platform and future work for it at Walmart.
Transforming Semiconductor Yield Management with AWS and Deloitte
Together, AWS and Deloitte have developed a reference architecture to enable the aforementioned yield management capabilities. The architecture, shown in Figure 1, depicts how to collect, store, analyze and act on the yield related data throughout the supply chain. The following describes how the modernized yield management architecture enables the six capabilities discussed earlier.
Collaboration and the speed of compute
When we first started building Encube, we realized that the only way to achieve the scale and speed of compute necessary to make real manufacturing simulation a core part of the product design cycle, would be to make all compute fully distributed. Not just multi-threaded, but truly distributed, with exponentially shorter compute times achieved through horizontal cloud scaling. And at the same time also leverage GPU compute, which is far more suited to linear algebra and 3D graphics related workloads, compared to the CPU.
If we consider the case of CNC machining, the market vertical we’re diving deep into at Encube, the only meaningful way to understand manufacturability of a component is to understand what it will cost to produce. And the only way to do this reliably, is to simulate the machining process itself. Crude measures like calculating the amount of material to be removed and dividing by a constant material removal rate (which is the current standard practice) are insufficient to generate an accurate best practice cost estimate.
GE Aerospace's cloud journey with AWS
Databricks Announces Lakehouse for Manufacturing, Empowering the World's Leading Manufacturers to Realize the Full Value of Their Data
Databricks, the lakehouse company, today announced the Databricks Lakehouse for Manufacturing, the first open, enterprise-scale lakehouse platform tailored to manufacturers that unifies data and AI and delivers record-breaking performance for any analytics use case. The sheer volume of tools, systems and architectures required to run a modern manufacturing environment makes secure data sharing and collaboration a challenge at scale, with over 70 percent of data projects stalling at the proof of concept (PoC) stage. Available today, Databricks’ Lakehouse for Manufacturing breaks down these silos and is uniquely designed for manufacturers to access all of their data and make decisions in real-time. Databricks’ Lakehouse for Manufacturing has been adopted by industry-leading organizations like DuPont, Honeywell, Rolls-Royce, Shell and Tata Steel.
The Lakehouse for Manufacturing includes access to packaged use case accelerators that are designed to jumpstart the analytics process and offer a blueprint to help organizations tackle critical, high-value industry challenges.
HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually
In this post, we share how HAYAT HOLDING—a global player with 41 companies operating in different industries, including HAYAT, the world’s fourth-largest branded diaper manufacturer, and KEAS, the world’s fifth-largest wood-based panel manufacturer—collaborated with AWS to build a solution that uses Amazon SageMaker Model Training, Amazon SageMaker Automatic Model Tuning, and Amazon SageMaker Model Deployment to continuously improve operational performance, increase product quality, and optimize manufacturing output of medium-density fiberboard (MDF) wood panels.
Quality prediction using ML is powerful but requires effort and skill to design, integrate with the manufacturing process, and maintain. With the support of AWS Prototyping specialists, and AWS Partner Deloitte, HAYAT HOLDING built an end-to-end pipeline. Product quality prediction and adhesive consumption recommendation results can be observed by field experts through dashboards in near-real time, resulting in a faster feedback loop. Laboratory results indicate a significant impact equating to savings of $300,000 annually, reducing their carbon footprint in production by preventing unnecessary chemical waste.
BMW Group Celebrates Opening the World's First Virtual Factory in NVIDIA Omniverse
NVIDIA Expands Omniverse Cloud to Power Industrial Digitalization
NVIDIA today announced that NVIDIA Omniverse™ 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 OVX™ infrastructure, with the scale and security of Azure cloud services.
How BigQuery helps Leverege deliver business-critical enterprise IoT solutions at scale
Leverege IoT Stack is deployed with Google Kubernetes Engine (GKE), a fully managed kubernetes service for managing collections of microservices. Leverege uses Google Cloud Pub/Sub, a fully managed service, as the primary means of message routing for data ingestion, and Google Firebase for real-time data and user interface hosting. For long-term data storage, historical querying and analysis, and real-time insights , Leverege relies on BigQuery.
BigQuery allows Leverege to record the full volume of historical data at a low storage cost, while only paying to access small segments of data on-demand using table partitioning. For each of these examples, historical analysis using BigQuery can help identify pain points and improve operational efficiencies. They can also do so with both public datasets and private datasets. This means an auto wholesaler can expose data for specific vehicles, but not the entire dataset (i.e., no API queries). Likewise, a boat engine manufacturer can make subsets of data available to different end users.
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 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 innovation—all 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 Digital’s 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
You’ve 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 — 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, retail’s 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.
Run Semiconductor Design Workflows on AWS
This implementation guide provides you with information and guidance to run production semiconductor workflows on AWS, from customer specification, to front-end design and verification, back-end fabrication, packaging, and assembly. Additionally, this guide shows you how to build secure chambers to quickly enable third-party collaboration, as well as leverage an analytics pipeline and artificial intelligence/machine learning (AI/ML) services to decrease time-to-market and increase return on investment (ROI). Customers that run semiconductor design workloads on AWS have designed everything from simple ASICs to large SOCs with tens of billions of transistors, at the most advanced process geometries. This guide describes the numerous AWS services involved with these workloads, including compute, storage, networking, and security. Finally, this paper provides guidance on hybrid flows and data transfer methods to enable a seamless hybrid environment between on-premises data centers and AWS.
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 plant’s 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 “few-shot learning,” 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 “fuel” 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 “Raw Zone”,” Cleansed”, and “Transformed” to build multiple fact tables suitable for the Enterprise team’s 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 you’re 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.