Today, manufacturers are advancing on their factory digitalization journey, betting on innovative technologies to strengthen competitiveness, deliver sustainable growth, and offer new services. Macroeconomic factors – such as high energy costs, increasing labor, and raw material shortages – drive the need for urgent operational optimizations and automation.

Cloud capabilities have matured at an accelerated pace, giving manufacturers practical avenues to achieve these goals. Manufacturers are finding new ways to bring AI and machine learning (ML) to practical use cases, like predictive maintenance, anomaly detection, and asset utilization management. However, manufacturers struggle to adopt AI at scale due to challenges around data accessibility, infrastructure, and technology.

Google Cloud created purpose-built tools and solutions to organize manufacturing data, make it accessible and useful, and help manufacturers quickly take significant steps on this journey by reducing the time to value. In this post, we will explore a practical example of how manufacturers can use Google Cloud manufacturing solutions to train, deploy and extract value from ML-enabled capabilities to predict asset utilization and maintenance needs.

The journey to machine learning insights starts with accessible data

The first step to a successful machine learning project is to unify necessary data in a common repository. For this, we will use Manufacturing Connect, the factory edge platform co-developed with Litmus Automation, to connect to manufacturing assets and stream the asset telemetries to Pub/Sub.

After the telemetry messages are published to Pub/Sub, Dataflow will identify each message based on its structure and apply corresponding normalizations and transformations, which are preconfigured in Manufacturing Data Engine. Once the messages are processed, the messages will be routed to Cloud Storage, BigQuery, and/or Cloud BigTable based on user configuration.

To train a machine learning model, manufacturers can use Vertex AI AutoML to build a no-code model based on the training data stored in the Manufacturing Data Engine.

Then, users can trigger a batch prediction job in Vertex AI or export the AutoML model to run on the edge component of Manufacturing Connect for real-time prediction. Regardless of the model deployment methods, the prediction and explanation results will be ingested into Manufacturing Data Engine, which can be analyzed and visualized in Looker.

A blueprint for classifying asset condition

The following scenario is based on a hypothetical company, Cymbal Materials. This company is a factitious discrete manufacturing company that runs 50+ factories in 10+ countries. 90% of Cymbal Materials manufacturing processes involve milling, which are accomplished using industrial computer numerical control (CNC) milling machines. Although their factories implement routine maintenance checklists, there are unplanned and unknown failures that happen occasionally. However, many of the Cymbal Materials factory workers lack the experience to identify and troubleshoot failures due to labor shortage and high turnover rate in their factories. Hence, Cymbal Materials is working with Google Cloud to build a machine learning model that can identify and analyze failures on top of Manufacturing Connect, Manufacturing Data Engine, and Vertex AI.

For the pilot, Cymbal Materials forms a team of manufacturing engineers and data scientists to evaluate the feasibility of solving the tool wear detection problem. To avoid compliance concern, the Cymbal Materials team chooses to start with a public tool wear detection dataset hosted on Kaggle. This dataset is collected from running machining experiments on 2″ x 2″ x 1.5″ wax blocks in a CNC milling machine. The dataset contains measurements from the 4 motors (X,Y, Z axes and spindle) and program values in the CNC machine, which maps well to the data that Cymbal Materials collects for their CNC milling machines.

To start, the Cymbal Materials data scientists download the tool wear detection dataset from Kaggle and upload the dataset to Cloud Storage. Then, the data scientists use Vertex AI to:

• Perform exploratory data analysis using Vertex AI Workbench
• Train machine learning models using Vertex AI AutoML
• Deploy machine learning models to perform batch prediction and export AutoML models for edge deployment
• Interpret predictions using Vertex Explainable AI

After seeing great performance of the AutoML tabular model, the Cymbal Materials data scientists decide to use the AutoML model to predict on the actual CNC milling machine telemetries from their factories and validate the generalizability of the AutoML model. They ask the manufacturing engineers to deploy Manufacturing Connect in a Cymbal Materials factory and stream telemetries for one CNC milling machine to the Manufacturing Data Engine.

Manufacturing Connect includes an edge component that can gather data from manufacturing assets via an extensive library of 250+ communication protocols. The edge component of Manufacturing Connect comes with built-in Node-RED and Docker runtime, which support running custom workflows and machine learning models at the edge.

Using pre-defined hierarchies, Manufacturing Connect pushes asset telemetries and states to Pub/Sub.

After the factory operational data are ingested into Pub/Sub, Cymbal Materials uses Manufacturing Data Engine to:

• Normalize, transform, and contextualize real-time operational data with slowly changing metadata
• Batch ingest historical operational data and prediction results
• Route data dynamically to Cloud Storage, BigQuery, and/or Cloud BigTable

Using the trained AutoML tabular model and real-time telemetries from CNC milling machines, the data scientists trigger a batch prediction job on the CNC milling machine telemetries in BigQuery. The data scientists configure the batch prediction to output prediction results in Cloud Storage such that Manufacturing Data Engine can batch ingest the prediction results after the batch prediction job completes.

To consume the prediction results, the Cymbal Materials manufacturing engineers use Looker to create visualizations. The dashboard allows the manufacturing engineers to:

• Visualize the CNC milling machine actual and predicted tool conditions over time
• Explain the prediction results by summarizing the top attributing features
• Create alerts based on the predicted tool condition for their assets
• Take actions by contacting the supplier and/or scheduling maintenance for their assets

From edge to cloud, improving production efficiency for manufacturers

To support the entire factory digitalization value journey, manufacturers are looking for capabilities from simple visualizations to predictive ML models. Robust solutions, such as the one covered here, provide rapid paths for engineers to extract insight from their factory data.

Having a common data repository for manufacturing data, industry-leading machine learning platform, and versatile dashboard components accelerate manufacturer’s digital transformation.

This solution brings the best of Google Cloud’s data analytics and artificial intelligence capabilities in an industrial environment. Manufacturing Connect creates the link between industrial machinery and Manufacturing Data Engine, the cloud platform where the manufacturing data are processed, normalized, contextualized, and stored in a ready-to-consume format. Vertex AI can build, deploy, and scale machine learning models using data stored in the Manufacturing Data Engine. Vertex AI includes AutoML and Workbench for training models without code and training custom models with code-first experience respectively.

Learn more about how Google Cloud is transforming manufacturing to meet changing customer expectations at our Google Cloud Next Manufacturing playlist.

What’s next

1. Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers
2. Manufacturing Data Engine | Solutions | Google Cloud
3. GitHub – GoogleCloudPlatform/mfg-ml-examples

Hello! ¡Hola! 你好! नमस्ते! Bonjour! Being greeted in your own language can instantly put a smile on your face, and organizations around the world recognize that messages become more inclusive and powerful when translated into many languages.

As the Head of Product Management for Translation AI at Google Cloud, I am always looking to make our user’s lives easier when it comes to language tools—like Google Cloud’s Document Translation API, which has been used to translate over 2.5 billion documents across Google Translate and our Cloud customers.

But during my time working on translation products, I have seen enterprise users making compromises, like sacrificing cost and speed of translation for quality, or paying much more for high-quality translations and making peace with the slow turnaround times and high costs. That’s why we are excited to see momentum building behind Translation Hub, which launched earlier this year. Built to address organizations’ localization and translation challenges and delight users with an incredibly intuitive and administratively simple offering, Translation Hub makes it easy to translate content at scale—users just need to select the files to be translated, choose the output languages, and then they’re off and running.

In this article, we’ll take a closer look at Translation Hub’s powerful features, and the ways it is helping customers do more with their content.

Enterprise translation, reimagined: fast, lean, and intuitive

Translation Hub is a fully-managed, self-serve translation offering, powered by Google AI and built for the enterprise. With Translation Hub, businesses can instantaneously translate content into 135 languages with a single click, via an intuitive interface that integrates human reviews (i.e., a “human in the loop”) where required.

Translation Hub is designed to be both easy to manage and use. Each organization’s Translation Hub administrator uses the Google Cloud console to onboard and manage business users, typically by adding their email, which triggers an invite to the business user. Once a business user is added, they can sign in and start requesting translations and post-editing reviews.

With advanced features like “Glossary” and “Custom Translation Models (AutoML Translation),” Translation Hub makes it easy for enterprises to control the terminology and inject domain-specific context to meet their unique needs. Furthermore, Translation Hub ensures that organizations own their Translation Memory, for use in future translations or to leverage as a rich, human-validated source of training data for custom translation models (e.g., AutoML Translation). For example, when a user edits a phrase or segment through Translation Hub, this is immediately captured and used in the next translation for all future users who initiate translation where the same content or segment appears.

One of my favorite features is that Translation Hub preserves the layout and format of documents, saving both time and money. And while Translation Hub works with Google Drive (you can ingest and export documents to Drive) and is compatible with all our Google Workspace content sources (e.g., Slides, Docs etc.), it also supports many of the most commonly used formats, like documents and presentations created in Microsoft Office, and scanned and native PDFs.

Organizations need to be able to share the output of AI-powered translation with localization teams or agencies, for review. They need to save time by leveraging glossaries or customer machine learning (ML) models. They need these capabilities in one cohesive platform—and they need all of this to be user-friendly for all kinds of business users. It’s the difference between being able to automate translations in a few scenarios and being able to translate at scale. Google Cloud’s mission is to accelerate every organization’s ability to digitally transform, and with Translation Hub, our customers can leverage the best of AI for their content translations without sacrificing speed, quality or cost.

All of Google’s Translation Innovations within a single product

Translation Hub uses Google Cloud’s Translation API, AutoML Translation, and innovations in Neural Machine Translation Quality Prediction and Translation Memory—all technologies we’ve put to good use inside Google. For example, our localization teams have realized more than $80 million in cost avoidance and savings since 2021, using Google Cloud AutoML Translation and localization expertise.

“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”

To get started with Translation Hub, visit our solution page, and for a deep dive, check out this Google Next ‘22 session, including more details about Avery Dennison’s use case.

“Talk about tough times, right?”

That’s how Dan Stuart, Senior Vice President of IT Services at Southwire Company, refers to the months following a December 2019 ransomware event, and the COVID crisis that began in spring of 2020. Those events hit just as the company was preparing for an overhaul of their SAP environment. This comprehensive plan included three key elements. First, the company wanted to upgrade their SAP ECC environment to take advantage of the latest functionality available for this critical ERP system. Second, Southwire aimed to deploy SAP Business Warehouse on SAP HANA to accelerate vital reporting for all business users. Third, the company wanted to upgrade to the latest version of SAP Process Orchestration—an essential component that touches key manufacturing interfaces in all Southwire facilities. 

Southwire had looked at multiple options for the upgrades, including remaining entirely on-premises, colocation, and full cloud migration. “Going to the cloud seemed a lot more compelling,” says Joe Schleupner, Southwire’s Senior Director of PMO & ITS planning and implementation. “We were going to the cloud eventually, so why take these intermediary steps? Let’s just get it done.”

After looking at several options, Southwire decided to migrate to Google Cloud. “We wanted to be on a platform for SAP that was flexible, scalable, and secure; that we could count on to get up and running quickly,” says Stuart. “We chose Google Cloud not only for those reasons, but also because we recognize that Google has other assets that we may be able to take advantage of down the line, such as technologies like artificial intelligence (AI).” 

More stability, less worry

As one of the leading manufacturers of wire and cable used in the transmission and distribution of electricity, Southwire aids the delivery of power to millions of people worldwide. They have more than 30 manufacturing facilities across the United States running 24/7. Any downtime directly affects productivity and revenue. With help from Google Cloud and their implementation partner NIMBL, Southwire completed the SAP migration to Google Cloud over a planned maintenance weekend on July 4th.

The migration itself, while complex, went quickly and smoothly. “Just moving to the cloud was quite a feat because we were dealing with so much data, but in total the SAP system was down for only ~16 hours,” says Schleupner.

“As a project manager, I always felt that Google Cloud had my back” Schleupner says. The Process Orchestration (PO) migration was of particular concern, considering that it controlled all of Southwire’s manufacturing interfaces across the entire company. “Every critical piece of information that goes from SAP down to the manufacturing system goes through that system,” says Schleupner.

Even after migrating, Southwire discovered that making changes to the system was fast, easy, and resulted in no downtime. Normally, certain types of changes would have involved taking down SAP for at least an hour.

The Southwire team also appreciates the fact that the modern cloud architecture means spending less time on routine infrastructure maintenance. “It’s one less thing for me to worry about,” Stuart says, “I can focus on the business side of the house and move the technology and responsibilities to what we do within the Google Cloud Platform.”

What comes next?

While the cloud migration will increase stability, uptime, performance, and security, there is much more to come. Southwire is currently working on a disaster recovery implementation for their SAP environment on Google Cloud. Stuart and Schleupner are excited about where Google Cloud can further take Southwire. They are considering an SAP Hybris e-commerce implementation as well as connected factory and/or factory automation initiatives that can take advantage of artificial intelligence and machine learning. 

To Stuart and Schleupner, the migration of Southwire’s SAP environment to Google Cloud, as important as it was, really represents the first step in the company’s tech evolution. Now that much of the heavy lifting is complete, Southwire’s digital transformation can begin in earnest. “There’s no shortage of areas where I think Google Cloud will come into play,” Stuart says, “and we intend to look at these things with an open mind to understand how we can leverage current investments to take our organization where we want to go.”

Learn more about Southwire’s SAP on Google Cloud deployment and how Google Cloud can transform the way you work with your SAP enterprise applications. Visit cloud.google.com/solutions/sap.

Google Cloud results

• Helps enable instant document approval with optical character recognition by Vision API
• Processes 100,000 documents in 20 minutes with automated document processing product Recognic, powered by Vision API and Apigee
• Helps increase the number of applications processed by 30% within the same timeframe

The insurance landscape in India has seen significant changes in recent years with the adoption of new technology. As one of the major insurance providers in the country, ICICI Prudential Life Insurance has aimed to lead in this transformation journey. “There has been a data explosion across India over the past few years, together with a high mobile penetration rate. Today, about 60% of our customers approach us via mobile, for example, which was certainly not the case before,” says Alpesh Karnik, SVP, IT, at ICICI Life Insurance.

Consumer expectations have also evolved, with easier access to information and online services. “Consumers today are more informed on the importance of investing in insurance products, so there’s much more of a pull factor when it comes to sales, but they also want to be able to get these products quickly and easily,” adds Alpesh. To meet the demands of these consumers, ICICI Prudential Life Insurance realized it needed to make its processes even faster and more efficient. Looking to upgrade its infrastructure, the company turned to Google Cloud.

“The biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

Serving customers better by speeding up processes with Google Cloud

ICICI Prudential Life Insurance’s distributors were already using tablets to input customer data faster and more efficiently, but many of the company’s solutions still required a team at the back end to manually sift through documents for approval. This meant that customers needed to wait five or six hours, or sometimes until the next working day, to know if their documents were approved or needed revision.

That all changed after partnering with Google Cloud Premier Partner Searce to take advantage of its AI/ML powered automated document processing product Recognic, which is built on Google Cloud. Developed using the optical character recognition (OCR) capabilities of Cloud Vision, Recognic reads, understands, and validates documents at scale, enabling organizations that handle massive amounts of paperwork to digitize these documents and then accurately store and index them.

“Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

“In the case of ICICI Prudential, the biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others,” Alpesh adds.

Alpesh explains that if the details on the application form match the documents provided, the case doesn’t need to go to the underwriter for further checks and can go directly to policy issuance. “Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”

ICICI Prudential Life Insurance is also working with Searce to build deep learning models into Recognic so that it can overcome template barriers and input data from a variety of forms. This is particularly helpful for financial and medical documents underwriting because unlike a passport or driving license, financial documents have a higher structural complexity.

As customer data becomes more important in the work of ICICI Prudential Life Insurance, so does protecting it, and the company is taking every measure to safeguard the security and privacy of its customers’ information. “Details of customers’ contactability are automatically removed by Google Cloud after processing is complete. This step in the workflow gives us the confidence that data is not stored at any level of the optical character recognition process,” says Alpesh.

Partnering with the right teams for dedicated support

In achieving the best solution for its business goals, ICICI Prudential Life Insurance recognizes the importance of its decision to work with partners that truly understand the insurance business. “There are many intricacies involved in this business, and it’s clear that both Google Cloud and Searce really took the time to understand our underwriting processes before coming up with a solution,” says Alpesh. He adds that during the implementation process, all findings were well documented and queries were responded to quickly.

“We didn’t want to take any shortcuts deploying Recognic, but at the same time, we didn’t want to draw out the implementation process. The excellent support from both Google Cloud and Searce throughout the journey was reassuring for us as they were always thinking ahead.”

Future-proofing the organization through machine learning and AI

In the coming years, Alpesh foresees the insurance industry to be even more agile than it is today. “I doubt elaborate processes such as underwriting or operations checks will need to be done manually in the future. Everything will be done through machine learning and AI.” In light of this, ICICI Prudential Life Insurance is doing everything it can to prepare, as customers’ expectations are set to keep evolving. “We have to be prepared for the future, and I believe that with Google Cloud, we can do it.”

About ICICI Prudential Life Insurance

ICICI Prudential Life Insurance aims to lead the Indian insurance field through quality products and a hassle-free claim settlement experience. A customer-centric company, it offers long-term savings and protection plans to meet customers’ needs at every stage of life.Industries: Financial Services & InsuranceLocation: India

About Searce

Searce is a niche cloud consulting business with futuristic tech in its DNA, focused on “realizing the Next in the Now” for its clients. Specializing in cloud data engineering, AI/ML, and ad

At Arpeely, we’ve developed some of the world’s most advanced advertising technology. Our machine learning (ML) media acquisition platform and “win-win” business model enables customers to bring highly intentful users to their offerings with precision, peace of mind and minimal overhead.

Real-time bidding is a dynamic and intricate process that involves buying and selling ad impressions in milliseconds. Each time a user opens an app or website, advertisers or ad-tech companies acting on behalf of advertisers have milliseconds to bid on ad spaces in real-time auctions, and the highest bidder wins the opportunity to display their ad. This is where Arpeely comes in, leveraging advanced algorithms, innovative UX and funnels to optimize the bidding process and maximize performance for advertisers.

At Arpeely, we use various signals often not used by traditional competitors to outperform the market and zero-down on high-intent and soon-to-be loyal users. For example, in advertising a mobile app, we predict, based on real-time conditions and user context, the user’s likelihood to make an in-app purchase many weeks into the future.

As for the ads themselves, gone are the days of simple banners; today’s ads are “mini products” that captivate and engage users. We employ a wide range of ad formats that go beyond industry standards. Our ads can be immersive videos, compelling messages, interactive experiences like mini-games or mini-apps, or even a multi-step mixture of all of the above. By integrating logic and interactivity, Arpeely enables users to engage with the ad content seamlessly and gauge user intent without leaving their main activity.

Our business model dictates that we don’t get paid if our advertiser doesn’t get paid. We like to say that our algorithms, like water, can trickle into hidden market opportunities missed by the rest of the industry that uses less granular tools. These and other capabilities make Arpeely a strategic partner in the challenging space of media and user acquisition.

Innovation at the edges of data science and engineering

Today, we handle millions of impressions per second and over a billion ML predictions daily. We are directly connected to seven of the world’s largest real-time bidding exchanges, including Google AdX. We also work very closely with our clients, ranging from prominent startups to companies in the S&P Top 20.

Daily, we tackle complex engineering and data challenges on multiple fronts. On the engineering side, we ensure that every real-time auction receives a lightning-fast response within a strict 150ms timeframe. On the data side, we fire multiple ML predictions per auction and ingest TBs of data daily. On the user-serving front, we serve A/B-tested assets across a long tail of geos and devices, with even the slightest fluctuations in load speeds affecting business dramatically.

Right from the start, we knew we couldn’t do it alone when it came to building our technology stack. When you look at available platforms, it’s clear Google Cloud has a robust architecture that is easy to manage, use and scale, especially for our use cases. They also have reliability and feature completeness which are critical in our line of business.

Under the hood

Building upon Google Kubernetes Engine (GKE), we run multiple services that handle our main bidding flows. We utilize Golang for services that run at a large scale and Python for when we prioritize development speed, community and readability. All of these can reach an immensely high scale, which is managed and monitored automatically in GKE. Communication between these services and our Redis (our Google Cloud partner) cluster happens in sub-millisecond latency over Google Cloud’s strong network infrastructure and enables us to run complex real-time logic for every impression.

Once we have tackled the actual bidding, we are left with the challenge of streaming our data into BigQuery for analytics and model training. We utilize a mix of Cloud Pub/Sub, Memorystore and Cloud Storage to create a mechanism capable of ingesting many TBs per day in near real-time without compromising on cost. Real-time data is critical for a company like Arpeely to test and reiterate at a fast pace.

BigQuery is our data warehouse for operational analysis and is an essential part of our business. We use it both as a warehouse, for large-scale computations and in an operational capacity that closes the loop between production, data, and ML retraining. A team of two or three people can manage petabytes at scale with minimal maintenance.

On top of these, we’ve built a state-of-the-art in-house model pipeline suited specifically for ad-tech industry purposes. It allows us to effectively deploy complex solutions — on-the-fly calibration, flexible conversion steps, sampling of heavily imbalanced data sets, adjusting weights, and A/B-tested model deployment and more.

Google Cloud also offers us an entry point for several very useful built-in products that have become deeply embedded in our daily stack and routine. Among them are Operations Suite (formerly Stackdriver), Cloud Profiler, Cloud Storage, Cloud CDN, Cloud SQL, Memorystore, Cloud Scheduler, Error Reporting, and more.

In addition to all the technology, there’s also a human touch. Google’s skilled Customer Success team possesses a unique blend of technical expertise and business acumen, acting as strategic advertisers and opening doors we did not know existed.

Opening the door to the future of advertising

Standardizing on Google Cloud enables us to focus our resources on innovation and growth. Instead of having to research business solutions and invest time integrating disparate technologies, we can tap into a wide range of Google Cloud tools as needed. Thus, it is crucial that we set up a good technological foundation and prepare for future growth of the business.

Google Cloud enables us to focus our resources on innovation rather than our infrastructure. This means we can put more effort into finding ways to match clients with high-value customers and grow their revenues. Even though online advertising has been around for over 20 years and pioneered by Google itself, Google Cloud gives us the impetus to disrupt the market and deliver greater levels of value to our customers today and in the future. 

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Check out the Redis listing on Google Cloud Marketplace. Please give it a try and let us know what you think!