Supply Chain & Logistics

Waze keeps traffic flowing with 1M+ real-time reads per second on Memorystore

Fri, 14 Nov 2025 17:00:00 +0000

Editor’s note: Waze (a division of Google parent company Alphabet) depends on vast volumes of dynamic, real-time user session data to power its core navigation features, but scaling that data to support concurrent users worldwide required a new approach. Their team built a centralized Session Server backed by Memorystore for Redis Cluster, a fully managed service with 99.99% availability that supports partial updates and easily scales to Waze’s use case of over 1 million MGET commands per second with ~1ms latency. This architecture is the foundation for Waze’s continued backend modernization.

Real-time data drives the Waze app experience. Our turn-by-turn guidance, accident rerouting, and driver alerts depend on up-to-the-millisecond accuracy. But keeping that experience seamless for millions of concurrent sessions requires robust and battle hardened infrastructure that is built to manage a massive stream of user session data. This includes active navigation routes, user location, and driver reports that can appear and evolve within seconds.

Behind the scenes, user sessions are large, complex objects that update frequently and contribute to an extremely high volume of read and write operations. Session data was once locked in a monolithic service, tightly coupled to a single backend instance. That made it hard to scale and blocked other microservices from accessing the real-time session state. To modernize, we needed a shared, low-latency solution that could handle these sessions in real time and at global scale. Memorystore for Redis Cluster made that possible.

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Choosing the right route

As we planned the move to a microservices-based backend, we evaluated our options, including Redis Enterprise Cloud, a self-managed Redis cluster, or continuing with our existing Memcached via Memorystore deployment. In the legacy setup, Memcached stored session data behind the monolithic Realtime (RT) server, but it lacked the replication, advanced data types, and partial update capabilities we wanted. We knew Redis had the right capabilities, but managing it ourselves or through a third-party provider would add operational overhead.

Memorystore for Redis Cluster offered the best of both worlds. It’s a fully managed service from Google Cloud with the performance, scalability, and resilience to meet Waze’s real-time demands. It delivers a 99.99% SLA and a clustered architecture for horizontal scaling. With the database decision made, we planned a careful migration from Memcached to Memorystore for Redis using a dual-write approach. For a period, both systems were updated in parallel until data parity was confirmed. Then we cut over to Redis with zero downtime.

Waze’s new data engine

From there, we built a centralized Session Server – our new command center for active user sessions – as a wrapper around Memorystore for Redis Cluster. This service became the single source of truth for all active user sessions, replacing the tight coupling between session data and the monolithic RT server. The Session Server exposes simple gRPC APIs, allowing any backend microservice to read from or write to the session state directly, including RT during the migration. This eliminated the need for client affinity, freed us from routing all session traffic through a single service, and made session data accessible across the platform.

We designed the system for resilience and scale from the ground up. Redis clustering and sharding remove single points of contention, letting us scale horizontally as demand grows. Built-in replication and automatic failover are designed to keep sessions online. While node replacements may briefly increase failure rates and latency for a short period, sessions are designed to stay online, allowing the navigation experience to quickly stabilize. And with support for direct gRPC calls from the mobile client to any backend service, we can use more flexible design patterns while shaving precious milliseconds off the real-time path.

Fewer pit stops, faster rides

Moving from Memcached’s 99.9% SLA to Memorystore for Redis Cluster’s 99.99% means higher availability and resiliency from the service. Load testing proved the new architecture can sustain full production traffic, comfortably handling bursts of up to 1 million MGET commands per second with a stable sub-millisecond service latency.

Because Memorystore for Redis supports partial updates, we can change individual fields within a session object rather than rewriting the entire record. That reduces network traffic, speeds up write performance, and makes the system more efficient overall – especially important when sessions can grow to many megabytes in size. These efficiencies translate directly into giving our engineering teams more time to focus on application-level performance and new feature development.

Session data in Memorystore for Redis Cluster is now integral to Waze’s core features, from evaluating configurations to triggering real-time updates for drivers. It supports today’s demands and is built to handle what’s ahead.

The road ahead

By proving Memorystore for Redis Cluster in one of Waze’s most critical paths, we’ve built the confidence to use it in other high-throughput caching scenarios across the platform. The centralized Session Server and clustered Redis architecture are now standard building blocks in our backend, which we can apply to new services without starting from scratch.

With that initial critical path complete, our next major focus is the migration of all remaining legacy session management from our RT server. This work will ultimately give every microservice independent access to update session data. Looking ahead, we're also focused on scaling Memorystore for Redis Cluster to meet future user growth and fine-tuning it for both cost and performance.

Learn more

Waze’s story showcases the power and flexibility of Memorystore for Redis Cluster, a fully managed service with 99.99% availability for high-scale, real-time workloads.

Learn more about the power of Memorystore and get started for free.
See Memorystore for Redis Cluster product documentation.

Where’s the beef? For São Paulo’s agricultural secretariat, it’s on Cloud SQL for SQL Server

Fri, 14 Feb 2025 17:00:00 +0000

When most people think of São Paulo, business and culture usually come to mind, not beef and chicken. But the state of São Paulo isn’t only home to the largest city in the hemisphere — it’s also the second largest producer of meat in a country that’s the second largest agricultural exporter in the world. Given the importance of agribusiness to Brazil’s economy, the Secretariat of Agriculture and Supply of the State of São Paulo (SAA-SP) plays a fundamental role in the development of agribusiness across the region and, by extension, the country.

With the mission of promoting the sustainable production of food, fibers and bioenergy, SAA-SP offers support to rural producers in several areas, such as technical assistance, research, agricultural defense, and access to markets. The Secretariat is also responsible for ensuring food security for the population, monitoring the quality of agricultural products and promoting nutritional education actions.

As the world’s food systems have evolved and grown more complex, organizations have looked to technology to help meet the goals for food security and sustainability. In the case of SAA-SP, the secretariat needs to securely manage increasing amounts of confidential data and ensure its critical systems are available 24/7. These systems include the Rural Environmental Registry (a mandatory electronic registry for all rural properties), and GEDAVE (a management system for animal and plant monitoring).

To give a sense of just how complex the system is, in one example, GEDAVE would handle controls for the management of poultry production, whereby each chick, after birth, needs to be transferred to a new location within 24 hours — and the entire process must be rigorously documented to ensure food safety.

If it wanted to meet the needs not only of its aging IT infrastructure but also the needs of a growing global population in need of safe, reliable food sources, the SAA-SP knew it was time to modernize some of our most important systems.

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The growing pains:

Our team in the Department of Systems Management sits within the Information Technology Coordination organization of the SAA-SP. We’re in charge of operating GEDAVE, which is a crucial system for SAA-SP that’s responsible for controlling and monitoring animal and plant health throughout the state of São Paulo.

GEDAVE records and manages data on animal movement, plant production, use of pesticides, vaccination, pest and disease control, among other information relevant to São Paulo's agriculture. GEDAVE assists in issuing documents such as the Animal Transit Guide (GTA) and the Phytosanitary Certificate of Origin (CFO), which are essential for the trade of agricultural products.

GEDAVE's back-end was developed in Java and connected to a SQL Server database. It contains sensitive information about rural producers, such as production data, management strategies, and financial information. Previously, this database was hosted on-premises, which caused a series of issues, including:

Difficulty in keeping the database up to date: Applying patches and security updates in the on-premises environment required time and planning, resulting in periods of system unavailability, directly impacting producers who depend on SAA-SP services.
Complexity in performing regular backups: Ensuring data security with regular and reliable backups was a complex and laborious process in the on-premises environment.
Challenging high availability: Maintaining high availability of the on-premises database required investments in redundant and complex infrastructure, increasing management costs and complexity.

In addition, SAA-SP needed to ensure the 24/7 availability of these systems to help producers meet market demands, including such complex issues as quality control for export and monitoring internal production.

Data security was also crucial, as information on types of herds, vaccination strategies, pest control, among others, is highly sensitive and requires rigorous protection.

Sowing the seeds of innovation:

SAA-SP decided to modernize its data infrastructure to address these challenges, choosing Google Cloud. They felt the Google Cloud platform's high performance could ensure application availability and efficiency, while its ease of management would simplify database administration and allows the IT team to focus on other priorities.

As a first step in this modernization, SAA-SP migrated its SQL Server database to Cloud SQL for SQL Server on Google Cloud. A crucial factor in the choice was the ease of enabling high availability (HA) in Cloud SQL for SQL Server. With just a few clicks, SAA-SP configured automatic database replication and failover, ensuring service continuity in the event of failures and compliance with SLAs, without the need for complex configurations. In addition, the migration to Cloud SQL for SQL Server was carried out quickly and easily, minimizing the impact on SAA-SP's operations.

This strategic change brought a series of benefits, allowing Java applications to connect to a more modern, scalable and secure database environment.

Harvesting success:

Simplified updates: Cloud SQL for SQL Server makes it easier to apply patches and updates, minimizing downtime and ensuring that systems are always protected with the latest versions of the software.
Automated backups: The service offers automated and managed backups, ensuring data security and recovery in the event of failures.
Simplified high availability: The simplified configuration of high availability in Cloud SQL for SQL Server reduced the effort of the IT team and ensured compliance with service SLAs.
Enhanced security: With data encryption at rest and in transit, Cloud SQL for SQL Server protects SAA-SP’s confidential information from unauthorized access.
On-demand scalability: SAA-SP can adjust Cloud SQL for SQL Server resources according to demand, ensuring optimal performance of Java applications, even during peak periods.
Focus on innovation: SAA-SP’s IT team can now focus on strategic projects, such as developing new features for Java applications, instead of worrying about managing the data infrastructure.
Reduced IT costs: The migration to Cloud SQL for SQL Server eliminated the need to invest in hardware and software to maintain the on-premises database, reducing operational costs.

Cultivating a future of innovation in agriculture with Cloud SQL for SQL Server:

The migration to Cloud SQL for SQL Server was a strategic decision that allowed SAA-SP to overcome the challenges of on-premises data management and ensure the availability, security, and scalability of its critical systems. The ease of enabling high availability and the simplicity of the migration were determining factors for the success of the project.

But more than that, Cloud SQL enabled innovation at SAA-SP, opening doors to integration with generative AI for more assertive and efficient analysis and decision-making. For example, SAA-SP is leveraging the power of Gemini with Looker to provide C-level executives with real-time data insights hosted on Cloud SQL, facilitating data-driven decisions.

Furthermore, SAA-SP is empowering its customers with Gemini Database, allowing them to harness AI to enhance database performance and maintenance.

SAA-SP plans to continue modernizing its infrastructure and services, undertaking:

Migration to microservices: Launch an updated version of the microservices-based application to increase the flexibility, scalability and capacity of the system.
Data analysis with generative AI: Enable the use of generative AI to perform predictive analysis and obtain real-time insights from Cloud SQL for SQL Server data, assisting in strategic decision-making for the agricultural sector.
Data management with Gemini: Use Gemini to facilitate data management and analysis, extracting relevant information and simplifying access to complex data.

SAA-SP’s move towards intelligent management of operations, coupled with the advancements in analysis, has consolidated SAA-SP's position as a reference in technology and innovation in the agricultural sector, driving the development of agribusiness across São Paulo and serving as a beacon for those around the world.

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How Prewave is helping to secure deep supply chains worldwide with AI on Google Cloud

Thu, 25 Apr 2024 08:00:00 +0000

Most businesses know that taking responsibility for their environmental and social impact is key for long-term success. But how can they make fully-informed decisions when most companies only have visibility into their immediate suppliers? At Prewave, we’re driven by the mission to help companies make their entire supply chains more resilient, transparent, and sustainable.

Our end-to-end platform monitors and predicts a wide range of supply chain risks, and AI is the driving-force behind its success. Without AI, handling vast volumes of data and extracting meaningful insights from publicly-available information would be almost unfathomable at the scale that we do to help our clients. Because of that, Prewave needs a rock-solid technology foundation that is reliable, secure, and highly scalable to continually handle this demand. That’s why we built the Prewave supply chain risk intelligence platform on Google Cloud from inception in 2019.

Back then, as a small team, we didn’t want to have to maintain hardware or infrastructure, and Google Cloud managed services stood out for providing reliability, availability, and security while freeing us up to develop our product and focus on Prewave’s mission. A shared concern for sustainability also influenced our decision, and we’re proud to be working with data centers with such a low carbon footprint.

Tracking hundreds of thousands of suppliers

Prewave’s end-to-end platform solves two key challenges for customers: First, it makes supply chains more resilient by identifying description risks and developing the appropriate mitigation plans. And second, it makes supply chains more sustainable by detecting and solving ESG risks, such as forced labor or environmental issues.

It all starts with our Risk Monitoring capability, which uses AI that was developed by our co-founder Lisa in 2012 during her PhD research. With it, we’re scanning publicly available information in 120+ languages, looking for insights that can indicate early signals of Risk Events for our clients, such as labor unrest, an accident, fire, or 140 other different risk types that can disrupt their supply chain. Based on the resulting insights, clients can take actions on our platform to mitigate the risk, from filing an incident review to arranging an on-site audit.

With this information, Prewave also maps our clients’ supply chains from immediate and sub-tier suppliers down to the raw materials’ providers. Having this level of granularity and transparency is now a requirement of new regulations such as the European corporate sustainability due diligence directive (CSDDD), but it can be challenging for our clients to do without help. They usually have hundreds or thousands of suppliers and our platform helps them to know each one, but also to focus attention, when needed, on those with the highest risk.

The Prewave platform keeps effort on the supplier’s side as light as possible. They only have to act if potential risk is flagged by our Tier-N Monitoring capability, in which case, we support them to fix issues and raise their standards. Additionally, this level of visibility frees them up from having to manually answer hundreds of questionnaires in order to qualify to do business with more partners.

To make all this possible, our engineering teams rely heavily on scalable technology such as Google Kubernetes Engine (GKE) to support our SaaS. We recently switched from Standard to Autopilot and noticed great results in time efficiency now that we don’t need to ensure that node pools are in place or that all CPU power available is being used appropriately, helping save up to 30% of resources. This also has helped us to reduce costs because we only pay for the deployments we run.

We also believe that having the best tools in place is key to delivering the best experience not only to customers but also to our internal teams. So we use Cloud Build and Artifact Registry to experiment, build, and deploy artifacts and manage docker containers that we also use for GKE. Meanwhile, Cloud Armor acts as a firewall protecting us against denial of service and web attacks.

Because scalability is key for our purposes, the application development and data science teams use Cloud SQL as a database. This is a fully managed service that helps us focus on developing our product, since we don’t have to worry about managing the servers according to demand. Data science teams also use Compute Engine to host our AI implementations as we develop and maintain our own models, and these systems are at the core of Prewave’s daily work.

Helping more businesses improve their deep supply chains

Since 2020, Prewave has grown from three clients to more than 170, our team of 10 grew to more than 160, and the company’s revenue growth multiplied by 100, achieving a significant milestone. We’ve also since then released many new features to our platform that required us to scale the product alongside scaling the company. With Google Cloud, this wasn’t an issue. We simply extended the resources that the new implementations needed, helping us to gain more visibility at the right time and win new customers. Because our foundation is highly stable and scalable, growing our business has been a smooth ride.

Next, Prewave is continuing its expansion plans into Europe that began in 2023, before moving to new markets, such as the US. This is going well and our association with Google Cloud is helping us win the trust of early-stage clients who clearly also trust in its reliability and security. We’re confident that our collaboration with Google Cloud will continue to bring us huge benefits as we help more companies internationally to achieve transparency, resilience, sustainability, and legal compliance along their deep supply chains.

Ninja Van: delivering flexibility, stability and scalability to core applications with a cloud container platform

Mon, 22 Apr 2024 16:00:00 +0000

At Ninja Van, running applications in a stable, scalable environment is business-critical. We are a fast-growing tech-enabled express logistics business with operations across South East Asia. In the past 12 months, we’ve partnered with close to two million businesses to deliver around two million parcels daily to customers, with the help of more than 40,000 workers and employees.

We are an established customer of Google Cloud, and continue to work closely with them to expand into new products and markets, including supply chain management and last mile courier services.

Deploying a secure, stable, container platform

To run the applications and microservices architecture that enable our core businesses, we opted to deploy a secure and stable container platform. We had been early adopters of containerization. We were initially running our container workload in CoreOS with a custom scheduler, Fleet. As we monitor the activities in the open source community, it was evident Kubernetes was gaining more traction and is becoming more and more popular. We decided to run our own Kubernetes cluster and API server, later deploying a number of Compute Engine virtual machines, consisting of both control plane and worker nodes.

As we dived deeper into Kubernetes, we realized that a lot of what we did was already baked into its core functionalities, such as service discovery. This feature enables applications and microservices to communicate with each other without the need to know where the container is deployed to among the worker nodes. We felt that if we continued maintaining our discovery system, we would just be reinventing the wheel. Thus, we dropped what we had in favor of this Kubernetes core feature.

We also found the opportunity to engage with and contribute to the open source community working on Kubernetes compelling. With Kubernetes being open standards-based, we gained the freedom and flexibility to adapt our technology stack to our needs.

However, we found upgrading a self-managed Kubernetes challenging and troublesome in the early days and decided to move to a fully managed Kubernetes service. Among other benefits, we could upgrade Kubernetes easily through Google Kubernetes Engine (GKE) user interface or Google Cloud command line tool. We could also enable auto upgrades simply by specifying an appropriate release channel.

Simplifying the operation of multiple GKE clusters

With a technology stack based on open-source technologies, we have simplified the operation of multiple clusters in GKE. For monitoring and logging operations, we have moved to a centralized architecture to colocate the technology stack on a single management GKE cluster. We use Elasticsearch, Fluentbit and Kibana for logging and Thanos, Prometheus and Grafana for monitoring. When we first started, logging and monitoring were distributed across individual clusters, which meant that administrators had to access the clusters separately which led to a lot of operational inefficiencies. This also meant maintaining duplicated charts across different instances of Kibana and Grafana.

For CI/CD, we run a dedicated DevOps GKE cluster used for hosting developer toolsets and running the pipelines. We embrace the Atlassian suite of services, such as JIRA, Confluence, Bitbucket, Bamboo to name a few. These applications are hosted in the same DevOps GKE cluster.

Our CI/CD is centralized, but custom steps can be put in, giving some autonomy to teams. When a team pushes out new versions of codes, our CI pipeline undertakes the test and build processes. This then produces container image artifacts for backend services and minified artifacts for frontend websites. Typically, the pipelines are parameterized to push out and test the codes in development and testing environments, and subsequently deploy them into sandbox and production. Depending on their requirements, application teams have the flexibility to opt for a fully automated pipeline or a semi-automated one, that requires manual approval for deployment to production.

Autoscaling also comes into play during runtime. As more requests come in, we need to scale up to more containers, but shrink down automatically as the number of requests decreases. To support autoscaling based on our metrics, we integrate KEDA to our technology stack.

Delivering a seamless development experience

In a sense, our developers are our customers as well as our colleagues. We aim to provide a frictionless experience for them by automating the testing, deployment, management and operation of application services and infrastructure. By doing this, we allow them to focus on building the application they’re assigned.

To do this, we’re using DevOps pipelines to automate and simplify infrastructure provisioning and software testing and release cycles. Teams can also self-serve and spin up GKE clusters with the latest environment builds mirroring production with non-production sample data preloaded, which gives them a realistic environment to test the latest fixes and releases.

As a build proceeds to deployment, they can visit a Bamboo CI/CD Console to track progress.

Code quality is critical to our development process. An engineering excellence sub-vertical within our business monitors code quality through part of our CI/CD using the SonarQube open-source code inspection tool. We set stringent unit test coverage percentage requirements and do not allow anything into our environment that fails unit or integration testing.

We release almost once a day excluding code freezes on days like Black Friday or Cyber Monday, when we only release bug fixes or features that need to be deployed urgently during demand peaks. While we’ve not changed our release schedule with GKE, we’ve been able to undertake concurrent builds in parallel environments, enabling us to effectively manage releases across our 200-microservice architecture.

Latency key to Google Cloud and GKE selection

When we moved from other cloud providers to Google Cloud and GKE, the extremely low latency between Google Cloud data centers, in combination with reliability, scalability and competitive pricing, gave us confidence Google Cloud was the right choice. In a scenario with a lot of people using the website, we can provide a fast response time and a better customer experience.

In addition, Google Cloud makes the patching and upgrading of multiple GKE clusters a breeze by automating the upgrade process and proactively informing us when an automated upgrade or change threatens to break one of our APIs.

Google Cloud and GKE also open up a range of technical opportunities for our business, including simplification. Many of our services use persistent storage, and GKE provides a simple way to automatically deploy and manage them through their Container Storage Interface (CSI) driver. The CSI driver enables native volume snapshots and in conjunction with Kubernetes CronJob, where we can easily take automated backups of the disks running services such as TiDB, MySQL, Elasticsearch and Kafka. On the development front, we are also exploring Skaffold, which opens up possibilities for development teams to improve their inner development loop cycle and develop more effectively as if their local running instance is deployed within a Kubernetes cluster.

Overall, the ease of management of GKE means we can manage our 200-microservice architecture in 15 clusters with just 10 engineers even as the business continues to grow.

If you want to try this out yourself, check out a hands-on tutorial where you will set up a continuous delivery pipeline in GKE that automatically rebuilds, retests, and redeploys changes to a sample application.

How supply chain organizations can use data and AI to drive efficiency and optimization

Thu, 31 Aug 2023 16:00:00 +0000

Data and AI are transforming every industry, and so is the supply chain that powers many of them. In recent years, supply chains have increased in visibility and impact for both businesses and consumers. At the same time, the challenges of adhering to sustainability expectations, while balancing geopolitical and financial challenges, have made it harder than ever to run efficient and sustainable supply chains. At Google Cloud, we’ve delivered great results to our supply chain customers through data and AI, allowing them to better understand their supply chains, identify areas for improvement, reach their sustainability goals, and make more informed decisions.

Today, we’re proud to bring our expertise to more customers with Data and AI Cloud for Supply Chain. Together with our partners and customers, we’ve gained a deep understanding across many industries, and our customers have gotten huge value from building data-driven supply chains with our technology and value accelerators. With Data and AI Cloud for Supply Chain, you can enjoy the following benefits:

Exceptional customer experiences by using data and AI to gain a deeper understanding, increase efficiency and resilience of your supply chain. Accurately forecast when products are needed and how to get them there in the most efficient way.
Reduced costs and carbon footprint: Identifying bottlenecks, inefficiencies, and risks through improved visibility has enabled organizations to free up resources, automate where possible, and increase efficiency. This has not only been clear through reduced costs, but also through improved customer and employee satisfaction.
A platform to accelerate deployment of AI-enabled use cases: Be ready to leverage the latest generative AI technology for intercompany search, for example, for insights into the far ends of the supply chain through a simple question; or to serve up personalized experiences to your customers based on real-time inventory availability.

Delivering value to our customers

Innovative companies have been using Google’s Data and AI Cloud for many years now, achieving incredible results.

Coop reduces food waste by forecasting with Google’s Data and AI technologies, resulting in a 43% performance improvement over the purpose build, in-house solution.
Renault improves its supply chain visibility, reduces cost of inventory, understands end-of-life of stock components, lower costs, and manages logistics more effectively, in turn improving customer satisfaction.

Google’s Data and AI Cloud for Supply Chain capabilities

Google’s Data and AI Cloud for Supply Chain includes a variety of solutions and value accelerators that help businesses optimize their supply chains. Some of the key solutions and value accelerators include:

AI-optimized planning and forecasting: Google Cloud’s products and our partners provide businesses the ability to use machine learning to help optimize their planning, forecasting processes and on-shelf availability. This can help them reduce waste, improve customer service, and increase profits.
Vendor performance visibility: This solution helps businesses track the performance of their vendors. This information can be used to identify and address potential problems, such as late deliveries or poor-quality products.
Cross-company data sharing: Businesses can more easily share data across their supply chain with partners, vendors and customers, improving collaboration and coordination, and helping them make more informed decisions. By connecting to various data sources from our wide ecosystem of providers and securely sharing data with trading partners, businesses can improve collaboration and coordination and make more informed decisions.
Sophisticated partner ecosystem: We partner with an assortment of Independent Software Vendors ( ISVs) in supply chain and sustainability. ISVs such as Kinaxis, Dematic, Atlas AI, ESG Book, NGIS, Project44 and Geotab integrate into Google Cloud’s Data and AI capabilities and support critical workflows for our customers. Organizations can further accelerate their development journey by tapping into Google Cloud's latest Industry Value Networks, which brings accelerators and reusable IP / assets with select partners to solve several high-value use cases across industries.

Augment and accelerate your supply chain opportunities

Google’s Data and AI Cloud for Supply Chain is a powerful suite of solutions, value accelerators, and partner offerings that help businesses of all sizes optimize their supply chains using data and AI. To learn more, tune into our session at Google Cloud Next or contact us here.

Coop reduces food waste by forecasting with Google’s AI and Data Cloud

Thu, 16 Mar 2023 16:00:00 +0000

Although Coop has a rich history spanning nearly 160 years, the machine learning (ML) team supporting its modern operations is quite young. Its story began in 2018 with one simple mission: to leverage ML-powered forecasting to help inform business decisions, such as demand planning based on supply chain seasonality and expected customer demand. The end goal? By having insight into not only current data but also projections of what could happen in the future, the business can optimize operations to keep customers happy, save costs, and support its sustainability goals (more on that later!).

Coop’s initial forecasting environment was one on-premises workstation that leveraged open-source frameworks such as PyTorch and TensorFlow. Fine tuning and scaling models to a larger number of CPUs or GPUs was cumbersome. In other words, the infrastructure couldn’t keep up with their ideas.

So when the question arose of how to solve these challenges and operationalize the produced outcomes beyond those local machines, Coop leveraged the company’s wider migration to Google Cloud to find a solution that could stand the test of time.

Setting up new grounds for innovation

Over a two-day workshop with the Google Cloud team, Coop kicked things off by ingesting data from its vast data pipelines and SAP systems to BigQuery. At the same time, Coop’s ML team implemented physical accumulation cues of incoming new information and sorted out what kind of information this was. The team was relieved to not have to worry about setting up infrastructure and new instances.

Next, the Coop team turned to Vertex AI Workbench to further develop its data science workflow, finding it surprisingly fast to get started. The goal was to train forecasting models to support Coop’s distribution centers so they could optimize their stock of fresh produce based on accurate numbers.

Achieving higher accuracy, faster, to better meet customer demand

During the proof-of-concept (POC) phase, Coop’s ML team had two custom-built models competing against an AutoML-powered Vertex AI Forecast model, which the team ultimately operationalized on Vertex AI: a single Extreme Gradient Boosting model and a Temporal Fusion Transformer in PyTorch. The team established that using Vertex AI Forecast was faster and more accurate than training a model manually on a custom virtual machine (VM).

On the test set in the POC, the team reached 14.5 WAPE (Weighted Average Percentage Error), which means Vertex AI Forecast provided a 43% performance improvement relative to models trained in-house on a custom VM.

After a successful POC and several internal tests, Coop is building a small-scale pilot (to be put live in production for one distribution center) that will conclude with the Coop ML team eventually streaming back the forecasting insights to SAP, where processes such as carrying out orders to importers and distributors take place. Upon successful completion and evaluation of the small-scale pilot in production in the next few months, they could possibly scale it out to full blown production across distribution centers throughout Switzerland. The architecture diagram below approximately illustrates the steps involved in both stages. The vision is of course to leverage Google’s data and AI services, including forecasting and post-forecasting optimization, to support all of Coop’s distribution centers in Switzerland in the near future

Leveraging Google Cloud to increase the relative forecasting accuracy by 43% over custom models trained by the Coop team can significantly affect the retailer’s supply chain. By taking this POC to pilot and possibly production, the Coop ML team hopes to improve its forecasting model to better support wider company goals, such as reducing food waste.

Driving sustainability by reducing food waste

Coop believes that sustainability must be a key component of its business activity. With the aim to become a zero-waste company, its sustainability strategy feeds into all corporate divisions, from how it selects suppliers of organic, animal-friendly, and fair-trade products to efforts for reducing energy, CO2 emissions, waste materials, and water usage in its supply chains.

Achieving these goals boils down to an optimal control problem. This is known as a Bayesian framework: Coop must carry out quantile inference to determine the scope of its distributions. For example, is it expecting to sell between 35 and 40 tomatoes on a given day, or is its confidence interval between 20 and 400? Reducing this amount of uncertainty with more specific and accurate numbers means Coop can order the precise number of units for distribution centers, ensuring customers can always find the products they need. At the same time, it prevents ordering in excess, which reduces food waste.

Pushing the envelope of what can be achieved company-wide

Having challenged its in-house models against the Vertex AI Forecast model in the POC, Coop is in the process of rolling out a production pilot to one distribution center in the coming months, and possibly all distribution centers across Switzerland later thereafter. In the process, one of the most rewarding things was realizing that the ML team behind the project could use different Google Cloud tools, such as Google Kubernetes Engine and BigQuery, and Vertex AI to create its own ML platform. Beyond using pre-trained Vertex AI models, the team can automate and create data science workflows quickly so it’s not always dependent on infrastructure teams.

Next, Coop’s ML team aims to use BigQuery as a pre-stage for Vertex AI. This will allow the entire data streaming process to flow more efficiently, serving data to any part of Vertex AI when needed. “The two tools integrate seamlessly, so we look forward to trying that combination for our forecasting use cases and potentially new use cases, too. We are also exploring the possibility of deploying different types of natural language processing-based solutions to other data science departments within Coop that are relying heavily on TensorFlow models,” says Martin Mendelin, Head of AI/ML Analytics, Coop.

“By creating and customizing our own ML platform on Google Cloud, we’re creating a standard for other teams to follow, with the flexibility to work with open-source programs but in a stable, reliable environment where their ingenuity can flourish,” Mendelin adds. “The Google team went above and beyond with its expertise and customer focus to help us make this a reality. We’re confident that this will be a nice differentiator for our business.”

Harness the power of data and AI in your life science supply chain

Tue, 13 Dec 2022 17:00:00 +0000

Global life science supply chains are lengthy and complex with many moving parts. One small disruption can create serious delays and affect your ability to deliver therapeutics for patients.

Supply chain disruptors

Over the last few years, healthcare organizations have encountered a range of obstacles, from both internal and external factors, that have resulted in supply networks failing to get drugs and medical devices to where they need to be on time. These obstacles include:

Labor and supply shortages
Rising material costs
Raw material constraints
Geo-political events
Unpredictable weather

How do you overcome supply chain disruptors that are out of your control?

The intelligent healthcare supply chain

While many organizations have already implemented data-driven supply chains, organizations are still faced with the challenges of static, siloed, and different functional supply chain applications; limited data exchange with key trading partners across upstream and downstream operations; and the inability to effectively leverage relevant external data.

At Google Cloud, we believe the key to meaningful and effective change is a data-driven supply chain that allows you to achieve visibility, flexibility, and innovation.

Our solutions help you prepare for the unpredictable and enhance the value of your data. By unlocking AI-driven insights, you can strengthen distribution networks and optimize your workflows and supply chains to become more reliable, intelligent, and sustainable. Some of the business challenges we address include:

Predicting demand with Vertex AI Forecast
Visual inspection for quality and predictive maintenance with pre-built ML models
Automating and optimizing pickup and delivery operations with Cloud Fleet Routing API
Real time and holistic inventory visibility with Supply Chain Twin

Make sure you’re prepared for the unpredictable with real-time visibility over your distribution networks. Learn how you can harness the power of AI and analytics and gain actionable insights that enhance your supply chain.

Beating inflation: 5 tips for retailers heading into the holidays

Fri, 18 Nov 2022 17:00:00 +0000

Budget concerns and inflation shock are causing noticeable shifts in consumer behavior, a head-spinning turnabout since the pandemic days of 2020. We went from not having access to enough goods to now not having the right goods at the right price points. Heading into the holiday season, retailers will need to look sharp and be able to deliver value to the increasingly cost-conscious consumer — with frictionless product discovery and digital shopping experiences, relevant inventory, and personalization.

Those are a few of the insights gleaned from a recent eMarketer webcast on how retailers can better combat the effects of inflation, with pragmatic recommendations from experts including Amy Eschliman, managing director of retail solutions strategy and industry engagement at Google Cloud; Alexis Hoopes, vice president and head of e-commerce and direct-to-consumer (DTC) at Mattel Inc; and Elissa Quinby, head of retail insights at Quantum Metric. We’d like to share five key takeaways that retailers can adopt.

1. Be prepared for earlier holiday planning. Budget-conscious buyers are planning their purchases earlier than ever. According to our partner Quantum Metric’s latest Retail Benchmarks research, 40% of U.S. consumers and 30% in the UK have already started their holiday shopping¹. At Mattel, Alexis noted, “We’ve learned to be nimble and early for our consumers, and to have products available for them when they’re ready to shop.” With higher prices, she added, “Consumers are preplanning and researching more, viewing product detail pages multiple times to check a higher-price item before adding it to their basket.”

Quantum Metric’s same research also shows that while average cart values grew between January to July 2022 — larger than they were at the same time last year1 — consumers were shopping less frequently. Because of higher prices, 37% of shoppers are now pre-planning and purchasing items all at once to help keep to their budget1. Amy observed. “That means retailers need to make it super easy for customers to find what they want to avoid shopping-cart abandonment.”

Retailers who make it easier for shoppers to find the right products, by providing Google-quality search and recommendations, can help reduce cart abandonment and increase conversions.

2. Get a handle on out-of-stock issues. While many of the supply-chain issues that surfaced during the pandemic have cleared up, inventory continues to be a challenge in retail. During the 2021 holiday season, Google/Ipsos research shows that consumers saw a 253% increase in “out of stock” messages versus pre-pandemic². Elissa further pointed out that a great majority of both U.S. and UK consumers experience out-of-stock issues several times a month. It’s a fluid situation, however, with retailers facing bloated inventory as consumer demands quickly pivot from, for example, branded goods to generic or white-label items. “In the next six months or so, you really need to make sure you have the right inventory to meet consumer demands,” she advised.

The need for end-to-end visibility and the ability to act in real-time across the supply chain has never been more pressing. Google Cloud and our partners are tackling the top supply chain issues head on with solutions that enable end-to-end visibility, analytics and alert-driven event management as well as AI solutions and automation to streamline processes like procurement, fulfillment, and delivery.

3. Introduce value: communicate the quality of the product and the experience. While rising prices are paramount for many shoppers, it’s a common misconception that consumers are making purchase decisions based solely on price. Alexis remarked, “In e-commerce, we talk about price, product, service, and experience. Price is only one of the pillars for consumers. At Mattel, we are fully focused on product and experience. What makes this special? How do we connect directly in new ways to the consumer? Create ‘wow’ moments and deeper connections. When we think about value for our consumers, it’s the strength of the products that will drive the purchase.”

Elissa also noted that while consumers are doing a lot of comparison shopping, it’s really about value, whether a high-quality product or a high-quality experience. Quantum Metric’s Continuous Product Design solution, which is built with BigQuery, ingests data across multiple digital touchpoints, including from mobile and web applications, and connects customer signals to every stage of the product lifecycle to help deliver the products and experiences that customers actually want.

4. Ensure consistent experiences across channels. Today, consumers crave the cross-channel shopping experience. “The channel experience has gone from online to in-store to now everywhere,” Amy commented. “We call it ubiquitous digital shopping.” Elissa added that 75% of consumers do most of their shopping digitally. However, mobile drives 67% of digital traffic, but just 49% of sales.¹ “Recent trends in traffic and conversion rates by device show that we can expect the most traffic for the big sale days on mobile,” she said. “People are looking for discovery and awareness, maybe even adding items to the cart as a placeholder or reminder. But they prefer to complete a sale on a desktop. It will be critical for retailers to offer a consistent experience, especially on major sale days like Black Friday and Cyber Monday.” Elissa also advised wrapping up any experiments with product and site design early, making sure to understand the customer experience holistically across the organization and prioritizing efforts to eliminate friction.

Consumers now expect to be able to shop wherever and whenever best fits their needs, whether in a store, on your website, through your app, or from within a social media ad, and have it be a consistently good experience no matter how they first entered or exited your commerce site. Google’s 2022 Retail Marketing Guide provides useful insights and tips on how to grow your online and in-app sales.

5. Personalize touchpoints to build loyalty. Connecting with your customer base and making sure they understand the value of your offering is essential. Alexis noted that the key is to keep your messages fresh as the holiday shopping season expands, providing new messages as they keep coming back to your store. “We need to engage them by helping them find what they are looking for at the right time,” she said. “Recognize that they are doing more planning and wish-list building early, then buying last-minute gifts at lower prices towards the end. Make sure those are front and center.”

Alexis also pointed out that today, consumers are providing more data points with the products they view or the items sitting in their carts. “What's so great about e-commerce is that we can use all of this to create more personalized, direct messages targeted to those consumers,” she commented.

Amy recommended continuing to focus on conversion with product discovery and personalization, harnessing customer data to drive insights and action. “Any company’s biggest asset is their data. Using it in as many ways possible and activating it across the company is incredibly important,” she remarked. “Take advantage of your first-party data to activate everything from marketing campaigns to a more efficient supply chain. For every customer who comes to one of your digital properties, make sure your product discovery is as easy as possible. Pay attention to your recommendations, driving personalization to make that experience as unique and fruitful for the customer as possible.”

Achieving these objectives requires a modern cloud data warehouse and activation of a customer data platform. Retailers can explore how Google Cloud's advanced data capabilities and our ecosystem of partners can power a customer data platform that supports more personalized marketing, shopping experience, and customer service.

While these are our key takeaways, we invite you to register to watch the on-demand webinar, “5 Ways Retailers Can Combat the Effects of Inflation,” for even more insights.

^{1. Quantum Metric Retail Benchmarks, “Adjusting for Inflation.” The report is based on aggregated browsing behavior from January to July 2022, paired with a survey of 3,400 consumers in the U.S. and UK.
2. Google/Ipsos, Holiday Shopping Study, Oct 2021 – Jan 2022, Online survey, US, n=7,253, Americans 18+ who conducted holiday shopping activities in past two days}

Unlocking the power of connected vehicle data and advanced analytics with BigQuery

Fri, 28 Oct 2022 16:00:00 +0000

As software-defined vehicles continue to advance and the quantity of digital services grows to meet consumer demand, the data required to provide these services continue to grow as well. This makes automotive manufacturers and suppliers look for capabilities to log and analyze data, update applications, and extend commands to in-vehicle software.

The challenges the automotive sector faces can be quantified. A modern vehicle contains upwards of 70 electronic control units (ECUs), most of which are connected to one or more sensors. Not only is it now possible to exactly measure many aspects of vehicle performance, but new options become available. Using LIDAR (light detection and ranging), for example, vehicles are achieving higher levels of autonomy; this leads to a data stream from such demanding applications that may reach 25 GB per hour. For the in-vehicle processing of data, 100 million lines of software code may be present — more than a fighter jet. This in-vehicle code will have to be maintained with updates and new functionalities.

Access to the data will allow manufacturers to gain valuable insights into operational details of their vehicles. The use of this data can help to reduce costs and risks, increase ROI, support ESG initiatives, and provide valuable insights to develop innovative solutions and shorten the time to value for Electric Vehicle innovations.

Sibros’ Deep Connected Platform (DCP) makes it possible for these manufacturers to build and launch new connected vehicle use cases from production to post-sale at scale by connecting and managing all software and data throughout every life cycle stage. A key component of this platform is the Sibros Deep Logger that provides capabilities like the following:

Full configurability of what to record, when to record it, and how fast to record it.
High resolution timestamps of all Controller Area Network (CAN) messages.
Dynamic application of live log configurations to receive new data points without deploying new software.

For example, properly analyzed engine data enables true predictive maintenance for the first time, which creates the option to repair or replace components before failure happens. Another example would be the evaluation of data regarding the use of certain in-car features with the goal to redesign its interior.

Two other components of the DCP are software updates and remote commands to ECUs. The DCP on Google Cloud enables seamless integration with any vehicle architecture and provides OEMs and suppliers with the platform to manage connected vehicle data at rest and in transit using a proven and secure way on a global scale.

OEMs can pull data through APIs provided by Sibros into Google Data Cloud (including BigQuery) to gain access to the rich information data sets provided by the DCP within their environment and blend this data with their first party data sets to provide value insights for their business. Some of the Connected Vehicle insights that DCP information enables are:

Damage prevention, improved operation, or development of the next generation of engines with insights from complex analyses that could consider parameters like model, engine type, mileage, overall speed, temperature, air pressure, load, services, and more.
The combination of electric vehicle battery usage data like charging cycles, engine performance, and battery age with contributing factors as the use of the air conditioning to determine if such factors contribute to hazardous battery conditions and for improved battery development.
Cross-organization collaboration in R&D by the provision of information on all these metrics and more from real-world driving, like engine knock data and even tire pressure.

Google Cloud’s unified data cloud offering provides a complete platform for building data-driven applications like those from Sibros — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — integrated with Google Cloud. With a diverse partner ecosystem and support for multi-cloud, open-source tools and APIs, Google Cloud provides Sibros the portability and the extensibility they need to avoid data lock-in.

“Software has an ever increasing importance in the automotive world, even more so with electric vehicles and new mobility services. Google Cloud is partnering with Sibros to bring their award winning Deep Connected Platform to deliver high frequency, low latency over-the-air software updates, data logging & diagnostics capabilities to our automotive customers, leveraging the security and scale of Google Cloud. This is revolutionizing everything from development cycles to business models and customer relationships.” — Matthias Breunig, Director, Global Automotive Solutions, Google Cloud

Through Built with BigQuery, Google Cloud is helping tech companies like Sibros build innovative applications on Google’s Data Cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs.

“Sibros is looking forward to partnering with Google Cloud, which will enable vehicle manufacturers and suppliers to reach the next level in their use of data. Sibros solutions for Deep Data Logging and Updating on the Google Data Cloud, combined with Google BigQuery, will help them to mitigate risks, reduce costs, add innovative products, and introduce value-added use cases.” — Xiaojian Huang, Chief Digital Officer, Software, Sibros

Sibros and Google Cloud are driving Connected Mobility transformation to help our customers accelerate R&D innovation, power efficient operations, and unlock software-defined vehicle use cases with a full stack connected vehicle platform. Click here to learn more about Sibros on Google Cloud.

Lufthansa increases on-time flights by wind forecasting with Google Cloud ML

Wed, 28 Sep 2022 16:00:00 +0000

The magnitude and direction of wind significantly impacts airport operations, and Lufthansa Group Airlines are no exception. A particularly troublesome kind is called BISE: it is a cold, dry wind that blows from the northeast to southwest in Switzerland, through the Swiss Plateau. Its effects on flight schedules can be severe, such as forcing planes to change runways, which can create a chain reaction of flight delays and possible cancellations. In Zurich Airport, in particular, BISE can potentially reduce capacity by up to 30%, leading to further flight delays and cancellations, and to millions in lost revenue for Lufthansa (as well as dissatisfaction among their passengers).

Being able to predict this kind of wind well in advance lets the Network Operations Control team schedule flight operations optimally across runways and timeslots, to minimize disruptions to the schedule. However, predicting speed and magnitude can be incredibly difficult to model and thus to predict— which is why Lufthansa reached out to Google Cloud.

Machine learning (ML) can help airports and airlines to better anticipate and manage these types of disruptive weather events. In this blog post, we’ll explore an experiment Lufthansa did together with Google Cloud and its Vertex AI Forecast service, accurately predicting BISE hours in advance, with more than 40% relative improvement in accuracy over internal heuristics, all within days instead of the months it often takes to do ML projects of this magnitude and performance.

“Being impressed with Google’s technology and prowess in the field of AI and machine learning, we were certain that my working together with their expert, to combine our technology with their domain expertise, we would achieve the best results possible,“ said Christian Most, Senior Director, Digital Operations Optimization at Lufthansa Group.

Collecting and preparing the dataset

The goal of Lufthansa and Google Cloud’s project was to forecast the BISE wind for Zurich’s Kloten Airport using deep learning-based ML approaches, then to see if the prediction surpasses internal heuristics-driven solutions and gauge the ease of use and practicality of the deep learning approach in production.

Since deep learning-based techniques require large datasets, the project relies on Meteoswiss simulation data, a dataset consisting of multiple meteorological sensor measurements collected from several weather stations across Switzerland over the past five years. By using this dataset, we obtained data on factors like wind direction, speed, pressure, temperature, humidity and more, at a 10 min resolution, along with some information about the location of the weather stations, such as altitude. These factors, which we hypothesized to be predictive of the BISE, ended up carrying valuable signals, as we would see later.

This collected data was next subjected to an extensive cleaning and feature engineering process using Vertex AI Workbench, in order to prepare the final dataset for training. The cleaning phase included steps to drop the features, or rows, that contained too many missing values, or failed statistical tests for entropy, etc. Since the direction of wind is a circular feature (between 0 and 360 degrees), this column/feature was replaced with two features: the corresponding sine and cosine embedding. The dataset was then flattened such that the columns contained all the relevant features and sensor measurements from all the weather stations at a particular 10-minute interval.

Since the target variable — i.e,. BISE — was not directly available, we engineered a proxy target variable for BISE called “tailwind speed around runway,” which above a certain threshold indicates the presence of BISE along the runway.

Forecasting wind in the Cloud

Once the dataset was ready, Lufthansa and Google Cloud evaluated several options before deciding to experiment and tune Vertex AI Forecast, Google’s AutoML-powered forecasting service, in order to achieve optimum results. Vertex Forecast is capable of the required feature engineering, neural architecture search, and hyper parameter tuning, and it is managed by Google Cloud to score in the top 2.5% in the M5 Forecasting Competition on Kaggle, in a completely automated fashion. These qualities made it an excellent choice for Lufthansa, to reduce the manual overhead of creating, deploying, and maintaining top performing deep learning models.

The raw data files were loaded from cloud storage, preprocessed on Vertex AI Workbench. Then, a training pipeline was initiated on Vertex AI Pipelines, which performed the following steps in sequence:

The .csv data file was loaded from Cloud Storage into a Vertex AI managed dataset.
A Vertex AI forecasting training job was initiated with the dataset, and it was also registered as a model in the Vertex AI Model Registry.
Upon completion, the model was evaluated on the test set, and the model’s predictions and the input features and ground truth of the test set, were stored in a user-defined table in BigQuery. Several test metrics were also available on the service and model dashboards.

One of the biggest challenges was the severe imbalance in the dataset, as measurements with BISE were very far and few in between. In order to account for this, instances where BISE occurred, as well the occurrences temporally close to them, were upweighted using weights calculated with methods including Inverse of Square Root of Number of Samples (ISNS), Effective Number of Samples (ENS), and Gaussian reweighting. The formulas for the methods are given below. These weights were supplied as separate columns in the dataset, and were iteratively used thereafter by the service as the “weight” column.

ISNS

ENS

Weighted gaussian

Results and next steps

Fig 1. Recall for 2-hour horizon

Fig 2. F1 Score for a 2-hour Horizon

In the above figures, the x-axis represents the forecast horizon and the Y-axis shows the respective metrics (Recall/F1-score). As shown after multiple experiments, we can see Vertex AI Forecast achieved higher recall and precision t (red bar), outperforming Lufthansa’s internal baseline heuristics, with the performance gap widening steadily as the forecast horizon extends further into the future. At the two-hour mark, our custom-configured Vertex AI Forecast model improved by 40% relative to the internal heuristics and 1700% compared to the random guess baseline. As we saw with other experiments, at a six-hour forecast horizon, the performance gap widens even more, with Vertex AI Forecast in the lead. Since forecasting BISE a few hours in advance is very beneficial to prevent flight delays for Lufthansa, this was a great solution for them.

“We are very excited to be able to not only do accurate long term forecasts for the BISE, but also that Vertex AI Forecasting makes training and deploying such models much easier and faster, allowing us to innovate rapidly to serve our customers and stakeholders in the best possible manner,” said Swiss Oliver Rueegg, Product Owner, Swiss International Airlines.

Lufthansa plans to explore productionizing this solution by integrating it into their Operations Decision Support Suite, which is used by the network controllers in the Operations Control Center in Kloten, as well as to work closely with Google’s specialists to integrate both Vertex AI Forecast and other of Google’s AI/ML offerings for their use cases.

Building a Mobility Dashboard with Cloud Run and Firestore

Tue, 21 Jun 2022 16:00:00 +0000

Visualization is the key to understanding massive amounts of data. Today we have BigQuery and Looker to analyze petabytes scale data and to extract insights in a sophisticated way. But how about monitoring data that actively changes every second? In this post, we will walk through how to build a real-time dashboard with Cloud Run and Firestore.

Mobility Dashboard

There are many business use cases that require real-time updates. For example, inventory monitoring in retail stores, security cameras, and MaaS (Mobility as a Service) applications such as share ride. In the MaaS business area, locations of vehicles are very useful in making business decisions. In this post, we are going to build a mobility dashboard, monitoring vehicles on a map in real-time.

The Architecture

The dashboard should be accessible from the web browser without any setups on the client side. Cloud Run is a good fit because it can generate URLs, and of course, scalable that can handle millions of users. Now we need to implement an app that can plot geospatial data, and a database that can broadcast its update. Here are my choices and architecture.

Cloud Run — Hosting a web app (dashboard)
(streamlit — a library to visualize data and to make web app)
(pydeck — a library to plot geospatial data)
Firestore — a full managed database that keeps your data in sync

The diagram below illustrates a brief architecture of the system. In the production environment, you may also need to implement a data ingestion and transform pipeline.

Before going to the final form, let’s take some steps to understand each component.

Step 1: Build a data visualization web app with Cloud Run + streamlit

streamlit is an OSS web app framework that can create beautiful data visualization apps without knowledge of the front-end (e.g. HTML, JS). If you are familiar with pandas DataFrame for your data analytics, it won’t take time to implement. For example, you can easily visualize your DataFrame in a few lines of code.

code_block: <ListValue: [StructValue([('code', "import streamlit as st\r\nchart_data = pd.DataFrame(\r\n np.random.randn(20, 3),\r\n columns=['a', 'b', 'c'])\r\nst.line_chart(chart_data)"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed33ee80>)])]>

The chart on the webapp (Source)

Making this app runnable on Cloud Run is easy. Just add streamlit in requirements.txt, and make Dockerfile from a typical python webapp image. If you are not familiar with Docker, buildpacks can do the job. Instead of making Dockerfile, make Procfile with just 1 line as below.

code_block: <ListValue: [StructValue([('code', 'web: streamlit run app.py --server.port $PORT --server.enableCORS=false'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed33ee20>)])]>

To summarize, the minimum required files are only as below.

code_block: <ListValue: [StructValue([('code', '.\r\n|-- app.py\r\n|-- Procfile\r\n|-- requirements.txt'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed33ee50>)])]>

Deployment is also easy. You can deploy this app to Cloud Run with just a command.

code_block: <ListValue: [StructValue([('code', '$ gcloud run deploy mydashboard --source .'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed2c5c40>)])]>

This command will build and make your image with buildpacks and Cloud Build, thus you don’t need to set up a build environment in your local system. Once deployment is completed, you can access your web app with the generated URL like https://xxx-[…].run.app. Copy and paste the URL into your web browser, and you will see your first dashboard webapp.

Step 2: Add a callback function that receive changes in Firestore database

In the STEP 1, you can visualize your data with fixed conditions or interactively with UI functions on streamlit. Now we want it to update by itself.

Firestore is a scalable NoSQL database, and it keeps your data in sync across client apps through real-time listeners. Firestore is available on Android and iOS, and also provides SDKs in major programming languages. Since we use streamlit in Python, let us use a Python client.

In this post we don’t cover detailed usage of Firestore though, it is easy to implement a callback function that is called when a specific “Collection” has been changed. [reference]

code_block: <ListValue: [StructValue([('code', "from google.cloud import firestore_v1\r\n\r\ndb = firestore_v1.Client()\r\ncollection_ref = db.collection(u'users')\r\n\r\ndef on_snapshot(collection_snapshot, changes, read_time):\r\n for doc in collection_snapshot.documents:\r\n print(u'{} => {}'.format(doc.id, doc.to_dict()))\r\n\r\n# Watch this collection\r\ncollection_watch = collection_ref.on_snapshot(on_snapshot)"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed2ddaf0>)])]>

In this code, on_snapshot callback function is called when users Collection has been changed. You can also watch changes of Document.

Since Firestore is a fully managed database, you would not need to provision the service ahead. You only need to choose “mode” and location. To use real-time sync functionality, select “Native mode”. Also select nearest or desired location.

Using Firestore with streamlit

Now let’s implement Firestore with streamlit. We add on_snapshot callback and update a chart with the latest data sent from Firestore. Here is one quick note when you use the callback function with streamlit. on_snapshot function is executed in a sub thread, instead UI manipulation in streamlit must be executed in a main thread. Therefore, we use Queue to sync the data between threads. The code will be something like below.

code_block: <ListValue: [StructValue([('code', 'from queue import Queue\r\n\r\nq = Queue()\r\ndef on_snapshot(collection_snapshot, changes, read_time):\r\n for doc in collection_snapshot.documents:\r\n q.put(doc.to_dict()) # Put data into the Queue\r\n\r\n# below will run in main thread\r\nsnap = st.empty() # placeholder\r\n\r\nwhile True:\r\n # q.get() is a blocking function. thus recommend to add timeout\r\n doc = q.get() # Read from the Queue\r\n snap.write(doc) # Change the UI'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed2ddc10>)])]>

Deploy this app and write something in the collection you refer to. You will see the updated data on your webapp.

Step 3: Plot a geospatial data with streamlit

We learned how to host web apps on Cloud Run, then how to update data with Firestore. Now we want to know how to plot geospatial data with streamlit. streamlit has multiple ways to plot geospatial data which includes latitude and longitude, we here used pydeck_plot(). This function is a wrapper of deck.gl, a geospatial visualization library.

For example, provide data in latitude and longitude as to plot, add layers to visualize them.

code_block: <ListValue: [StructValue([('code', 'import streamlit as st\r\nimport pydeck as pdk\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndf = pd.DataFrame(\r\n np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],\r\n columns=[\'lat\', \'lon\'])\r\nst.pydeck_chart(pdk.Deck(\r\n map_provider="carto",\r\n map_style=\'road\',\r\n initial_view_state=pdk.ViewState(\r\n latitude=37.76,\r\n longitude=-122.4,\r\n zoom=11,\r\n pitch=50,\r\n ),\r\n layers=[\r\n pdk.Layer(\r\n \'HexagonLayer\',\r\n data=df,\r\n get_position=\'[lon, lat]\',\r\n radius=200,\r\n elevation_scale=4,\r\n elevation_range=[0, 1000],\r\n pickable=True,\r\n extruded=True,\r\n ),\r\n pdk.Layer(\r\n \'ScatterplotLayer\',\r\n data=df,\r\n get_position=\'[lon, lat]\',\r\n get_color=\'[200, 30, 0, 160]\',\r\n get_radius=200,\r\n ),\r\n ],\r\n ))'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed2dddf0>)])]>

Plotting with pydeck_plot (Source)

pydeck supports multiple map platforms. We here chose CARTO. If you would like to know more about great examples using CARTO and deck.gl, please refer to this blog.

Step 4: Plot mobility data

We are very close to the goal. Now we want to plot locations of vehicles. pydeck supports some ways to plot data, and TripsLayer would be a good fit to plot mobility data.

Demo using Google Maps JavaScript API (Source)

TripsLayer can visualize location data in time sequential. That means, when selecting a specific timestamp, it plots lines from location data in the time including last n periods. It also draws like an animation when you change the time in sequential order.

In the final form, we also add IconLayer to identify the latest location. This layer is also useful when you want to plot a static location, and it just works like a “pin” on Google Maps.

Now we need to think about how to use this plot with Firestore. Let’s make Document per vehicle, and only save the latest latitude, longitude, and timestamp of every vehicle. Why not save the history of locations? In that case, we should rather use BigQuery. We just want to see the latest locations that update in realtime.

Firestore is useful and scalable, yet NoSQL. Note that there are some good fits and bad fits in NoSQL.

Location data in Firestore Console

Step 5: Run

Finally, we are here. Now let’s ride in a car and record data… if possible.

For demo purposes, now we ingest dummy data into Firestore. It is easy to write data by using a client library.

code_block: <ListValue: [StructValue([('code', "db = firestore.Client()\r\ncol_ref = db.collection('connected')\r\ncol_ref.document(str(vehicle_ind)).set({\r\n 'lonlat': [-74, 40.72],\r\n 'timestamp': 0\r\n})"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f22ed2dddc0>)])]>

With writing dummy data, open the web page hosted on Cloud Run. you will see the map is updated upon new data coming.

Firestore syncs data on streamlit

Note that we used dummy data and manipulated the timestamps. Consequently, the location data updates much faster than actual time. This can be fixed once you use proper data and update cycle.

Try it with your data

In this post, we learned how to build a dashboard updated in real-time with Cloud Run and Firestore. Let us know when you find other use-cases with those nice Google Cloud products.

Find out more automotive solutions here.
Haven’t used Google Cloud yet? Try it from here.
Check out the source code on GitHub.

Learn how to tackle supply chain disruptions with SAP IBP and Google Cloud

Mon, 06 Jun 2022 16:00:00 +0000

Responding to multiple, simultaneous disruptive forces has become a daily routine for most demand planners. To effectively forecast demand, they need to be able to predict the unpredictable while accounting for diverse and sometimes competing factors, including:

Labor and materials shortages
Global health crises
Shifting cross-border restrictions
Unprecedented weather impacts
A deepening focus on sustainability
Rising inflation

Innovators are looking to improve demand forecast accuracy by incorporating advanced capabilities for AI and data analytics, which also speed up demand planning. According to a McKinsey survey of dozens of supply chain executives, 90% expect to overhaul planning IT within the next five years, and 80% expect to or already use AI and machine learning in planning.

Google Cloud and SAP have partnered to help customers navigate these challenges and supply chain disruptions starting with the upstream demand planning process, focusing on improving forecast accuracy and speed through integrated, engineered solutions. The partnership is enabling demand planners who use SAP IBP for Supply Chain in conjunction with Google Cloud services to access a growing repository of third-party contextual data for their forecasting, as well use an AI-driven methodology that streamlines workflows and improves forecast accuracy. Let’s take a closer look at these capabilities.

Unify data from SAP software with unique Google data signals

When it comes to demand forecasting and planning, the more high-quality and relevant contextual data you use, the better, because it helps you understand the influencing factors of your product sales to sense trends and react to disruptions or capitalize on market opportunities more timely and accurately.

The expanded Google Cloud and SAP partnership helps customers who use SAP® Integrated Business Planning for Supply Chain (SAP IBP for Supply Chain) bring public and commercial data sets that Google Cloud offers into their own instances of SAP IBP and include them in their demand planning models in SAP IBP. So, in addition to sales history, promotions, stakeholder inputs and customer data that are typically in SAP IBP, a demand planner can incorporate their advertising performance, online search, consumer trends, community health data, and many more data signals from Google Cloud when working through demand scenarios.

More data enables more robust and accurate planning, so Google continues to build an ecosystem of data providers and grow the number of available data sets on Google Cloud. Some current providers include the U.S. Census Bureau, the National Oceanic and Atmospheric Administration, and Google Earth, and partnerships are underway with Crux, Climate Engine, Craft, and Dun & Bradstreet to help companies identify and mitigate risk and build resilient supply chains.

Augmenting demand planning with additional external causal factor data is a starting point to drive more accurate forecasting. For example, knowing what regional events may be happening, or the weather patterns that may impact sales of your products, allows you to react faster to these changes by making sure adequate supply is being provided. The result is a more accurate overall plan that reduces resource waste and out-of-stock events. Planners can respond with more accurate and granular daily predictions about sales, pricing, sourcing, production, inventory, logistics, marketing, advertising, and more based on the expanded data.

Get more accurate forecasts with Google AI inside

Extending the already expansive algorithm selection available in SAP IBP, the release of version 2205 allows SAP IBP customers to access Google Cloud’s supply chain forecasting engine, which is built on Vertex AI — Google Cloud's AI-as-a-platform offering — from within SAP IBP as part of their forecasting process.

The benefit of using an AI-driven engine for demand forecasting is that it meaningfully improves forecast accuracy. Most demand forecasting today is done through a manually set, rules-based model versus an AI-driven model that is smarter and gets better at predicting demand as it works.

Take the fastest path from data to value with streamlined workflows

Vertex AI can include relevant contextual data sets for demand planning, and the results can be shown in SAP IBP for planners to incorporate when building their workflows.

In addition to more accurate forecasts, planners can work faster and more efficiently as they build potential scenarios, meaning they can do more simulations than they do now so that a wider range of disruptions can be modeled. Customers of SAP IBP don’t have to do any of the heavy lifting. They just have to share their data from SAP IBP with Google, then access the process workflow capabilities to set up automated workflows that use the combined data. Google makes the data available so that planners can use it as they’re setting up their workflows in Vertex AI.

Users of the Google Supply Chain twin and SAP IBP can combine the rich planning data from IBP with additional SAP data and other Google data sources to provide better supply chain visibility. The Google Supply Chain twin is a real-time digital representation of your supply chain based on sales history, open customer orders, past and future promotions, pricing and competitor insights, consumer history signals, external data signals and Google data.

Leverage Google data signals with SAP IBP for more accurate forecasts

It’s not difficult to access these new capabilities, and the benefits are more accurate near-term forecasts and more return on your investments in SAP IBP and Google Cloud. If you happen to be at the Gartner Supply Chain Symposium from June 6-8th in Orlando, Florida, stop by our booth to say hello. Or, get started now

No more normal? No problem when you build supply chains with data and AI

Tue, 31 May 2022 17:00:00 +0000

What if, after all the upheavals and innovations of the past two years, we’re not headed for some new normal but instead an era of no more normal?

“There are big, big challenges that need to be solved every single day by supply chain professionals,” Hans Thalbauer, Google Cloud’s managing director for supply chain and logistics, pointed out during our recent Supply Chain & Logistics Spotlight event. Among the issues Thalbauer ticked off were changes from the pandemic, consumer demand, labor shortages, the climate crisis, geopolitical instability, and energy shortages.

“And the thing is, it’s not just a short term issue, we think it’s a long-term and systemic issue,” Thalbauer said. “There’s a big question out there, which is: How will global trade change? Is it really transforming and translating into something new? Will global trade continue to work as is? I would even argue, we are living in a post-outsourcing world."

He's not alone in asking such questions. Executives and analysts are likewise wondering about the future of logistics, as are experts at the White House. The same day as the Supply Chain & Logistics Spotlight, the president’s Council of Economic Advisors released their annual report with an entire chapter dedicated to supply chain. In it, they noted that once-obscure, and ideally invisible, supply chains had “entered dinner table conversations.” And for good reason. “Because of outsourcing, offshoring, and insufficient investment in resilience, many supply chains have become complex and fragile,” the economists wrote.

Wherever, and whenever, we finally settle—more global or local, more automated or disintermediated, more agile or fragile—one of the likeliest results is a greater reliance on technology, and especially data, to help handle all the disruptions and interruptions on the horizon.

Leaders in the field, including at The Home Depot, Paack, and Seara Foods, are discovering opportunities in a few key areas: connecting data from end to end; the power of platforms to access and share information; and the importance of predictive analytics to mitigate issues as, or even before, they arise.

“We need to create visibility, flexibility, and innovation,” Thalbauer said. “Too often companies just focus on their orders, forecasts, and inventory, but typically they ignore the rest of the world. We need to bring in the public information, the traffic, weather, climate, and financial risks, connect that with the enterprise data, and we need to actually enable community data to create collaboration between business partners at every tier.”

End-to-end data

Companies have always sought visibility from the factory to the warehouse to the store and now the front door, and all the points in between. Both the challenge and necessity of seeing into all these is that as the data has grown, and our capabilities along with it, so has the complexity. It’s at a scale no humans can manage, which makes the importance not only of data but analytics and AI all the more essential.

Home Depot has had a front row seat to these growing interdependencies—especially when it comes to serving competing yet complimentary clienteles.

The pandemic presented its share of unexpected opportunities, as the combination of soaring home values, disposable income, and DIYers looking for (stay at) home projects led to runs on everything from lumber to sheds-turned-offices to garage doors. Empty shelves can lead to angry customers. And in this case, it wasn’t just homeowners and renters Home Depot was contending with, explained Chris Smith, vice president of IT Supply Chain at Home Depot, but also an increasingly important base of contractors and even large-scale developers. Each have their own specific, and sometimes competing, needs.

Whatever the future of logistics look like—more global or local, more automated or disintermediated, more agile or fragile—one of the likeliest results is a greater reliance on technology.

“We really have what we call an omnichannel algorithm.” Chris Smith, VP of IT Supply Chain at The Home Depot. “It’s really marrying up the customer’s preferences with our understanding of capacity, assortment, inventory availability, taking all that together, and saying: How do we best meet the customer promise and do it with the most efficient use of our supply chain? So where do we fulfill it from, where is the inventory available, and how do we do that in a way that’s most economical for us while still meeting the promise of the customer,” Smith said.

Paack, a last-mile delivery start-up serving the UK, Spain, France, Portugal, and Italy, is similarly pushing the envelope on fulfillment. The company focuses on combining a wealth of data—from drivers, customers, sensors, weather, and more—to ensure guaranteed delivery. So far, their success rate is approaching 98% of on-time delivery, with special scheduling tools to ensure customers are available to receive their packages.

Using solutions like the Last Mile Fleet Solution from Google Maps Platform, Paack can manage drivers and customers in real-time.

“The granularity of information we can collect in terms of which routes are being effectively followed by the driver’s route versus planned routes, the ability for them to change directions, because we might know locally of better ways to go, notifications from the customer as to their availability—these really allows us to build a better experience for everyone,” Olivier Colinet, chief product and technology officer for Paack, said. “We want first-time drivers to be the most productive drivers, and this first step allows us to do so.”

Power of platforms

Paack’s success exemplifies the power of building a strong platform for customers and workers, as well as tapping existing platforms, like Google Maps, to bolster your own.

On the other side of the globe, the world’s largest meat supplier is seeking to empower thousands of ranchers and farmers with a platform of their own. Seara, a Brazil-based supplier of pork, chicken and eggs that is part of the globe-spanning JBS conglomerate, launched its SuperAgroTech platform in July 2021.

Though in development for years, the program could hardly have come at a more critical time for the global food supply. The food industry was already coping with pandemic-related shortages and shutdowns, and then came the spillover effects from the war in Ukraine.

“In general, the entire supply chain was affected and the operation had to adapt to new working conditions,” Thiago Acconcia, the director of innovation and strategy at Seara, said. “So in the farms, in the field, the same situations are repeated, and the creation of this digital online platform enters as a facilitator when it gives autonomy to the farmer, providing them with the data input and digital communication.” It’s a level of connectivity the farmers never had with Seara before—and vice versa.

The technology has been deployed to more than 9,000 farms at launch. Through a range of IoT sensors, monitoring devices, and data inputs from farmers, operators and Seara data, teams can track a host of results. These include yields, animal health, profits, and even environmental and social impacts, which are becoming increasingly important features for consumers.

The eventual goal is to reach 100% digital management of the farm.

“So today, we are able to activate any producer in a few seconds, regardless of the location,” Acconcia said. With SuperAgroTech, the platform “doesn't mind if it's in the very south of the country, if it's in the central part. It's strengthening the relationships with our producers and also promoting a level of personalized attention they’ve never had.”

Stacks of containers

Predictive Analytics

As a company’s digital strategies evolve through integrated data and robust platforms, one of the most exciting opportunities arises around predictive analytics.

Home Depot has looked at ways to quickly adapt its digital stores using consumer data and AI to create better experiences, as well as smoothing out supply chain issues. Home Depot’s Chris Smith pointed to a listing for an out-of-stock appliance or tool, for example, that will quickly offer other locations or items for sale as a convenient alternative.

“We can apply machine learning in many different ways to make better, faster decisions, both in how we support moving inventory through our supply chain or how we understand available capacity to support our customers,” Smith said. “And with automation, from our distribution centers to our forecasting and replenishment systems, we’re going to continue to look at places where we can optimize and automate to make better decisions.”

For Paack, predictions could come in the form of traffic or storms or even the likelihood that a repeat customer will be available or not, without having to prompt them.

And at Seara, the role of data and analytics is not just vital to the business but the very vitality of the world. As climate, supply chains, global conflicts, migration, and other issues continue to constrain the food supply, anticipating issues could be the difference between salvaging a crop or not.

“We started creating advanced analytics by means of AI tools to not only notify real-time problems but also to predict what's going to happen in the near and long future,” Acconcia said. “We are talking about the world's food, and SuperAgroTech has the role to feed the world, and to overcome these biggest challenges.”

Fresh updates: Google Cloud 2021 Summits

Fri, 29 Oct 2021 18:00:00 +0000

There are a lot of great things happening at Google Cloud, and we’re delighted to share new product announcements, customer perspectives, interactive demos, and more through our Google Cloud Summit series, a collection of digital events taking place over the coming months.

Join us to learn more about how Google Cloud is transforming businesses in various industries, including Manufacturing & Supply Chain, Retail & Consumer Goods, and Financial Services. We’ll also be highlighting the latest innovations in data, artificial intelligence (AI) and machine learning (ML), security and more.

Content will be available for on-demand viewing immediately following the live broadcast of each event. Bookmark this page to easily find updates as news develops, and don’t forget to register today or watch summits on demand by visiting the Summit series website.

Upcoming events

Government & Education Summit | Nov 3-4, 2021

Mark your calendars – registration is open for Google Cloud’s Government and Education Summit, November 3–4, 2021.

Government and education leaders have seen their vision become reality faster than they ever thought possible. Public sector leaders embraced a spirit of openness and created avenues to digital transformation, accepting bold ideas and uncovering new methods to provide public services, deliver education and achieve groundbreaking research. At Google Cloud, we partnered with public sector leaders to deliver an agile and open architecture, smart analytics to make data more accessible, and productivity tools to support remote work and the hybrid workforce.

The pandemic has served as a catalyst for new ideas and creative solutions to long-standing global issues, including climate change, public health, and resource assistance. We’ve seen all levels of government and education leverage cloud technology to meet these challenges with a fervor and determination not seen since the industrial revolution. We can’t wait to bring those stories to you at the 2021 Google Cloud Government and Education Summit.

The event will open doors to digital transformation with live Q&As, problem-solving workshops and leadership sessions, designed to bring forward the strongest talent, the most inclusive teams, and the boldest ideas. Interactive, digital experiences and sessions that align with your schedule and interests will be available, including dedicated sessions and programming for our global audiences.

Register today for the 2021 Google Cloud Government and Education Summit. Moving into the next period of modernization, we feel equipped with not just the technology, but also the confidence to innovate and the experience to deliver the next wave of critical digital transformation solutions.

Digital Manufacturer Summit | June 22, 2021

Together we can transform the future of our industry. At Google Cloud’s Digital Manufacturer Summit customers will hear from Porsche, Renault Group, Doosan Heavy Industries & Construction, GE Appliances and Landis+Gyr who are boosting productivity across their enterprise with digital solutions powered by AI and analytics.

Google Cloud recently launched a report and blog on AI Acceleration, which reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. We will continue this thought leadership in the summit.

Hear from forward-thinking business executives as they discuss the latest trends and the future of the industry. Participate in focused sessions and gain game-changing insights that dive deep into customer experience, product development, manufacturing operations, and supply chain operations.

APAC Technical Series | June 22 - 24, 2021

IT and business professionals located in the Asia Pacific region can continue their cloud technology learnings by taking part in a three-day deep-dive into the latest data and machine learning technologies. This event will help you harness data and unlock innovation to build, iterate, and scale faster and with confidence.

Security Summit | July 20, 2021

At Google Cloud Security Summit, security professionals can learn why many of the world’s leading companies trust Google Cloud infrastructure, and how organizations can leverage Google’s cloud-native technology to keep their organization secure in the cloud, on-premises, or in hybrid environments.

During the opening keynote, engaging sessions, and live Q&A, customers will learn about how our Trusted Cloud can help them build a zero-trust architecture, implement shared-fate risk management, and achieve digital sovereignty. Join us to hear from some of the most passionate voices exploring how to make every day safer with Google. Together, we’ll reimagine how security should work in the cloud.

Retail & Consumer Goods Summit | July 27, 2021

Are you ready for the continued growth in digital shopping? Do you understand how leveraging AI and ML can improve your business? Join your peers and thought leaders for engaging keynotes and breakout sessions designed for the Retail and CPG industries at our upcoming Retail and Consumer Goods Summit on July 27th.

You’ll learn how some of the world’s leading retail and consumer goods companies like Ulta, Crate & Barrel, Albertsons, IKEA, and L’Oreal are using Google Cloud AI, machine learning, and data analytics technology to accelerate their digital transformation. In addition, we’ll share announcements on new products and solutions to help retailers and brands succeed in today’s landscape.

Now available on demand

Data Cloud Summit | May 26, 2021

In case you missed it, check out content from the Google Data Cloud Summit, which featured the launch of three new solutions - Dataplex, Analytics Hub and Datastream - to provide organizations with a unified data platform. The summit also featured a number of engaging discussions with customers including Zebra Technologies, Deutsche Bank, Paypal, Wayfair and more.

Watch on-demand now: Global

Financial Services Summit | May 27, 2021

We launched Datashare at the Financial Services Summit; this solution is designed to help capital markets firms share market data more securely and efficiently. Attendees can also view sessions on a range of topics including sustainability, the future of home buying, embedded finance, dynamic pricing for insurance, managing transaction surges in payments, the market data revolution, and more. Customers such as Deutsche Bank, BNY Mellon, HSBC, Credit Suisse, PayPal, Global Payments, Roostify, AXA, Santander, and Mr Cooper shared their insights as well.

Watch on-demand now: NORTHAM & EMEA

We have also recently launched several new blog posts tied to the Financial Services Summit:

Applied ML Summit | June 10, 2021

The Google Applied ML Summit featured a range of sessions to help data scientists and ML engineers explore the power of Google’s Vertex AI platform, and learn how to accelerate experimentation and production of ML models. Besides prominent Google AI/ML experts and speakers, the event also featured over 16 ML leaders from customers and partners like Spotify, Uber, Mr. Cooper, Sabre, PyTorch, L’Oreal, Vodafone and WPP/Essence.

Watch on-demand now: Global

Solving for more sustainable and resilient value chains

Wed, 21 Apr 2021 16:00:00 +0000

The past year highlighted the fragility of global supply chains and logistics networks and the need to adapt to rapidly changing business models and customer preferences. The importance of flexible, resilient and sustainable customer value chains is critical as organizations recalibrate to a post-pandemic world.

Global supply chains are also subject to environmental risks. In 2020, over 8,000 suppliers disclosing through CDP, a global disclosure system for environmental impacts, reported that US$1.26 trillion of revenue is likely to be at risk over the next five years due to climate change, deforestation, and water insecurity¹. Organizations are also likely to be challenged with reputational and regulatory risks across their value chain. For instance, emissions from supply chains are on average 11.4x higher than emissions from a company’s direct operations². In many sectors, supply chains are responsible for over 80% of total greenhouse gas emissions.

There is a strong argument for accelerating efforts to tackle these emissions, as suppliers reported combined savings of US$33.7 billion in 2020 by actively cutting emissions.³

Challenges in creating a sustainable customer value chain

Organizations struggle with achieving sustainable value chain goals largely due to three factors:

Limited visibility across the end-to-end value chain to drive transparency and accelerate adoption of sustainable best practices
Lack of flexibility to adapt to new business models as highlighted during the pandemic and the rapid shift to e-commerce
Limited insight into an organization’s operational decisions and their impact in reducing carbon emissions, preventing data-driven decision making.

How Google Cloud can help

Our mission at Google Cloud is to accelerate your organization’s ability to digitally transform your business. That includes supply chains. With better insights from data you can automate processes more intelligently. With smarter ML models you can optimize systems and routing. With an open platform you can integrate partner solutions. And you can connect your workforce in real time to collaborate up and down the value chain.

Leveraging these technologies, we’re partnering with our customers to tackle the unique sustainability challenges they face to help transform their supply chains.

Click to enlarge

Greater visibility into supply chain impacts with ENI: By creating an open digital platform for companies to measure their own sustainability performance, Google Cloud and ENI, together with Boston Consulting Group, are helping companies gain an awareness of their strengths and areas for improvement across industrial supply chains.

Enabling sustainable sourcing at Unilever: By combining the power of cloud computing with satellite imagery and AI, Google Cloud and Unilever are building a more holistic view of the forests, water cycles, and biodiversity that intersect Unilever’s supply chain—raising sustainable sourcing standards for suppliers and bringing Unilever closer to its goal of ending deforestation and regenerating nature.

Reducing emissions from last-mile logistics and fleet operations at UPS: UPS leverages Google Cloud smart analytics platform to reduce fuel consumption by 10 million gallons a year, reducing carbon emissions and saving up to $400 million a year.

Reducing manufacturing waste and improving production quality at LG and GlobalFoundries: LG improved defect detection accuracy by 6% and reduced the time to design and train ML models from days to hours using Google Cloud Vision AI. The Vision AI solution was able to reduce waste and increase customer satisfaction and quality at GlobalFoundries.

Reducing packaging at Lush: Lush was able to nearly eliminate plastic packaging by using Google AI to develop an app that leverages AI and augmented reality to recognize products and overlay product information.

In solutions engineering our goal is to take these unique experiences and scale them to help organizations reduce emissions and meet their sustainability goals. To facilitate collaboration along this journey we are creating a program to work with our customers and assess where cloud technology can impact their value chains in an environmentally positive way.

Steps for creating more sustainable value chains

We’ve developed innovative models to tackle challenges from reducing IT costs to data center transformations to IT infrastructure emissions. We’re excited to apply it to our customers’ sustainability priorities and pain points across the supply chain. We will partner closely to build proof of concepts (PoCs) to tackle new opportunities that help customers achieve their sustainability goals.

We’ll start with benchmarks. We measure where customers are relative to industry benchmarks, to better help them achieve their target goals.
Next, we’ll assess sustainability and supply chain processes against a maturity curve. This creates a gap analysis to identify areas for improvement.
We’ll prioritize the areas of focus using factors such as cost reduction, productivity improvement, revenue impact, and environmental and financial risks.
We’ll map the Google Cloud solutions against your areas of focus identifying where cloud technology can potentially reduce environmental impact and operational costs, and where it can enhance security, compliance, and flexibility.
With a short list of opportunities, we’ll partner closely with your teams to build PoCs and test the impact of our solutions and ability to scale across your end-to-end value chain.

Click to enlarge

This is just the beginning of our journey to help address the most challenging problems across the supply chain and partner with our customers to help them achieve sustainability goals. You can learn more about our sustainability efforts and how we’re integrating circular economy principles into our own supply chain to make it more sustainable. Contact us to tell us what you’re solving for and get started. A Google Cloud expert will help you find the best engagement for you. We look forward to helping you achieve your sustainable value chain goals.

^{1. CDP Global Supply Chain Report 2020
2. CDP Global Supply Chain Report 2020
3. CDP Global Supply Chain Report 2020}

Run a transformed supply chain—see how at Google’s Digital Supply Chain summit

Thu, 25 Mar 2021 18:00:00 +0000

At the start of 2020, who knew that our supply chains would be so disrupted that we'd have to worry about having enough toilet paper or paper towels? Yet, the early days of the COVID-19 pandemic resulted in many disruptions and unanticipated events. On a practical level, the sudden changes in consumer behavior placed supply chains in the spotlight, and revealed to many—including consumers everywhere—the fragility of our logistics networks.

Of course, this disruption wasn’t just isolated to household items. Entire modes of purchasing shifted dramatically (and perhaps permanently). At the end of 2019, 16% of global sales was e-commerce. Within four months, that number grew to 33%. Supply chain companies were forced to adapt almost overnight to massive shifts to e-commerce and rapidly changing delivery models.

The follow-on effects of this shift have been equally dramatic, including leading to a shortage of shipping containers. As COVID-19 lockdowns resulted in fewer people in the ports, this caused shipping traffic jams, which in turn led to a sharp rise in container shipping costs. And let’s not forget perhaps the most visible manifestation of why supply chains are the backbone of the global economy: The massive worldwide effort to deliver and distribute COVID-19 vaccines.

The limitation of today’s supply chains

It is not that supply chain professionals haven’t made investments to better predict demand, deliver or fulfill orders, and manage inventory. In fact, according to IDC Research, investments in supply chain management and service delivery are projected to grow by 34% in just the next 3 years—from $48 billion today to $64 billion by the end of 2023. However, there are still significant limitations to overcome, particularly in three key areas:

Visibility: Companies don’t have enough information about their inventories to react to the uncertainties of profound change.
Flexibility: Companies running standard processes are slow to adapt to the changes.
Intelligence: Without streamlined, cleaned, and actionable data, companies can’t accurately predict and meet demand.

Supply chains, then, are due for innovation. Unlike the manufacturing sector overall, which has adopted everything from AI and robotics to smart factories, supply chains have made only relatively small adjustments to their standard processes.

Join the supply chain transformation at our summit

To help companies discuss and address these pressing issues, we’re hosting a Digital Supply Chain Summit on March 30, 2021, to bring together more than 300 senior supply chain and logistics leaders from across the world. At this event, attendees will learn how they can create a digital supply chain platform that enables them to deliver exceptional customer experience; how to build resilient and sustainable supply chains; and how to run supply chains autonomously through the use of AI, ML, and other advanced technologies.

Among the featured industry speakers are Kuehne+Nagel and J.B. Hunt, both among the world's leading transportation and logistics companies, who are digitizing their supply chains to enable every process, person, and team. They will discuss their digital transformation journeys, particularly how they’re leveraging the cloud, artificial intelligence, and data analytics to unlock new levels of efficiency and business performance.

Also at the event, you’ll hear from the leaders of Google's own supply chain and data center operations, who will discuss how the cloud-based solutions they’ve deployed have driven real impact and business results. Finally, industry practice leaders from Accenture and Deloitte will also share how you can architect a customer-centric digital supply chain and a connected digital thread across your extended value chain.

Why be average when you can be unique?

As a company running a supply chain, you don’t need to stick with the status quo. Your transformation can be achieved by leveraging data to power individualized processes, which will set your company apart from the pack. We’re helping companies build a cloud-based digital supply chain platform based on four capabilities:

First, using a digital supply chain twin, which is a digital representation of the physical supply chain as its core.
Second, using intelligence to anticipate and predict potential outcomes.
Third, allowing end users to access information from whatever device they are using, anywhere.
And fourth, embracing partnering when it comes to applications. Supply chain companies succeed when they complementing their existing systems with new technologies, making it easier to innovate, adapt, and overcome limitations.

Take the first step to transformation

We welcome you to register for the Google Cloud Digital Supply Chain Summit to get a comprehensive look at how companies are digitally transforming their supply chain and logistics operations. This online event is taking place on March 30. We hope it will help you identify steps you can take today to advance your own digital strategies with cloud-based solutions, data analytics, and AI.

COVID-19 reshapes manufacturing landscape, new Google Cloud findings show

Thu, 10 Dec 2020 14:00:00 +0000

After facing severe headwinds from COVID-19, ranging from decreased orders to negative impacts on operations, manufacturers around the world have started to revamp their operating models and supply chain strategies—and now feel more prepared to successfully navigate future pandemics, according to our new research released today.

The key for manufacturers' ability to transform—despite the ongoing pandemic—is their embrace of digital enablers and disruptive technologies. In fact, more than two in five manufacturers have increased their use of data and analytics, digital productivity tools, and public cloud platforms, irrespective of their location in the world.

“Manufacturers have always prepared for unpredictable events that could adversely impact operations,” said Bob Parker, Senior Vice President, Enterprise Applications, Data Intelligence, Services, and Industry Research for IDC. “But what makes COVID-19 so unique is its sustained nature that touches the supply chain, irrespective of geographical location, in a way we haven’t seen in our lifetime. As a result, we’re seeing an urgency from manufacturers to quickly put the right technological levers in place, sooner rather than later. While there may have only been initial conversations about digital transformation in the past, we’re now seeing a rapid acceleration of critical tools and technologies being adopted within the industry.”

Below are five noteworthy takeaways we’ve identified within our findings:

1. Not surprisingly, as with other industries, the pandemic has had a devastating effect on manufacturers overall. Nearly all of manufacturers (95%) believe their manufacturing or supply chain operations have been negatively impacted by the pandemic. The top three adverse impacts include lost productivity (46%), lower sales (44%), and increased lead times, possibly due to supply chain disruptions (39%). About a third of manufacturers have also experienced downward pressure on overall customer demand (35%), labor shortages (34%), and/or the inability to maintain a safe working environment (33%).

2. To overcome COVID-19-related challenges, manufacturers were forced to pivot their operating models and supply chain strategies. More than three-fourths of surveyed manufacturers (77%) said COVID-19 caused their companies to re-evaluate their operating model strategies. The most common reasons include an inability to collaborate effectively with value chain partners (41%), the inability to collaborate effectively with employees (40%), and a lack of the right technology to operate without a large number of on-site workers (39%).

3. Technology played the most critical role in maneuvering through the pandemic, particularly “disruptive” AI, robotics, and more. More than three-fourths of surveyed manufacturers (76%) revealed that the pandemic has caused their companies to increase the use of digital enablers and disruptive technologies such as: cloud, artificial intelligence (AI), data analytics, robotics, 3D printing/additive manufacturing, Internet of Things, and augmented or virtual reality. More specifically, the top three digital enablers/disruptive technologies that respondents are further utilizing are data and analytics (46%), digital productivity tools (43%), and public cloud platforms (42%).

Caroline Gorski, Group Director, R² Data Labs, Rolls-Royce/Co-Founder and Former Chair, Emergent Alliance shared: “Digital technology has played a huge role in enabling the continuity of substantial parts of our business and as we pursue our ambitions of pioneering sustainable power, we need people in factories making products, alongside technologies that improve efficiency, accuracy and productivity.”

4. Interestingly, despite many manufacturers not being prepared for COVID-19, most now feel prepared to successfully navigate future pandemics. As mentioned earlier, nearly (95%) believe their manufacturing or supply chain operations have been negatively impacted by the pandemic. That said, 82% of those surveyed now feel prepared to deal with another COVID-19-like event in the future. This sentiment could be related to how manufacturers successfully ventured into new verticals, such as providing ventilators and PPE during shortages and resuming investments in new digital factory plans.

5. Finally, the pandemic—and its aftermath on the manufacturing industry—has differed greatly by country.

In Japan, approximately half of manufacturers who cited a negative impact (51%) say that the pandemic has led to lower sales, compared to 44% globally.
In Korea, more than two in five manufacturers who cited a negative impact (43%) said that the pandemic hindered their ability to maintain a safe working environment, compared to 33% globally.
In France, nearly half of manufacturers (48%) felt equipped with the right technological tools to maintain business continuity in the first 1-3 months of the pandemic, compared to 37% globally.
In the UK, more than two in five manufacturers (43%) said that dependency on legacy technology has created more risk for their respective business operations over the next year, compared to 30% globally.
In Italy, more than a third of manufacturers (35%) felt that their IT systems lacked necessary redundancies, which undermined their overall operational resiliency, compared to slightly less than a quarter of overall surveyed manufacturers (23%).
In Germany, for 86% of manufacturers, COVID-19 has caused an increased use of digital enablers and disruptive technologies, compared to 76% globally.
In the United States, 64%of manufacturers have increased their use of data and analytics, compared to 46% globally.

To learn more about these findings and more, download our full report here and our infographic here.

^{Research Methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

Helping manufacturers during and after COVID-19

Thu, 14 May 2020 13:00:00 +0000

The impact of COVID-19 has touched every industry—including manufacturing, which relies heavily on skilled, hands-on workers and complex supply chains. According to a March 2020 survey by the U.S. National Association of Manufacturers, four out of five U.S. manufacturing companies expect to be financially impacted by COVID-19, more than half think they’ll need to change how they operate, and over a third anticipate supply chain disruptions.

COVID-19 has also compelled manufacturers to address important issues facing their employees, including remote work and social distancing. It’s a critical time for manufacturers to increase the agility and digitization of their supply chains and operations; here’s how cloud can help businesses resume under new norms.

Enabling automation

While the full impact of COVID-19 is still unknown, many factories are already experiencing decreases in workforce capacity and resources. Manufacturers face tough questions around how to quickly understand this new landscape and develop new operating procedures and automation initiatives that enable them to adapt quickly and enable their employees to safely work on site.

We want to help manufacturers address these challenges by offering tools that automate processes, remotely monitor systems, and extend their capabilities beyond the factory floor. Using Vision AI, for example, manufacturers can train machine-learning models to visually inspect goods and processes for quality and compliance, without putting human inspectors at risk. By connecting operational technology (OT) and information technology (IT) via the cloud, operators can monitor and control specific machines or plants remotely, using dashboards and performance views.

GlobalFoundries, a leader in the semiconductor manufacturing industry, is already using AutoML Vision to build a visual inspection solution that can detect random defects in wafer map and scanning electron microscope (SEM) images, which are essential elements in the semiconductor manufacturing process. AutoML Vision reads in the images of wafers and sample defects, and trains customized models to detect these defects. AutoML Vision could successfully classify 80% of the images based on a limited amount of training data in the initial pass itself. This fast path to high accuracy let GlobalFoundries quickly move to production, start realizing benefits, and scale up. The foundry subsequently improved quality and customer satisfaction, and 40% of the manual inspection workload has already been successfully shifted to the visual inspection solution built based on AutoML Vision.

Supporting remote work

Google Meet, our premium, secure video meetings solution, can help manufacturers hold daily meetings, training, and onboarding without the need to be on site, while G Suite tools like Google Docs, Sheets, and Slides let teams collaborate on document authoring remotely. Google Meet can also assist with virtual training and the safe and secure on-boarding of new hires.

KAESER Compressors, one of the world leaders in compressed air products, accelerated their deployment of G Suite when they needed to rapidly convert their teams to remote work as a result of COVID-19. “We were deeply impressed by how quickly our employees adopted G Suite and we really believe in cloud’s benefits,” says Falko Lameter, CIO at KAESER Compressors. “We have access to more memory, better machines, and more advanced technology, such as machine learning. Google Cloud has shown its commitment to innovation in these areas, and we are looking forward to scaling our collaboration in the future.”

Energy solutions provider Viessmann was able to keep up its production, and G Suite has helped them to convert their employees to remote work within a 48-hour span. Since then they have conducted roughly 60,000 Google Meet conferences per month. Additionally, Google Sheets was established as the primary IT dashboard for monitoring KPIs for Viessmann’s IT infrastructure. Being one of the first manufacturing companies that chose Chromebooks granted employees who work from home peace of mind, as their accounts are secure and protected through Google’s modern authentication technologies. Viessmann even developed a ventilator in significantly less time than usual. G Suite helped the involved engineers to collaborate and exchange on the project with ease and thus was essential to speed up the launch process. “The G Suite communications and collaboration infrastructure is self-maintaining, and it has made each of us more independent,” says Alexander Pöllmann, Head of Intranet and Collaboration Services at Viessmann. “We can work together from everywhere across borders and time zones, on any device. This kind of flexibility makes our workforce more agile, and ultimately, happier and more productive.”

Koenig & Bauer, the world's oldest printing press manufacturer, migrated to G Suite in early 2020 to increase productivity and collaboration amongst teams. In light of COVID-19, the timely switch to G Suite helped Koenig & Bauer significantly to keep workplaces connected. Teams in different locations—even at home—have access to tools like video conferencing, calendar functions, word processing, and spreadsheet calculations, and can now share files, all with a single click and on one single platform.¹ “Before we moved to G Suite, we had a very heterogeneous, non-collaborative IT environment and the resource consumption was pretty heavy,” says Jürgen Tuffentsammer, CIO of Koenig & Bauer. “Since the migration, our team collaboration has improved and we can share and execute against innovative ideas so much faster than before.”

Managing volatility in supply and demand

The COVID-19 pandemic has disrupted global supply chains and distribution channels. Now more than ever, accurately forecasting demand and optimizing supply in this continuously evolving environment requires integrating data from multiple sources and analyzing it in real time. It's a job analytical tools like BigQuery were designed for. By applying smart analytics and AI, manufacturers can better predict demand and adapt their operations to meet it.

AI can also help mitigate problems with last-mile delivery, which account for more than half of all shipping costs. The ability to optimize routes using real-time weather and traffic data, as well as to deploy ML models to predict where new pickups are likely to come from, will enable companies to minimize operating expenses while maximizing services.

Last month, Missouri Governor Mike Parson announced the launch of a new tool developed by Google Cloud to help health care providers connect with Missouri manufacturers and suppliers of personal protective equipment (PPE). The Missouri PPE Marketplace tool is a joint effort between the state and the Missouri Hospital Association, built to help those manufacturers that have shifted production to PPE enter the healthcare market and connect with buyers.

Additionally, over the past month, the state’s Department of Economic Development (DED) has gathered interest from more than 200 PPE manufacturers and suppliers, inviting them to register in the system. State healthcare agencies and the Missouri Hospital Association are reaching out to healthcare providers across the state to ensure they have access and can connect directly with suppliers through the new tool.

Optimizing IT spend

Moving to the cloud is an important way businesses can optimize their technology spend and find new efficiencies. For manufacturers, this can translate into more customers served, more issues resolved, and more adaptability for the overall business. The cloud offers the potential for drastically reduced data costs and infrastructure savings, plus increased performance, simplicity, and scalability across IT environments.

The cloud can support manufacturers in many ways, from improving safety, to weathering market uncertainties, to preparing for the future. For example, we recently joined the Rolls-Royce EMER²GENT alliance, which aims to help foster global economic recovery by identifying early signs of rebounding economies (we’re providing access to our public data sets and BigQuery as part of the effort). Whether it’s modernizing infrastructure, increasing agility, or digitizing supply chains and operations, we’re focused on providing the solutions that will help enable manufacturers to operate during the pandemic and beyond.

Visit our website to learn more about manufacturing on Google Cloud.

^{1. Source: Koenig & Bauer Annual Report 2019}

Mitigating risk in the hardware supply chain

Fri, 22 Mar 2019 16:00:00 +0000

At Google, security is among our primary design criteria as we build hardware, software, and services. We think comprehensively about potential risks, no matter how small, and the best ways to mitigate them and stay ahead of attackers.

We take a “defense in depth” approach to security, which means that we don’t rely on any one thing to keep us secure, but instead build layers of checks and controls. Even if an attacker were to circumvent one of our safeguards, they would be met with many more carefully designed protections to keep them out.

One area where we've put a lot of thought, and which we continue to focus on, is the security of our hardware supply chain. Today, I’d like to go into a few of the things we do specifically in this area.

Hardware design and provenance

A Google data center consists of thousands of servers connected to a local network. In most cases, both the server boards and the networking equipment are custom-designed by Google. We vet component vendors and choose components with care, working with vendors to audit and validate the security properties provided by the components. We also design custom chips, such as the Titan hardware security chip that we’re rolling out on both servers and peripherals, which help us securely identify and authenticate legitimate Google devices at the hardware level.

Hardware tracking and disposal

Google meticulously tracks the location and status of all equipment within our data centers—from acquisition to installation to retirement to destruction—via barcodes and asset tags. Metal detectors and video surveillance are implemented to help make sure no equipment leaves the data center floor without authorization. If a component fails to pass a performance test at any point during its lifecycle, it is removed from inventory and retired.

When a hard drive is retired, authorized individuals verify that the disk is erased by writing zeros to the drive and performing a multiple-step verification process to ensure the drive contains no data. If the drive cannot be erased for any reason, it is stored securely until it can be physically destroyed. Depending on available equipment, we either crush and deform the drive or shred the drive into small pieces. Each data center adheres to a strict disposal policy and any variances are promptly addressed.

Secure boot stack and machine identity

Google servers use a variety of technologies to ensure that they are booting the correct software stack. We use cryptographic signatures over low-level components like the BIOS, bootloader, kernel, and base operating system image. These signatures can be validated during each boot or update. The components are Google-controlled, built, and hardened. With each new generation of hardware we strive to continually improve security: for example, depending on the generation and type of server, we root the trust of the boot chain in either a lockable firmware chip, a microcontroller running Google-written security code, or the above mentioned Google-designed security chip.

Each server in the data center has its own specific identity that can be tied to the hardware root of trust and the software with which the machine booted. This identity is used to authenticate API calls to and from low-level management services on the machine.

Google has developed automated systems which ensure servers run up-to-date versions of their software stacks (including security patches), detect and diagnose hardware and software problems, and remove machines from service if necessary.

Defense-in-depth

As mentioned, while these are examples of protections designed to address specific attack vectors in a potential supply chain attack, they are by no means the only defense. Google’s infrastructure and Google Cloud have been designed with a defense-in-depth approach so that we have opportunities to mitigate potential vulnerabilities at other layers of our stack. For example, even if a piece of server hardware were compromised, our network infrastructure is designed to be able to detect and automatically prevent the command-and-control communications that are often necessary to take advantage of compromised hardware. Similarly, by encrypting and authenticating network traffic we are able to prevent a compromised network device from accessing sensitive data.

Google will continue to invest in our platform to allow you to benefit from our services in a secure and transparent manner. To learn more about our approach to infrastructure security, visit our Infrastructure Security page, and download our Infrastructure Security whitepaper.

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