The Internet of Things on AWS – Official Blog

Optimizing Operational Efficiency for Project Kuiper’s Satellite Manufacturing with AWS IoT SiteWise


Project Kuiper is Amazon’s low Earth orbit (LEO) satellite broadband network. It aims to provide fast, affordable connectivity to communities around the world that are unserved or underserved by traditional internet and communications options. The network will also have the performance, capacity, and flexibility to serve a wide range of enterprise, telecommunications, and government customers. To achieve this goal, Amazon is deploying thousands of satellites in Low Earth orbit (LEO) linked to a global network of antennas, fiber, and internet connection points on the ground.

High-tech manufacturing, including advanced CNC (Computer Numerical Control) machining, produces high-precision components for Project Kuiper’s broadband satellites. The Project Kuiper team then assembles and integrates these components into thousands of satellites, relying on cutting-edge technology throughout the production process. Optimizing these complex manufacturing operations required a solution to ingest, organize, compute, analyze, and monitor critical measurements for near real-time (NRT) monitoring and equipment analysis.

The team decided to build a solution using AWS IoT SiteWise, a managed service to collect, store, organize, and monitor industrial equipment data at scale. By leveraging AWS IoT SiteWise’s industrial data modeling and processing capabilities, Project Kuiper was able to create data-driven insights that improve operational and performance efficiency, as well as manufacturing quality. In this blog, you’ll learn about the challenges Project Kuiper faced in their operations, the solution architecture they deployed, and the business impact they achieved.

Opportunity | Using AWS IoT SiteWise for Operational Efficiency

Project Kuiper’s high-tech manufacturing process utilizes CNC machines to convert raw materials, such as aluminum and composites, into intricate parts and components for its broadband satellites. These components include antenna reflectors, mounting brackets, and mechanical housings, all of which require complex geometries and tight tolerances. The automated milling, turning, and grinding operations enabled by the CNC machines ensure consistent quality across high-volume manufacturing. This helps the team maintain the precision and reliability necessary for Project Kuiper’s cutting-edge satellite technology.

It was critical for the team to collect and analyze manufacturing data in NRT because the parts were highly customized and there were frequent design changes. This AWS IoT SiteWise analysis enabled them to make rapid adjustments to the manufacturing process. These adjustments minimized machine downtime, defects, and waste, while maximizing quality and efficiency. However, it was a challenge to collect the data for the NRT visibility of the manufacturing Key Performance Indicators (KPIs) and equipment performance. To accomplish this, the team tracked metrics like overall equipment effectiveness (OEE), which is a standard industry measure of how well manufacturing time is utilized to produce good parts. Because the team monitored OEE, they gained deep insight into loss categories, and identify operation bottlenecks and improvement opportunities.

Solution | Enabling NRT Data Delivery for Proactive Early Identification of Issues

To address these challenges, the Project Kuiper team implemented a solution that leveraged multiple AWS services. This solution helped them collect manufacturing operational data, compute KPIs, monitor NRT dashboards, and run longer-term trend analytics.

The AWS IoT SiteWise Edge software securely collected manufacturing data from the CNC machine. It selectively forwarded this equipment data, or process data, to AWS IoT SiteWise in the cloud. AWS IoT SiteWise provides two ingestion mechanisms – a streaming ingestion API to ingest telemetry data within milliseconds, and a buffered ingestion API to process analytical data streams in batch. By leveraging both ingestion methods, Project Kuiper was able to configure cost-efficient and scalable data pipelines that supported their NRT monitoring and data analytics needs. This approach helped them optimize costs by sending only the necessary data for NRT monitoring via the streaming path, while using the more cost-effective buffered ingestion for analytical applications.

AWS IoT SiteWise created a virtual representation of the physical assets, such as the CNC machines, and organized them in a hierarchical structure to help contextualize the manufacturing data. This contextualization helped the Project Kuiper team to associate data streams (including sensor readings, machine status, and performance metrics) with specific assets. Contextualized data is more accessible and easier to interpret for many stakeholders (such as engineers, operators, and managers) so that they can quickly search, locate, and analyze relevant information.

Project Kuiper team leveraged AWS IoT SiteWise’s multi-tiered storage for cost optimization, data lifecycle management, and system performance as their manufacturing operation scaled. The team defined data retention periods to keep the most recent and frequently accessed data in hot storage for real-time monitoring. AWS IoT SiteWise automatically moved older, less frequently accessed data to cost-effective warm and cold storage tiers. This storage lifecycle strategy enabled long-term retention of historical data for trending and insights while ensuring fast query performance for real-time monitoring and analysis. The scalable storage solution accommodated Project Kuiper’s evolving requirements as their manufacturing operations grew and data volumes increased, without incurring excessive costs or performance issues.

Data visualization plays a crucial role in monitoring operational efficiency of manufacturing processes. AWS IoT SiteWise Monitor is used for NRT operational dashboards.The Project Kuiper team used the NRT runtime charts, primarily line graphs, to quickly identify abnormal conditions and escalate issues for prompt resolution. Engineering then looked closer at the affected data points to understand their impact on other operating conditions. Dashboard user can also search for assets and properties they want to monitor and drag them into data widgets, including XY-plots, timelines, and tables. The NRT dashboard tracked key metrics such as OEE, Defect Rates, Cycle Times, and overall throughput efficiency. For longer-term analysis and business intelligence, Project Kuiper utilized Amazon QuickSight. QuickSight provided a wide range of capabilities to create management reports and conduct in-depth data inspections over extended historical timeframes.

Figure 1: High-level architecture

Outcome | Improved data-driven decision-making for optimized operational efficiency, quality, and cost

Project Kuiper achieved success supported by their implementation of the AWS IoT SiteWise based architecture to monitor and analyze CNC machine data in NRT. By leveraging AWS IoT SiteWise, SiteWise Monitor, and other AWS analytical tools (like Amazon Athena and Amazon QuickSight), they gained deep visibility into the manufacturing process. The contextualized insights helped them to make data-driven decisions that optimized production efficiency, quality, and cost.

Since deploying the solution, Project Kuiper has seen improved OEE, resulting in reduced unplanned downtime and improved asset utilization. The ability to detect and address quality issues in NRT has led to a reduction in scrap and rework, which has resulted in substantial cost savings. Additionally, the insights gained from historical data analysis have facilitated their ability to identify production bottlenecks and implement targeted process improvements, which have led to overall throughput improvements.

“As engineering leader, I’m thrilled with the value our teams have gained from implementing AWS IoT SiteWise for near real-time manufacturing analytics. The intuitive cloud dashboards assessing effectiveness, quality, output, and downtime rates have empowered data driven decision making across our facility.”

– Paul Palcisco, Director, Production, Kuiper Production Operations

“We’re really excited to be part of Project Kuiper, and proud of the operational efficiency gains the team has achieved by adopting AWS IoT SiteWise, specifically for monitoring KPIs in near real time across their assets. With dynamic data collection and dashboards calculating operational equipment effectiveness (OEE), defect rates, cycle times, and overall throughput efficiency, Project Kuiper has gained greater visibility into their bottlenecks and how to resolve them.”

– Michael MacKenzie, GM of Industrial IoT and Edge at AWS


In this post, we discussed how Project Kuiper was able to collect, store, organize, and monitor data from the manufacturing process using AWS IoT SiteWise. This solution helped Project Kuiper team to identify inconsistencies, detect anomalies, and make data-driven proactive decisions to optimize production efficiency and quality. Project Kuiper’s journey with AWS IoT SiteWise demonstrates the transformative power of NRT monitoring and data-driven decision making in high-tech manufacturing.

Learn More

Read more about Amazon’s Project Kuiper initiative here. To get started with AWS IoT SiteWise, please visit the developer guide.

About the Author

Avik Ghosh

Avik is a Senior Product Manager on the AWS Industrial IoT team, focusing on the AWS IoT SiteWise service. With over 18 years of experience in technology innovation and product delivery, he specializes in Industrial IoT, MES, Historian, and large-scale Industry 4.0 solutions. Avik contributes to the conceptualization, research, definition, and validation of AWS IoT service offerings.

Mani Nazari

Mani is an experienced systems and development engineer with deep expertise in manufacturing, aerospace, distributed systems, and embedded technologies. He currently works as a System Development Engineer for Ground Support Equipment and Space Mech Assembly on Project Kuiper at Amazon. Mani has over 10 yrs’ experience in software engineering, factory automation, and quality control. Before Amazon, he held engineering/leadership roles at Boeing, developing factory automation systems and architecting APIs.

Joyson Neville Lewis

Joyson is a Sr. IoT Data Architect with AWS Professional Services. Joyson worked as a Software/Data engineer before diving into the Conversational AI and Industrial IoT space. He assists AWS customers to materialize their AI visions using Voice Assistant/Chatbot and IoT solutions