Hannover Messe 2024: AWS Unveils Purpose-Built “e-Bike Smart Factory“ Showcase

Introduction

Today at Hannover Messe industrial trade fair, Amazon Web Services (AWS) unveiled an immersive showcase (Hall 15, Booth D76) that is captivating attendees with a compelling narrative of how manufacturers can transform their operations and drive innovation with AWS for Industrial purpose-built services, generative AI, and partner solutions.

Through the lens of a fictitious e-bike manufacturer, “AWSome e-Bikes,” the demo chronicles the transformative journey of AWSome e-Bikes overcoming inventory shortages, production challenges, and quality control issues by working together with AWS and AWS Partners. By harnessing AWS’s supply chain capabilities, “AWSome e-Bikes” is able to forecast demand accurately, align inventory, and enhance supplier collaboration. AWS Industrial IoT offerings further enable real-time operational data monitoring, enhancing visibility into metrics like Overall Equipment Effectiveness (OEE) for continuous improvement. Additionally, the demo illustrates how AWS purpose-built services for Artificial Intelligence (AI) and Machine Learning (ML) not only help detect anomalies and predict equipment failures but also automate inspection processes. With generative AI-powered shop floor assistants and defect detection model training, “AWSome e-Bikes” accelerates mean time to resolve (MTTR) equipment failures and boosts operational efficiency, productivity, and product quality. Ultimately, this showcase emphasizes the synergy between AWS and AWS Partners, highlighting their integrated effort to address unique manufacturing challenges and enhance industrial resilience, efficiency, and innovation.

In addition to an in-depth overview of the “e-Bike Smart Factory” demo, this blog post shares real customer success stories, highlighting how leading manufacturers have implemented AWS services and partner solutions to achieve significant enhancements in operational agility, efficiency, and time to market.

e-Bike Smart Factory Demo Overview

The “e-Bike Smart Factory” demo at the AWS booth vividly brings to life the journey of “AWSome e-Bikes,” a fictitious e-bike manufacturer facing significant challenges as they gear up for a crucial peak season. With a large order of e-bikes pending for a bike-sharing customer, the company confronts daunting challenges such as faulty products reaching the distribution center, leading to potential loss of trust in their brand and customer dissatisfaction. Exacerbated by unplanned machine downtime and a shortage of raw materials, these issues threaten to disrupt their operations and revenue significantly.

Determined to overcome these obstacles, “AWSome e-Bikes” turns to AWS and AWS Partners for a solution.

Preview video of the “e-Bike Smart Factory” demo produced by AWS Partner, Denali Advanced Integration

Supply Chain Management

In response to these critical challenges, “AWSome e-Bikes” leverages AWS Supply Chain and a supply chain-focused generative AI assistant powered by Amazon Q to enhance visibility across its multi-tier supply network and accelerate insight. This allows them to optimize plans, rebalance inventory, and align material supply with forecasted demand. Utilizing AWS Supply Chain Demand Planning, they accurately forecast demand across bike categories, channels, and SKUs factoring promotions, seasonality, and historical data. This ensures adequate finished goods and raw material inventory to meet forecasted demand.

AWS Supply Chain Insights provides inventory availability visibility, identifies potential risks like stock outs, and recommends rebalancing options. “AWSome e-Bikes” also leverages AWS Supply Chain N-Tier Visibility and Sustainability capabilities to collaborate with suppliers, synchronize plans and forecasts for critical components, and promote sustainability by tracking emissions data. This heightened transparency, accuracy, agility, and production alignment, coupled with the Amazon Q assistant, empowers AWSome e-Bikes to effectively manage inventory, overcome faulty product and material shortage challenges, and meet peak customer demand while optimizing supply chain operations.

Industrial Edge

In the factory, “AWSome e-Bikes” deploys AWS IoT SiteWise Edge from the Siemens Industrial Edge Marketplace directly onto a Siemens Industrial Edge Device already connected to key operational technology (OT) within the work cell, including WAGO PLCs, robotic arms, and RFID readers along with laser distance measurement sensors by Pepperl+Fuchs. This streamlined approach for factory connectivity removes layers of configuration, reduces cost associated with transmitting OT data to the AWS cloud (i.e., AWS IoT SiteWise) via protocols such as OPC UA and MQTT, and significantly improves real-time visibility into vital operational metrics like production line efficiency, asset performance, and quality control.

Generative AI Equipped with these insights, “AWSome e-Bikes” is able to refine manufacturing processes, quickly identify and rectify defects, minimize downtime through proactive maintenance, and maintain accurate inventory levels. Using Advantech‘s all-in-one industrial Human Machine Interface (HMI) displays, shop floor operators interact with a generative AI assistant powered by Amazon Bedrock which analyzes real-time data and equipment conditions, providing targeted troubleshooting recommendations to minimize unexpected downtime and optimize equipment performance. The generative AI assistant is embedded within a digital twin displaying a virtual representation of the entire operation built using AWS IoT TwinMaker and Matterport to provide additional context as operators remotely navigate around the facility, zooming into 3D models of production line equipment and components to view real-time telemetry data.’

Predictive Maintenance and Quality Detection

To further optimize their operations, “AWSome e-Bikes” deploys Amazon Lookout for Equipment and Amazon Monitron for advanced predictive maintenance and monitoring, transitioning from traditional maintenance schedules to a more dynamic, data-driven approach. This shift not only reduces downtime, but also enables the deployment of automated work orders triggered by real-time asset condition changes. Additionally, quality rejections plummet after implementing the Denali Automated Quality Inspection, powered by Amazon Lookout for Vision, to find visual defects on the e-bikes. Eurotech’s EdgeAI server seamlessly connects the Denali Automated Quality Inspection application with AWS services, enabling efficient and secure transfer of defect detection images and data from the factory floor to the AWS cloud for analysis and storage. “AWSome e-Bikes” also uses generative AI tools like Amazon SageMaker to produce unique photorealistic images from text and image prompts, augmenting their training data and enabling highly accurate defect detection.

Security

Recognizing the critical need for secure operations, “AWSome e-Bikes” also incorporates Claroty on AWS to perform asset discovery, vulnerability management, and secure device management on the Operational Technology (OT) network. Coupled with AWS Security Hub, this robust security framework ensures comprehensive monitoring and protection of their operations against cyber threats.

Industrial Data Fabric

By integrating industrial operations with business intelligence through a governed Industrial Data Fabric using Amazon DataZone “AWSome e-Bikes” Plant Managers, Shift Supervisors, and Quality Managers are able to catalog, discover, and share data stored across AWS, on premises, and third-party sources for insights into performance, availability, and quality standards. This allows them to not only meet but exceed their delivery commitments without compromising quality, thus restoring customer confidence and solidifying their market leadership. This transformative approach, powered by AWS and AWS Partners, showcases how digital innovation can effectively address complex manufacturing challenges, ensuring sustainable business growth and operational excellence.

How AWS customers are transforming manufacturing operations

The “e-Bike Smart Factory” demo brings to life the transformative power of AWS and AWS Partners in addressing critical manufacturing challenges. While the story of “AWSome e-Bikes” is fictitious, many leading manufacturers around the world have embarked on similar digital transformation journeys, leveraging AWS to optimize their operations and drive innovation.

From supply chain resilience to predictive maintenance, automated quality inspection, and connected product intelligence, AWS customers across industries are realizing tremendous value through the implementation of purpose-built industrial solutions. The following stories illustrate how the capabilities highlighted in the “e-Bike Smart Factory” demo translate into real-world impact.

Predictive Maintenance at Toyota: Reducing Unplanned Downtime with AWS

The Maintenance Team at Toyota Motors North America (TMNA) struggled with time-consuming and costly efforts to prevent unplanned downtime across their manufacturing sites, each operating over 200 CNC machines running 24/7. To address this, they deployed a predictive maintenance solution leveraging AWS IoT SiteWise to collect and analyze equipment data, and Amazon Lookout for Equipment to build ML models that can predict failures days in advance. Since implementing this solution, TMNA has prevented dozens of potential incidents and hours of unplanned downtime.

The AWS-powered predictive maintenance system has improved TMNA’s operational availability by 10% compared to the previous 12-month average. By transitioning to a data-driven, proactive approach, TMNA maximizes asset utilization, reduces operational expenditures, and ensures consistent manufacturing uptime across their facilities.

“The Operation Availability of our focus line was between 78-82%, incurring around 40 hours of downtime each month. With the help of AWS, we have found many problems in our machines, if left unnoticed would lead to critical failure. Now our OA is 92% and the downtime is around 20 hours!” – Braden Burford, Sr. Maintenance Engineer, Toyota

Retaining Institutional Knowledge with Digital Twins at INVISTA

INVISTA, a leading polymer materials company, was facing the risk of losing invaluable institutional knowledge as long-tenured employees neared retirement. To retain and democratize this expertise across the organization, they turned to AWS and AWS Partner Matterport to develop immersive 3D digital twins of INVISTA’s manufacturing facilities, integrating Matterport’s spatial data capture and visualization capabilities with the contextual operational insights provided by AWS IoT TwinMaker. This established a live, crowdsourced knowledge repository where employees can virtually access the production floor, troubleshoot issues independently, and even contribute their own learnings.

The digital twin solution has catalyzed significant gains for INVISTA’s operations. With rapid data access directly within the virtual environment, the company has seen drastic reductions in unplanned downtime – moving from hours to just seconds for issue resolution. Looking ahead, INVISTA plans to unite digital twins with augmented reality, virtual reality, and generative AI to create a cohesive smart factory ecosystem. This digital transformation will unlock new levels of manufacturing efficiency, quality control, and competitive advantage while preserving critical institutional knowledge across the workforce.

“Thanks to Matterport, our experts can use AWS IoT TwinMaker to troubleshoot remotely—improving operational response time.” – Dane Laughlin, Director, Advanced Learning Capabilities, INVISTA

Boosting Operational Efficiency with AWS Computer Vision at Tyson Foods

As one of the world’s largest food producers, Tyson Foods processes millions of pounds of food weekly across over 100 facilities globally. To maintain high quality while maximizing operational efficiency at this immense scale, the company sought to automate time-consuming manual processes like inventory counting and machine inspections. Tyson Foods turned to AWS to quickly incorporate computer vision (CV) powered by ML into production lines.

In collaboration with the Amazon ML Solutions Lab, Tyson Foods developed and deployed CV/ML models using services like Amazon SageMaker and AWS Panorama. One model automatically counts chicken trays passing quality checks, providing near-real-time production quantity insights to supervisors. Another leverages Amazon Lookout for Vision to detect anomalies in product carriers, avoiding unplanned downtime from faulty components. Deploying this solution at edge with AWS Panorama appliances is estimated to save 15,000 hours of skilled labor annually at a single facility alone. By innovating with AWS’s cutting-edge CV and ML services, Tyson Foods continues optimizing processes to reduce waste, plan effectively, and drive sustainable operational excellence.

“These solutions help us use exactly what we need by understanding the true demand and optimizing inventory so that we can effectively plan and reduce waste.” – Barret Miller, Senior Manager of the Emerging Technology Team at Tyson Foods

As a pioneering adopter of production CV/ML, Tyson Foods exemplifies how manufacturers can leverage AWS to boost quality, safety, and profitability through computer vision-powered industrial optimization.

Combating False Rejects with Generative AI at Merck Pharmaceuticals

Merck, an American multinational pharmaceutical company, faced a common industry challenge – the occurrence of false rejects during critical drug inspection processes. To holistically investigate this issue, Merck needed to ingest and analyze product genealogy, process, quality, and real-time inspection machine data from disparate manufacturing systems across their global operations.

Merck turned to AWS to build an AI/ML platform to solve this complex problem. Using services like AWS Glue, Amazon Kinesis, Amazon Redshift, and Amazon QuickSight, they enable real-time data processing, analytics and visualization. Their AI/ML platform runs on Amazon SageMaker, leveraging AWS DataSync to incorporate defect image data from inspection machines worldwide. Merck employs innovative generative AI approaches like Generative Adversarial Networks (GANs) and Variational Autoencoders to synthetically generate training data for complex defects lacking sufficient real samples. This advanced use of AWS AI/ML has enabled Merck to reduce overall false rejects by over 50% across product lines, significantly improving drug availability, increasing yields, enabling rapid investigations, and driving time/cost savings – all while getting life saving medicines safely into patients’ hands faster.

“We use generative AI approaches and generative models like GANs and Variational Autoencoders to develop synthetic defect image data for complex defects where we have limited training data. The insights gained have helped us to understand root causes of rejects, optimize processes, and reduce overall false rejects across various product lines by more than 50%. It is gratifying and motivating to know that the work we are doing has a direct impact on patient lives in terms of improving the availability of life-saving medicines and vaccines.” – Nitin Kaul, Associate Director, IT Architecture, Merck & Co.

Accelerating Expertise with a Generative AI-Powered “Virtual Engineer” at Vivix Vidros Planos

As Brazil’s only 100% nationally-owned flat glass manufacturer, Vivix Vidros Planos faced challenges in rapidly training technicians and preserving operational knowledge across their experienced workforce. To expedite the expertise acquisition process and drive knowledge democratization, Vivix sought an innovative solution powered by generative AI.

Vivix turned to AWS and the advanced Amazon Bedrock and Claude-2 platforms to create a “Virtual Engineer” – an AI assistant capable of real-time troubleshooting guidance and expertise dissemination. By leveraging generative AI models trained on Vivix’s proprietary data, the Virtual Engineer enables technicians to acquire critical operations and maintenance skills in just months versus years through traditional training. Vivix expects this acceleration of workforce readiness to substantially reduce costs from line downtime, improve furnace asset availability, streamline onboarding of new hires, and foster greater autonomy across their technical teams.

“With generative AI, we’ve been able to compress our technician training process from years to just a few months. This is allowing us to expand our maintenance practices efficiently even as we bring in many new hires.” – Aristotle Third Neto, Industrial Transformation Manager at Vivix Vidros

By innovating with AWS’s cutting-edge generative AI services, Vivix is preserving institutional knowledge while rapidly upskilling their workforce – optimizing operations and positioning the company for sustainable growth.

Better Together with AWS Partners

The “e-Bike Smart Factory” demo at Hannover Messe 2024 highlights AWS for Industrial purpose-built capabilities in combination with offerings from various industry partners. These collaborations are essential for delivering a holistic and robust solution that addresses the unique challenges in manufacturing. Here’s an overview of AWS Partners involved in the demo and their contributions:

Denali Advanced Integration acted as the systems integrator for the demo, bringing their expertise in designing and implementing complex automated solutions. They were also instrumental in implementing the automated quality inspection system powered by AWS, utilizing machine learning to detect defects at the final assembly stage. This integration underscores the collaboration between AWS and Denali to drive quality assurance and operational efficiency.

Siemens plays a pivotal role in breaking IT-OT data silos. Through the deployment of AWS IoT SiteWise Edge on Siemens Industrial Edge Devices, this joint offering allows manufacturers to collect and process data directly at the source. This approach eliminates layers of complexity, enabling more straightforward and quicker access of OT data in the cloud and enhancing real-time operational efficiency and data transparency.

Eurotech provides a rugged and reliable edge computing device and a versatile platform that offers a combination of computational performance, storage capacity, and network bandwidth for workloads that include data acquisition and fusion, AI inference and data logging. Their edge servers and framework are essential for the seamless transfer of data from the defect detection application to the AWS cloud, ensuring security and reliability in harsh industrial environments.

Matterport enhances the digital twin capabilities within the demo by providing high-fidelity 3D modeling of the factory environment. Their integration with AWS IoT TwinMaker allows for immersive visualization and interaction with real-time operational data. By overlaying sensor data onto 3D visual representations, Matterport’s technology aids in better space management and operational planning, making complex industrial environments more comprehensible and navigable.

WAGO contributes its expertise in automation and interface electronics, particularly through advanced control systems and interconnect solutions. Their modular and scalable components, such as programmable logic controllers and fieldbus systems, ensure seamless automation and enhance the factory’s adaptability to changes, which is crucial for industrial transformation readiness.

Pepperl+Fuchs equipped the showcase with a diverse array of sensors, RFID readers, and laser distance measurement devices integrated through IO-Link masters and gateways that provide cloud connectivity and data integration capabilities. These components support real-time communication and diagnostics, facilitating predictive maintenance and operational data analysis that enhance asset management and factory automation.

Advantech provided robust industrial solutions for the “e-Bike Smart Factory” demo, featuring an all-in-one HMI with a widescreen touch display and aluminum alloy enclosure for durability and easy maintenance. Their compact mini PC, designed for thermal dissipation in rugged environments, supports real-time data acquisition and connectivity and enhances operational efficiency and data management throughout the factory.

The synergy between AWS’s cloud and AI capabilities with the hardware and software solutions provided by AWS Partners results in a powerful demonstration of a fully integrated smart factory. This collaborative model not only enhances operational efficiencies but also propels the manufacturing industry towards a more innovative and secure future.

Getting Started with AWS

The “e-Bike Smart Factory” demo at Hannover Messe 2024 illustrates how manufacturers can leverage AWS’s purpose-built industrial offerings to transform operations across their value chain. While the demo narrative is fictional, it reflects the very real journeys companies like Toyota, INVISTA, and Merck are undertaking with AWS to optimize productivity, quality, asset utilization, and supply chain resilience.

From reducing unplanned downtime through predictive maintenance, to enhancing quality control with generative AI, to democratizing knowledge via digital twins – AWS empowers manufacturers to unlock new levels of efficiency and competitive advantage. By establishing secure industrial data architectures that provide unprecedented visibility, manufacturers can contextualize disparate data sources to drive continuous improvement. With its breadth of purpose-built industrial services, AI/ML capabilities, a robust partner network, and culture of innovation, AWS is the trusted partner enabling manufacturers to outmaneuver uncertainty and lead their industrial transformation.

Visit the AWS booth at Hannover Messe (Hall 15, Booth D76) to see the demo in action, or learn more about AWS for Industrial and get started transforming your operations and processes at https://aws.amazon.com/industry/industrial/.