AWS for Industries

Semiconductor Supply Chain Resiliency with AWS

Introduction

AWS can help Semiconductor companies with their value chain transformation by migrating the entire PLM-CAD-ERP workflow to the cloud while mitigating resource and performance risks. Data ingested from SAP into AWS-powered data lakes and Amazon SageMaker for machine learning is used for predictive analytics and simulation of various scenarios. For example, to address supply chain roadblocks or demand spikes. Predictions can be visualized via Amazon QuickSight for engineering & business intelligence.

Engineering teams can use Engineering Design Applications & Desktop solutions on AWS to provision tools in secure, standardized design flows, using compliant methodologies. Administrators of this cloud infrastructure also have the ability to centralize and manage the AWS infrastructure in a cost-effective way. Using these solutions helps businesses facilitate remote workforce collaboration, resulting in improved productivity, while also helping them meet security and performance requirements.

Proven Methods

AWS has proven methods to help you establish your data lake and benefit from it. We can help you set it up and get it running in a remarkably short period of time. A data lake on AWS provides rapid time to value for you to get more from your existing systems and investments today. To start building your data lake on AWS, ingest data into Amazon S3 through 11 different AWS Services.

In the next step, you will manage security with  AWS Services, such as AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS). You will then need to catalog the data using the open data format of your choice (like ORC and Parquet) and tag the data in a central, searchable catalog. Now that the data is in S3, you can use AWS Glue to start analyzing your data and putting it to use in minutes. You can also apply traditional SQL analytics by leveraging Amazon Redshift or Amazon Athena, or use Amazon QuickSight for business intelligence. Let’s look at an example reference architecture below:

Figure 1: Reference Architecture – SAP with Engineering & Design Flow

This reference architecture shows how an on-premises data center connects with an AWS cloud Engineering & Design VPC. It shows how on-premises manufacturing execution systems (MES) logs and ERP applications are connected to the cloud using Amazon API Gateway, and how the factory network is connected via AWS IoT Greengrass services. Once the data is ingested and processed, both structure and unstructured, AWS Athena can be used for data preparation tasks like building views of the workload report data and finally with AWS QuickSight, you can query and build visualizations or dashboards to discover insights from your business and engineering metrics.

The reference architecture shows the process and AWS Services that bring data to actionable insights, and provide a decision support platform to improve semiconductor supply chain resiliency. Below is a more generic and high-level view of the AWS services stack built on top of external data, ERP systems and partner eco-system:

Figure 2: Supply Chain Framework

Conclusion

Running a federated ERP system on AWS, a single source of information, in combination with analytics and AWS Data Lake architectures on AWS can provide up to date, real-time insights across all points in the supply chain and improve the ability to address supply and demand problems, better than what can be achieved with legacy on premises systems.

Leveraging AWS services and solutions, supply chain resiliency for the semiconductor eco-system can be well architected and managed. For more information about running your workflows on AWS: AWS Semiconductor and Electronics Resources

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Umar Shah

Umar Shah

Umar Shah is the Head of Solutions at Amazon Web Services focused on the Semiconductor and Hitech industry workloads and has worked in the Silicon Valley for over 26 years. Prior to joining AWS, he was the ECAD manager at Lab126 where he created and delivered business and engineering best practices for Amazon EE teams. He has extensive experience in electronic sub-systems design, EDA design flow optimization, application engineering, project management, technical sales, technical writing, documentation & multimedia development, business development & negotiations, customer relations and business execution.

Mark Duffield

Mark Duffield

Mark Duffield is a Worldwide Tech Leader at Amazon Web Services, focusing on the semiconductor industry. Prior to joining AWS, he was a High Performance Computing SME at IBM, and designed multi-petabyte solutions at DDN Storage. He has deep experience with HPC, cluster computing, enterprise software development, and distributed file systems. He architects solutions in several verticals, to include electronic design automation, weather modeling and forecasting, manufacturing, and scientific simulations.

Ratna Dasari

Ratna Dasari

Ratna Dasari is a Senior Solutions Architect in the Enterprise SA team at AWS. She works closely with semiconductor customers helping them define and implement their cloud and digital transformation strategies. Ratna worked in IT organizations at semiconductor companies for over 20 years. She has extensive experience in leading, architecting and delivering innovative solutions at scale to generate business value.