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This Guidance shows how you can drive value by using GE Digital’s Proficy Smart Factory manufacturing execution system (MES) with AWS services. GE Digital Proficy Smart Factory is a Software-as-a-Service (SaaS) offering from General Electric (GE) and is available on the AWS Marketplace. Proficy Smart Factory is a powerful operation management offering built to handle multiple uses, such as efficiency, quality, and production management. It also supports batch analysis, scheduling, digital operations, and industrial data management. Proficy Smart Factory spans on-premises, cloud-based MES capabilities and, when used in conjunction with other AWS services, can help you can gain more insights from manufacturing data through machine learning (ML) and predictive analytics.
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Architecture Diagram
[Architecture diagram description]
Step 1
GE Digital Proficy Smart Factory is a Software-as-a-Service (SaaS) offering from General Electric (GE). It is available on the AWS Marketplace. Proficy Smart Factory is a powerful operation management offering built to handle multiple use cases, such as 1/ Efficiency Management 2/ Quality Management 3/ Production Management 4/ Batch Analysis 5/ Scheduling 6/ Digital Operations and 7/ Industrial Data Management.
Plant and enterprise users can access the SaaS software through web based user interface. GE Proficy Smart Factory offering provides access to Proficy Suite of Software including GE Proficy Plant Applications, GE Proficy Historian and GE Proficy Operations Hub.
Step 2
GE Proficy Historian Collectors running on-premises can collect data from a myriad of industrial data sources, such as Programmable Logic Controllers (PLCs), and Internet of Things (IoT) devices. Industrial data sources can include OPC DA/HDA, OPCUA, GE iFIX SCADA software, Proficy Historian, AVEVA PI Historian, MQTT, ODBC, or AWS IoT Core.
Step 3
GE Proficy Smart Factory can connect to other enterprise systems such, as Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), Warehouse Management System (WMS) and a Laboratory Information Management System (LIMS) through the GE Proficy Plant Applications.
Step 4
Establish a centralized data repository on AWS to facilitate enterprise-wide analytics. Use Proficy Smart Factory to provide contextualized data to GE Proficy Manufacturing Data Cloud (Proficy MDC). Use Proficy Historian’s Parquet export feature to directly ingest data from Proficy Historian into a data lake powered by AWS Lake Formation, AWS Glue Data Catalog, and Amazon Simple Storage Service (Amazon S3).
Step 5
Use AWS artificial intelligence and machine learning (AI/ML) services such as Amazon Forecast, Amazon Lookout for Equipment, and Amazon SageMaker to build, train, and deploy ML models.
Step 6
Use AWS analytics services such as Amazon EMR, Amazon Athena, and Amazon Redshift, along with Amazon QuickSight and Amazon Managed Grafana for data processing and visualization.
Step 1
GE Digital Proficy Smart Factory is a Software-as-a-Service (SaaS) offering from General Electric (GE). It is available on the AWS Marketplace. Proficy Smart Factory is a powerful operation management offering built to handle multiple use cases, such as 1/ Efficiency Management 2/ Quality Management 3/ Production Management 4/ Batch Analysis 5/ Scheduling 6/ Digital Operations and 7/ Industrial Data Management.
Plant and enterprise users can access the SaaS software through web based user interface. GE Proficy Smart Factory offering provides access to Proficy Suite of Software including GE Proficy Plant Applications, GE Proficy Historian and GE Proficy Operations Hub.
Step 2
GE Proficy Historian Collectors running on-premises can collect data from a myriad of industrial data sources, such as Programmable Logic Controllers (PLCs), and Internet of Things (IoT) devices. Industrial data sources can include OPC DA/HDA, OPCUA, GE iFIX SCADA software, Proficy Historian, AVEVA PI Historian, MQTT, ODBC, or AWS IoT Core.
Step 3
GE Proficy Smart Factory can connect to other enterprise systems such, as Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), Warehouse Management System (WMS) and a Laboratory Information Management System (LIMS) through the GE Proficy Plant Applications.
Step 4
Establish a centralized data repository on AWS to facilitate enterprise-wide analytics. Use Proficy Smart Factory to provide contextualized data to GE Proficy Manufacturing Data Cloud (Proficy MDC). Use Proficy Historian’s Parquet export feature to directly ingest data from Proficy Historian into a data lake powered by AWS Lake Formation, AWS Glue Data Catalog, and Amazon Simple Storage Service (Amazon S3).
Step 5
Use AWS artificial intelligence and machine learning (AI/ML) services such as Amazon Forecast, Amazon Lookout for Equipment, and Amazon SageMaker to build, train, and deploy ML models.
Step 6
Use AWS analytics services such as Amazon EMR, Amazon Athena, and Amazon Redshift, along with Amazon QuickSight and Amazon Managed Grafana for data processing and visualization.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
Amazon CloudWatch can be configured with this Guidance to enhance your operational excellence. GE uses CloudWatch to collect and centralize logs, metrics, and application performance data. The GE Proficy Smart Factory application is managed by GE Digital, and these tools provide the capability to monitor and manage the application and the underlying infrastructure.
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SecurityRead the Security whitepaper
Proficy Smart Factory MES authenticates and authorizes application users using a user account and authorization (UAA) service, an open-source identity server project, operated by the Cloud Foundry Foundation. You can integrate your on-premises active directories with this service. This Guidance also encrypts data in transit using TLS for user interfaces, API access, and data collector agents, and it uses AWS Key Management Service (AWS KMS) to encrypt all critical and sensitive data at rest.
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ReliabilityRead the Reliability whitepaper
In this Guidance, GE Digital manages the availability and reliability of the SaaS application for a highly available network topology. We do recommend using a reliable and redundant internet connection to maintain access to the application. Finally, resiliency is a shared responsibility between AWS and you. AWS is responsible for the resiliency of the infrastructure running the services in the AWS Cloud. Your responsibility is determined by the AWS Cloud services you configure.
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Performance EfficiencyRead the Performance Efficiency whitepaper
In this Guidance, GE Digital chooses AWS services for use in the SaaS to deliver a well-performing application. For data and analytics integration, you can use an Amazon S3 data lake along with purpose-built industrial AI/ML services.
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Cost OptimizationRead the Cost Optimization whitepaper
Proficy Smart Factory is priced on a per line basis, so you pay only for each physical line you bring to the MES system. The data repository cost depends on the amount of data ingested from the MES and scales with the size of the manufacturing line.
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SustainabilityRead the Sustainability whitepaper
In this Guidance, GE Digital manages the resources for the SaaS application to achieve optimum usage. This Guidance also uses Amazon S3 data lakes, which scale based on data volume. You should use serverless versions of the analytics services when possible so that you will use only the minimum resources required.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.