This Guidance helps consumer packaged goods (CPG) companies meet on-time in-full (OTIF) metric requirements by using machine learning (ML) features. These features can improve a CPG company’s ability to deliver products within prescribed delivery times. This Guidance also helps retailers and shippers improve store operations and inventory planning, reduce potential of product loss, and pass on cost savings from operations costs to consumers.
Architecture Diagram
Step 1
Users authenticate with their usernames and passwords, which Amazon Cognito User Pools manages.
Step 2
Amazon Simple Storage Service (Amazon S3) and Amazon CloudFront serve single page application codes to clients.
Step 3
Authenticated clients make API calls to AWS AppSync.
Step 4
AWS Lambda functions serve the resolvers that provide the planner with a route map of supply chain diagram, the manager with a real-time tracking itinerary of the shipment, and the operator with the point of record interface.
Step 5
Amazon Location Service provides maps and location points to visualize the supply chain node’s geographical location.
Step 6
Amazon Neptune stores the graph data of the supply chain so that each node is a location point. The connection between two nodes is known as an edge, and each edge represents the trip a shipment takes from one point to another.
Step 7
Amazon Aurora stores the trip data, itinerary (the set of trips needed to complete the shipment), and the item master list.
Step 8
Export data from Neptune and Aurora into Amazon S3 in a CSV format.
Step 9
The exported dataset is preprocessed to prepare it for model training.
Step 10
Train the model to predict estimated time of arrival (ETA) and item loss rate on each trip.
Step 11
Create an inference endpoint in Amazon SageMaker.
Step 12
Query the estimated value to the endpoint.
Step 1
Users authenticate with their usernames and passwords, which Amazon Cognito User Pools manages.
Step 2
Amazon Simple Storage Service (Amazon S3) and Amazon CloudFront serve single page application codes to clients.
Step 3
Authenticated clients make API calls to AWS AppSync.
Step 4
AWS Lambda functions serve the resolvers that provide the planner with a route map of supply chain diagram, the manager with a real-time tracking itinerary of the shipment, and the operator with the point of record interface.
Step 5
Amazon Location Service provides maps and location points to visualize the supply chain node’s geographical location.
Step 6
Amazon Neptune stores the graph data of the supply chain so that each node is a location point. The connection between two nodes is known as an edge, and each edge represents the trip a shipment takes from one point to another.
Step 7
Amazon Aurora stores the trip data, itinerary (the set of trips needed to complete the shipment), and the item master list.
Step 8
Export data from Neptune and Aurora into Amazon S3 in a CSV format.
Step 9
The exported dataset is preprocessed to prepare it for model training.
Step 10
Train the model to predict estimated time of arrival (ETA) and item loss rate on each trip.
Step 11
Create an inference endpoint in Amazon SageMaker.
Step 12
Query the estimated value to the endpoint.
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.
-
Operational ExcellenceRead the Operational Excellence whitepaper
Each AWS managed services sends its own set of metrics to Amazon CloudWatch, so you can monitor service output for errors.
-
SecurityRead the Security whitepaper
Amazon Cognito provides authentication, authorization, and user management for your web and mobile apps. Your users can sign in directly with a user name and password, or through a third party, such as Amazon, Apple, Facebook, or Google.
-
ReliabilityRead the Reliability whitepaper
Each component in this architecture is designed to maintain availability in the event of disaster. AWS managed services are designed to span multiple Availability Zones, which results in service continuity, even if one Availability Zone fails.
-
Performance EfficiencyRead the Performance Efficiency whitepaper
Scalable and highly available services like Amazon S3, Neptune, and Amazon Redshift are purpose-built for data analytics workloads.
-
Cost OptimizationRead the Cost Optimization whitepaper
This architecture follows a serverless-first approach. The API layer uses Lambda functions, and the database layer uses serverless services such as Neptune or Aurora. Serverless services scale according to load to help ensure you only pay for what you use.
-
SustainabilityRead the Sustainability whitepaper
AWS managed services scale up and down according to business requirements and user traffic to help you use only the minimum resources required. Additionally, serverless components of this architecture automate the process infrastructure management, freeing up resources for other tasks.
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.
Related Content
[Title]
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.