In the automotive industry, OEMs often use a hybrid cloud strategy for data storage. For example, Connected Mobility data (vehicle and telematics) might reside in AWS's US-West-2 region, while parts manufacturing data is stored in GCP's US-Central-1. Additionally, Supplier Quality data could be hosted in yet another cloud or region, such as Azure US-West-2. This distribution is merely illustrative, emphasizing a strategy to avoid reliance on any single public cloud provider. However, this multi-cloud approach introduces several challenges, particularly for the customer. One notable challenge is conducting "Vehicle Quality Root Cause Analysis (RCA)."

Each vehicle generates extensive telemetry data from thousands of sensors, which the onboard diagnostics systems translate into Diagnostic Trouble Code (DTC) errors—alerts that appear on the dashboard as warnings. For this use case, an OEM begins with DTC error data as an entry point for Quality RCA.

Here are some of the key questions that arise during this process:

Is this error code pointing towards a specific part? or is it affecting something fundamental? Is this error code showing any pattern? Are all vehicles affected or a specific vehicle platform is affected? What about a specific model year? or maybe a specific configuration within a platform such as sedan or suv? Which regions are affected? All regions / specific regions?

In early 2023, Snowflake launched the Manufacturing Data Cloud to help customers simplify their data operations and management and unleash the power of AI to improve supply chain performance, power smart manufacturing, and implement quality control from connected products. Industry 4.0 technologies, including IIoT, machine learning, and advanced data analytics. One major challenge seen in Manufacturing operations is striving to deliver the highest quality during every stage of the production or assembly process. There is a strong desire to improve efficiency, accuracy, and consistency in identifying defects if any in the production process. By automating quality control, organizations aim to reduce human error, increase the speed of inspections, and ensure that products meet the desired standards.

The Cross-region data sharing from Snowflake offers a robust solution to the multi-cloud challenges by providing a unified, scalable, and secure data platform. By centralizing data storage and processing in Snowflake, OEMs can streamline the RCA process, enhance collaboration, and derive meaningful insights, ultimately leading to better decision-making and improved vehicle quality.

Prerequisites

What You'll Learn

What You'll Need

What You'll Build

This section will walk you through creating various objects

High-Level Workflow

In a real-world situation with Snowflakes Data Sharing each of the data will be shared from a particular region and cloud provider. In this scenario for sake of simplicity we will assume the data is available in a single cloud provider, AWS S3 bucket. We will be downloading the data and storing in tables in a Snowflake account and use that for further analytics.

Step 1. - Clone GitHub repository.

Step 2. - Run the code below code in Snowsight SQL Worksheet that sets up a database and warehouse.

Create a database that will be the centralized storage for all the sources of data.

USE ROLE SYSADMIN;
create database connected_mobility_rca;

CREATE OR REPLACE WAREHOUSE cnctd_mblty_wh WITH 
WAREHOUSE_TYPE = standard WAREHOUSE_SIZE = Medium
AUTO_SUSPEND = 5 AUTO_RESUME = True;
USE WAREHOUSE cnctd_mblty_wh;

Once the database and the warehouse are created, now let us proceed to the notebook execution where the data loading happens.

Step 3. The Notebook is available to download from the notebook folder in the git repository.

Notebook Walkthrough :

After successful completion of the data loading in the notebook, you are all set to carry advanced analytics in Streamlit in Snowflake App in next section.

We will now build a quick multi-page Streamlit in Snowflake app. The app and necessary files are present in the cloned repository streamlit folder in the git repository.

In a real-world scenario, the automobile organization ,supplier company and manufacturing company typically host their data in their own Snowflake accounts that is based on 3 different Cloud providers like AWS,Azure and GCP that is supported. We will carry the same assumption and the data and maps displyed in the apps will be in tune to that situation rather than the original data ingestion process which was based out of 1 cloud provider in 1 region.

Homepage App

This page displays a map with the locations of data sources. It uses Pydeck for the visualization.

Connected Mobility Data App

This page serves to display and analyze connected mobility data. Overall in this page the user can :

Manufacturing Data App

This page serves to display and analyze manufacturing data related to vehicle battery parts. This page provides insights into the suppliers, manufacturing facilities, battery types, and components involved in the production process. Let's break down its functionality:

Supplier Quality App

This page provides a detailed analysis of battery part numbers, their performance metrics, and the corresponding supplier information. Let's break down its functionality:

RCA Bot

This page provides a chat interface for users to input natural language queries and get corresponding SQL queries, which can then be executed to retrieve and visualize data. Here's a detailed breakdown of its functionality:

Some of the LLM options include:

Vehicle Quality Root Cause Analysis is a complex yet critical process for OEMs to ensure the reliability and safety of their vehicles. The Cross-region data sharing capability from Snowflake, offers a robust solution by providing a unified, scalable, and secure data platform. By centralizing data storage and processing in Snowflake, OEMs can streamline the RCA process, enhance collaboration, and derive meaningful insights, ultimately leading to better decision-making and improved vehicle quality.

What You Learned

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