• 

EcoChain: AI for Ethical Supply Chains

Inspiration

We were inspired by the growing consumer and regulatory demand for corporate transparency. Major brands face immense reputational and financial damage from hidden supply chain issues like forced labor, excessive carbon emissions, or environmental negligence. We wanted to build a solution that moves sustainability analytics from static, historical reporting to predictive, proactive intelligence, empowering businesses to make ethical sourcing decisions before a crisis occurs.

What it does

EcoChain is an AI-powered analytics platform built on Tableau Next that predicts sustainability risks in global supply chains. It ingests data from supplier audits, IoT sensors, and shipment logs to:

Predict Anomalies:

Uses a trained LSTM model to flag potential violations (e.g., a sudden spike in a supplier's carbon footprint or a drop in audit scores).

Visualize Risk:

A Tableau Next dashboard visualizes supplier sustainability scores, maps risk hotspots, and tracks KPIs against UN Sustainable Development Goals (SDGs).

Trigger Actions:

Integrates with Slack to automatically alert procurement teams of high-risk suppliers, enabling rapid investigation and intervention.

How we built it

Our architecture leveraged the full power of the Salesforce Platform and modern data science tools:

Data Ingestion & Unification:

We used Salesforce Data Cloud to create a unified profile for each supplier, merging structured data (audit reports from Salesforce CRM) with semi-structured IoT sensor data from shipping containers.

AI/ML Modeling:

In Google Colab, we built a time-series forecasting model using TensorFlow and LSTM networks to analyze historical data and predict future anomaly scores for key metrics like CO₂e and waste_output.

The anomaly score \(A_t\) for a supplier at time \(t\) is calculated using the reconstruction error from the LSTM autoencoder: \(A_t = || \mathbf{x}_t - \mathbf{\hat{x}}_t ||^2\)

Analytics & Visualization:

We connected the predicted results back to Tableau Next to build an interactive dashboard. We utilized Tableau's Einstein AI capabilities to generate natural language explanations for the predicted risks.

Demo & Simulation:

We built a lightweight Gradio interface to simulate the impact of changing variables (e.g., "What if we switch from air freight to rail for Supplier X?") and show the corresponding change in risk predictions, making the AI interpretable for business users.

Workflow Automation:

We used Slack API integration to send alerts and created an Apex trigger to automatically update supplier risk statuses.

Challenges we ran into

Data Synthesis: As real-world supply chain data is often proprietary, we had to meticulously generate a realistic synthetic dataset for training and demonstration, ensuring it reflected complex, real-world patterns.

Platform Integration:

Streamlining the data flow between Google Colab, Data Cloud, and Tableau Next was challenging. We spent significant time optimizing the API calls and data schemas to ensure seamless integration.

Model Interpretability:

Translating the complex output of an LSTM model into a simple, actionable risk score and a clear narrative for the dashboard was a significant hurdle that we overcame with careful feature engineering and Einstein AI's narrative capabilities.

Accomplishments that we're proud of

Creating a fully functional, end-to-end prototype that demonstrates a practical and impactful use case for Tableau Next and AI.

Successfully integrating a custom-built AI model from Google Colab into the Salesforce ecosystem, proving the flexibility of the platform.

Designing an intuitive user experience where powerful analytics are made accessible and actionable for non-technical decision-makers through Gradio and Slack.

What we learned

We deepened our expertise in Salesforce Data Cloud and its capabilities for unifying disparate data sources.

We learned to leverage Tableau Next's advanced analytics features, moving beyond traditional BI to embedded predictive modeling.

We gained practical experience in building and operationalizing a time-series forecasting model and integrating it into a live application stack.

The hackathon reinforced the importance of designing for the end-user; the most advanced AI is useless if its insights cannot be easily understood and acted upon.

What's next for EcoChain: AI for ethical supply chains

Blockchain Integration: Utilize Salesforce Blockchain to create an immutable, transparent ledger of supplier audits and carbon credit transactions, adding a new layer of verifiable trust.

Supplier Portal:

Build a Experience Cloud site where suppliers can view their own risk scores, collaborate on corrective action plans, and submit verified documentation.

Carbon Credit Marketplace:

Integrate with Salesforce Commerce Cloud to allow companies to automatically purchase carbon offsets or source from pre-vetted sustainable suppliers directly through the platform, closing the loop on sustainable sourcing.

Built With

• apex
• einstein-ai
• gradio"],"platforms":-["salesforce-platform
• keras
• languages":-["python"
• latex"]
• markdown
• salesforce
• salesforce-data-cloud
• salesforce-objects"],"apis":-["slack-api
• salesforce-rest-api"],"othertechnologies":-["lstm-neural-networks
• saql"],"frameworks":-["tensorflow
• slack"],"cloudservices":-["google-colab"],"databases":-["salesforce-data-cloud
• soql
• tableau-cloud
• tableau-next
• tableau-rest-api
• time-series-forecasting