Automated predictive demand analytics for alternative fuels

Purpose

1.1. Provide precise, automated forecasting of demand for alternative fuels at retail energy stations, using historical data, external market indicators, weather, and real-time inventory; enable accurate procurement, optimized stock, reduced shortages/overages, and responsive supply chain operations.
1.2. Integrate disparate data sources (POS, IoT tanks, market APIs) to correlate trends, seasonality, and anomalies for all alternative fuels.
1.3. Trigger automated decisioning for purchase orders, stock transfers, and price adjustments based on demand predictions.

Trigger Conditions

2.1. Daily/weekly scheduled batch jobs.
2.2. Real-time low inventory thresholds crossed.
2.3. Arrival of new external market data (spot prices, local events).
2.4. Sudden weather changes or emergency notifications.
2.5. Monthly/quarterly strategic review cycles.

Platform Variants

3.1. Azure Machine Learning
• Feature/Setting: Deploy predictive model via REST API endpoint; configure data pipeline for scheduled retrain.
3.2. AWS Forecast
• Feature/Setting: Create Predictor for fuel sales; configure invoke endpoints for real-time inference.
3.3. Google AutoML Tables
• Feature/Setting: Batch prediction job configuration; automated data import from BigQuery.
3.4. Databricks
• Feature/Setting: notebook job schedule; real-time MLflow model serving.
3.5. Snowflake
• Feature/Setting: Snowpark Python UDF for prediction, automated Stream reads from supply events.
3.6. RapidMiner
• Feature/Setting: Real-time scoring on API trigger; historical data enrichment pipeline.
3.7. KNIME
• Feature/Setting: Workflow for batch prediction; trigger via REST API on inventory update.
3.8. DataRobot
• Feature/Setting: Deployment of prediction endpoint; scheduled retrain jobs post-data arrival.
3.9. Alteryx
• Feature/Setting: Weekly scheduled analytics workflow; output to inventory dashboard.
3.10. SAP Integrated Business Planning
• Feature/Setting: Predictive analytics view configured for fuel types; forecast alerts on threshold.
3.11. Microsoft Power Automate
• Feature/Setting: Trigger Power BI-driven demand forecast alert; auto-issue purchase order flow.
3.12. Tableau Prep
• Feature/Setting: Scheduled refresh of demand analytics; trigger Slack alerts on anomaly detection.
3.13. IBM Watson Studio
• Feature/Setting: Model deployment with Watson Machine Learning REST API; data pipeline scheduling.
3.14. Oracle Analytics Cloud
• Feature/Setting: Scheduled predictive model runs; push inventory restock notifications to ERP.
3.15. Salesforce Einstein Analytics
• Feature/Setting: Dataset linking with fuel sales; configure predicted demand dashboards.
3.16. Qlik Sense
• Feature/Setting: Scheduled AutoML predictions; Qlik Alerting for low inventory events.
3.17. H2O.ai
• Feature/Setting: AutoML model serving as REST API; real-time trigger integration with inventory updates.
3.18. OpenAI API
• Feature/Setting: Use function-calling for external market event extraction; time-series trend analysis.
3.19. Zoho Analytics
• Feature/Setting: Automated scheduled models; alert rules on spike detection.
3.20. Anaplan
• Feature/Setting: Predictive demand module setup; workflow for approval on forecast exceeding targets.

Benefits

4.1. Enables precise supply planning and minimizes stockouts.
4.2. Reduces excess inventory and working capital tied up in stock.
4.3. Reacts automatically to real-world events, driving agility.
4.4. Improves vendor negotiation via data-driven forecasting.
4.5. Enhances customer satisfaction by maintaining steady supply of alternative fuels.
4.6. Ensures regulatory compliance with automated documentation workflows.