Mensah M. Alkebu-Lan

malkebu-lan Comments

Smarter Retail Forecasting with Alteryx Designer + Databricks Data Intelligence Platform

Introduction: The Retail Forecasting Imperative

Retailers in the Northeast—from SoHo boutiques to Cherry Hill outlets—are navigating a volatile landscape of shifting consumer demand, supply chain disruptions, and omnichannel expectations. Static spreadsheets and siloed systems no longer suffice. To stay competitive, retailers must implement real-time, intelligent forecasting systems that integrate data across POS, ERP, and logistics platforms.

This article outlines a technical implementation of a modern demand planning architecture using Alteryx Designer Cloud, Databricks Lakehouse, and 3PL warehouse data, enabling SKU-level forecasting, anomaly detection, and agile inventory optimization.

Solution Architecture Overview

The proposed architecture consists of five core layers:

Data Ingestion Layer
Lakehouse Storage & Processing Layer
Analytics & Forecasting Layer
Machine Learning Layer
Reporting & Automation Layer

Each layer is powered by a combination of cloud-native tools and open APIs to ensure scalability, modularity, and real-time responsiveness.

Data Ingestion Layer

Sources:

Cloud-based POS systems (e.g., Square, Shopify, Lightspeed)
3PL Warehouse Management Systems (e.g., ShipBob, Flexe, Deliverr)
NetSuite ERP (via SuiteAnalytics Connect or REST APIs)
External Signals (weather APIs, event calendars, Google Trends)

Implementation:

Use Databricks Auto Loader to ingest streaming POS and 3PL data from cloud object storage (e.g., AWS S3, Azure Blob).
Configure structured streaming jobs in PySpark to parse JSON/CSV payloads from POS terminals and WMS systems.
Use Alteryx Input Data tool to pull ERP data via JDBC or REST connectors.
Normalize timestamps, SKU identifiers, and location metadata using Delta Live Tables (DLT).

from pyspark.sql.functions import col, to_timestamp

df = spark.readStream.format("cloudFiles") \
    .option("cloudFiles.format", "json") \
    .load("/mnt/pos-data")

df_clean = df.withColumn("timestamp", to_timestamp(col("sale_time"))) \
             .withColumnRenamed("store_id", "location_id")

Lakehouse Storage & Processing Layer

Platform: Databricks Lakehouse

All ingested data is stored in Delta Lake tables, partitioned by store_id, SKU, and date. This enables fast querying and scalable ML training.

Data Modeling:

Bronze Layer: Raw ingestion from POS, ERP, and 3PL
Silver Layer: Cleaned, normalized, and joined datasets
Gold Layer: Aggregated metrics for forecasting (e.g., daily sales, inventory levels, fulfillment latency)

Example Join Logic:

CREATE OR REPLACE TABLE gold_forecast_input AS
SELECT
  pos.store_id,
  pos.SKU,
  pos.sale_date,
  SUM(pos.quantity) AS total_sales,
  inv.stock_level,
  erp.promo_flag,
  weather.temp,
  weather.precip
FROM silver_pos pos
JOIN silver_inventory inv ON pos.SKU = inv.SKU AND pos.store_id = inv.store_id
JOIN silver_erp erp ON pos.store_id = erp.store_id
JOIN silver_weather weather ON pos.sale_date = weather.date
GROUP BY pos.store_id, pos.SKU, pos.sale_date, inv.stock_level, erp.promo_flag, weather.temp, weather.precip

Analytics & Forecasting Layer

Platform: Alteryx Designer Cloud

Business analysts use Alteryx Designer Cloud to build forecasting workflows without writing code. These workflows connect directly to Databricks via Partner Connect.

Workflow Components:

Input Data Tool: Connects to Databricks SQL Warehouse
Time Series Forecast Tool: Applies ARIMA, ETS, or Prophet models
Formula Tool: Derives features like days_to_stockout, promo_effect
Playbooks: Prebuilt templates for retail forecasting
Magic Reports: Auto-generates visualizations and dashboards

Example Forecasting Logic:

Forecast daily demand for each SKU using Prophet
Adjust for promotions and weather
Output forecast confidence intervals

from prophet import Prophet

df = spark.sql("SELECT sale_date, total_sales FROM gold_forecast_input WHERE SKU = 'ABC123'")
df.rename(columns={'sale_date': 'ds', 'total_sales': 'y'}, inplace=True)

model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

Alteryx wraps this logic in a visual workflow, allowing non-technical users to tweak parameters and rerun models.

Machine Learning Layer

Platform: Databricks Mosaic AI

For advanced use cases, data scientists use Mosaic AI to train and deploy ML models for:

Inventory optimization
Anomaly detection
Fulfillment prediction

Model Types:

XGBoost for predicting stockouts
LSTM for multi-step time series forecasting
Isolation Forest for detecting demand anomalies

Model Deployment:

Register models in MLflow Model Registry
Deploy as REST endpoints via Databricks Model Serving
Trigger predictions from Alteryx workflows or external apps

import mlflow
import xgboost as xgb

model = xgb.train(params, dtrain)
mlflow.xgboost.log_model(model, "stockout_predictor")

Reporting & Automation Layer

Tools:

Alteryx Magic Reports for auto-generated dashboards
Power BI / Tableau for executive reporting
Databricks SQL Alerts for threshold-based notifications

Automation:

Schedule Alteryx workflows via Alteryx Scheduler
Use Databricks Workflows to orchestrate ETL and ML pipelines
Trigger alerts when forecast deviates from actuals by >15%

Integrating 3PL Data for Agility

Why It Matters:

3PL providers offer real-time visibility into:

Inventory levels
Fulfillment capacity
Delivery performance

Technical Integration:

Use REST APIs or webhooks from 3PL platforms to stream data into Databricks
Normalize SKU and location mappings to match POS/ERP systems
Join 3PL data with forecast outputs to adjust inventory dynamically

Example Use Case:

If a 3PL warehouse in NJ reports a delay in restocking sunscreen SKUs, the forecast model can:

Reduce promotional push in affected stores
Reallocate inventory from nearby locations
Update delivery ETAs in customer-facing apps

Security, Governance, and Compliance

Databricks Features:

Unity Catalog for data governance
Role-Based Access Control (RBAC)
Audit Logging
Data Lineage Tracking

Alteryx Features:

Workflow versioning
Secure data connectors
User permissions and collaboration controls

Together, these platforms ensure compliance with GDPR, CCPA, and PCI-DSS standards.

Deployment Strategy

Phase 1: MVP

Ingest POS and ERP data
Build SKU-level forecasts in Alteryx
Visualize results in Magic Reports

Phase 2: 3PL Integration

Stream warehouse data into Databricks
Join with forecast outputs
Adjust inventory and fulfillment strategies

Phase 3: ML Optimization

Train Mosaic AI models
Deploy predictive services
Automate alerts and reporting

Universal Equations: Your Retail Data Partner

At Universal Equations, we specialize in building intelligent retail data ecosystems. Our services include:

Data architecture design
Alteryx workflow development
Databricks lakehouse implementation
3PL integration strategy
AI model training and deployment

Whether you're a regional chain or a growing DTC brand, we help you connect the dots—from POS to ERP to AI.

Conclusion: Forecast Smarter, Operate Smarter

Retailers in the Northeast face a complex, fast-moving environment. By implementing a modern forecasting stack with Alteryx Designer Cloud, Databricks Lakehouse, and 3PL data integration, they can:

Forecast demand at SKU/store/day level
Respond to external signals like weather and events
Optimize inventory and fulfillment in real time

This architecture empowers both business and technical teams to collaborate, innovate, and drive smarter decisions.

Ready to Forecast Smarter Across the Northeast?

Let’s talk. Universal Equations is here to help you unlock the full potential of your retail data stack.