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TL;DR (Key Takeaways)
- Most CPG companies cap out at 65% planning accuracy due to siloed data, missing external signals, and disconnected tools — leading to lost sales and excess inventory.
- LatentView’s ConnectedView is an AI-powered planning solution built on Databricks that unifies demand forecasting, inventory optimization, production scheduling, and procurement into a single workflow.
- The platform ingests 50+ external signals (weather, search trends, macroeconomic data) alongside internal POS, pricing, and promotion data to generate ensemble ML forecasts that outperform single-model approaches.
- Proven enterprise results include ~$8M in inventory waste reduction and an 8% potential improvement in inventory compliance at Fortune 500 CPG clients.
- Built on Databricks’ lakehouse architecture (Delta Lake, Unity Catalog, Mosaic AI), the solution offers production-grade reliability, data governance, and explainable outputs — no black-box forecasting.
- Deployment is modular and phased, allowing companies to start with one or two brands and scale to full portfolio coverage without heavy upfront investment.
Volatile markets have made supply chain resilience a necessity for CPGR companies. Yet many still struggle to get end-to-end forecasting right. For most organizations, demand and supply planning accuracy rarely crosses 65%. The impact shows up quickly: lost sales, excess inventory, and planning cycles that can’t keep up with real market shifts.
To address these challenges, LatentView Analytics has built ConnectedView – Connected Business Planning, an AI-powered solution on the Databricks Data Intelligence Platform that orchestrates the end-to-end planning process — from demand sensing through procurement — to deliver optimum service levels at lower operational costs while maintaining compliance targets.
What Makes Connected Business Planning Stand Out?
Connected Business Planning goes beyond traditional planning by weaving together demand planning, inventory optimization, production scheduling, and procurement into a single, intelligent planning fabric. Where conventional tools treat each planning function as a silo, ConnectedView senses risks across the entire value chain and enables real-time re-calibration of supply chain decisions.
The solution incorporates 50+ external signals — from weather patterns and Google Trends to macroeconomic indicators — alongside internal POS, shipment, pricing, and promotion data. This multi-signal approach powers ML-driven demand forecasts that outperform traditional methods. With pre-built accelerators, integrations with leading IBP tools, and an explainable results framework (avoiding black-box outputs), Connected Business Planning delivers a balance between speed and accuracy designed specifically for CPG supply chain complexity.
The Tech Behind Connected Business Planning – Built on Databricks
Connected Business Planning is built on LatentView’s Demand Forecasting Engine, powered by Databricks. The solution leverages a lakehouse architecture with medallion layers (bronze, silver, gold) to ingest, transform, and serve planning data at scale. Advanced ML models—from ensemble forecasting to clustering—run natively within the Databricks environment, enabling seamless model training, monitoring, and deployment.
The architecture integrates multiple data sources—assortment data, promotions, pricing inputs from Revenue Growth Management (RGM), and external signals like weather and search trends—through batch and streaming pipelines. Results are served to business applications and Power BI dashboards, providing planners with a connected, real-time view across planning horizons.
The Architecture of Connected Business Planning
Connected Business Planning leverages Databricks for intelligent, end-to-end planning workflows:
- Delta Lake: The foundation of the medallion architecture (bronze, silver, gold), Delta Lake ensures data versioning, and consistent data quality across planning datasets. It enables scalable processing of shipment, POS, and promotion data with the reliability required for production forecasting pipelines.
- Unity Catalog: Provides fine-grained data and AI governance across the planning platform. Unity Catalog manages data cataloging, lineage tracking, access controls, and model/feature registration, ensuring sensitive demand and inventory data is secure, auditable, and compliant across enterprise deployments.
- Delta Sharing: Enables secure, real-time data collaboration with retail partners, suppliers, and cross-functional teams. Connected Business Planning uses Delta Sharing to exchange live demand and inventory signals, external variables across organizational boundaries without data duplication, accelerating collaborative planning cycles.
- MLFlow on Databricks: Powers the solution’s advanced analytics layer with clustering models (K-Means, DBSCAN, Hierarchical Clustering, Gaussian Mixture Models) for SKU segmentation and ensemble forecasting models (ARIMAX/SARIMAX, Prophet, LSTM, Decision Trees) for demand prediction.
- Workflows & Autoloader: Databricks Workflows orchestrate the entire planning pipeline—from data ingestion through model scoring to dashboard refresh. Autoloader handles incremental data loading from streaming and batch sources, ensuring the planning engine always runs on the freshest data.
- Lakehouse Monitoring: Provides real-time data visibility and data quality monitoring across the lakehouse layers, ensuring planning decisions are always based on accurate, timely, and validated data.
The End-to-End Planning Process
Connected Business Planning covers four integrated planning domains, each powered by AI/ML intelligence and unified data:
| Demand Planning | Inventory Planning | Production Planning | Procurement Planning |
|---|---|---|---|
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While planning tools provide integration and automation of this end-to-end flow, every step requires data integration, transformation, and AI/ML-based additional intelligence. That is where LatentView’s Connected Business Planning adds value—bridging the gap between raw data and actionable planning decisions.
The Connected Business Planning Benefits
Better
- ~$8 Million in inventory waste reduction opportunities identified through SKU-level diagnostics and predictive waste modeling.
- ~8% potential increase in inventory compliance, ensuring greater accuracy and fewer order delays.
- Ensemble ML models combining ARIMAX, Prophet, LSTM, and Decision Trees deliver higher forecast accuracy compared with single-model approaches.
- Integrated pricing and promotion process connected to demand planning, leveraging POS and shipment data to identify promotional lift and baseline forecasts.
Faster
- Pre-built accelerators balance speed and accuracy, reducing time-to-value for new client deployments.
- Scenario planning and what-if simulation tools allow planners to model demand adjustments by PPG group, market, and promotion attributes in minutes, not days.
Scalable
- Built and scales on Databricks, eliminating the need for separate analytics infrastructure and reducing total cost of ownership.
- Modular phased deployment (Discover → MVP → Baseline → Scale) enables clients to start with zero upfront cost and scale investment with proven value.
LatentView Advantage
- Deep CPG Domain Expertise: Proven delivery across Fortune 500 CPG companies including a leading food, snack, and beverage corporation and a major global innerwear and activewear manufacturer, with demonstrated results in inventory waste reduction and demand accuracy improvement.
- Pre-Built Accelerators: 50+ external signals, suite of pre-built ML models, and ready-made integrations to leading IBP tools reduce deployment timelines from months to weeks.
- Explainable, Transparent Results: No black-box approach. Every forecast and recommendation is traceable, auditable, and understandable by business stakeholders—building trust and adoption.
- Modular, Scalable Design: Start with 1–2 brands and a single retailer, then scale to full product portfolios across all retailers and geographies—with flexible engagement models from discovery workshops to national rollout.
- Data Engineering Excellence: LatentView’s Databricks Center of Excellence brings deep platform expertise in lakehouse architecture, data engineering services, and ML operations to ensure production-grade reliability.
Portfolio Mix Optimization and Scenario Planning for CPG
AI driven planning systems allow CPG companies to simulate portfolio mix scenarios across brands, pack sizes, retailers, and promotional strategies. By combining demand forecasting with production and inventory constraints, planners can run scenario planning exercises to evaluate trade offs between revenue growth, inventory risk, and supply capacity before executing decisions.
AI-Driven Production Planning and Inventory Allocation for CPG
AI-driven planning platforms help CPG companies optimize production planning and inventory allocation using machine learning models that combine demand forecasts, promotion data, retailer signals, and supply constraints. These systems enable planners to balance service levels, inventory costs, and production capacity across the network.
Conclusion
Connected Business Planning transforms supply chain planning by combining advanced AI/ML models with the Databricks lakehouse platform to deliver an integrated, data-driven approach across the entire planning process. By connecting demand forecasting, inventory optimization, production planning, and procurement into a unified intelligence layer, the solution addresses the root causes of planning inaccuracy-siloed data, missing external signals, and disconnected processes.
With proven results including $8M+ in inventory waste reduction and 8% potential improvement in inventory compliance at enterprise CPG clients, Connected Business Planning delivers measurable value from the first engagement. Curious about how Databricks can elevate your supply chain planning intelligence?
Let’s discuss how this integration can transform your approach to demand and inventory optimization.
FAQs
1. What is Connected Business Planning in supply chains?
Connected Business Planning integrates demand forecasting, inventory optimization, production planning, and procurement into a unified planning framework. Instead of operating in silos, these functions share real-time data and predictive insights, enabling faster and more accurate supply chain decisions.
2. How does AI improve demand planning?
AI models analyze large volumes of historical data along with external signals such as weather, promotions, and search trends. These models identify patterns traditional forecasting approaches often miss, improving forecast accuracy and enabling more responsive planning. Therefore, improving further downstream planning processes.
4. What role does Databricks play in supply chain planning?
Databricks provides the lakehouse infrastructure that supports large-scale data ingestion, transformation, and machine learning model deployment. This allows organizations to combine operational data, external signals, and forecasting models within a single platform.
5. Why do CPG companies struggle with forecasting accuracy?
Many organizations rely on fragmented planning systems, incomplete datasets, and traditional statistical models. These limitations prevent planners from incorporating external signals or adjusting quickly to market changes.
6. What benefits can AI-driven planning deliver?
AI-driven supply chain planning can improve forecast accuracy, reduce excess inventory, identify waste reduction opportunities, and enable faster scenario planning. These improvements help organizations respond more effectively to demand volatility.
7. What is multi-echelon inventory optimization? And how is MEIO different from traditional safety stock planning?
Multi-echelon inventory optimization determines optimal inventory levels across multiple stages of the supply chain, considering demand variability, lead times, and service targets at the network level. Traditional approaches set safety stock independently at each location. MEIO optimizes inventory across the entire network, reducing total inventory while improving service.
