In 2026, the old supply chain playbook is rapidly losing relevance. For technical decision-makers and executives overseeing SAP and Salesforce ecosystems, the biggest bottleneck isn’t a lack of data; it’s the fragmentation around it. If your planning relies solely on historical sales from your ERP, you are effectively driving by looking in the rearview mirror.

Today, demand sensing has moved from a buzzword to an operational necessity. It’s about catching market shifts before they show up in your sales reports. By merging your private enterprise data with public signals like Google Trends and localized weather patterns, you can build a truly 360° view of demand.

At Kartaca, we are seeing the most resilient enterprises use the Google Cloud Cortex Framework as a foundation, moving from reactive replenishment to predictive, continuously adjusted execution.

The 2026 Reality: Volatility is the New Baseline

As we move through 2026, global volatility has ceased to be an intermittent disruption and has become a permanent planning assumption. For the enterprise, two primary forces are driving the need for a 360° signal:

1. The New Tariff Reality: A 2025 McKinsey study found that 82% of supply chain leaders report their operations are significantly affected by new tariffs, with up to 40% of their total activity subject to ongoing regulatory flux. Relying on static monthly plans is a strategic mistake when landed costs can change overnight.
2. Climate-Driven Financial Risk: Between 2020 and 2024, the uninsured portion of natural catastrophe costs hovered near 60%.* For a CPG company, a localized heatwave or flash flood isn’t just a weather event; it triggers sudden demand spikes, disrupted logistics, and inventory imbalance that traditional models fail to anticipate.*

2026 Macro-Environmental Driver	Impact on Supply Chain Planning	Required Analytical Response
Trade Fragmentation	Overnight shifts in sourcing viability and costs	Real-time scenario simulation & multi-tier visibility*
Climate Loss Inflation	Supply line breaks and inventory “whiplash” (stockouts during peak demand and expensive overstock when the weather shifts)	Site-specific weather-responsive forecasting*
Regionalization Imperative	Complexity in managing local, fragmented supply bases	Multi-echelon inventory optimization*

Together, these forces are redefining supply chain planning. Enterprises need models that react at the speed of the external world, not at the pace of internal reporting cycles.

The Architecture of the 360° Signal: Google Cloud Cortex Framework

To bridge the gap between rigid enterprise systems and high-velocity public data, technical leaders need a foundation that eliminates the ETL tax, the time, cost, and risk of manual data movement. The Google Cloud Cortex Framework acts as a solution accelerator, providing a unified, analytics-ready data core in BigQuery.

Rather than starting from scratch, Cortex offers predefined data models, pipelines, and best practices aligned with SAP and other enterprise platforms. This allows teams to focus on insight and execution instead of data plumbing.

The “Zero-Copy” Evolution

A major pain point for executives is the delay caused by data replication. With innovations such as SAP Business Data Cloud Connect for Google BigQuery, organizations can now establish a “zero-copy” data fabric. This approach allows AI models and demand-sensing applications to access live, governed SAP data directly, without relying on stale snapshots. The result is faster decisions, lower operational risk, and greater confidence in automated actions.

The Semantic Layer

Cortex doesn’t just ingest data; it preserves business meaning. The framework’s predefined transformation templates flatten complex SAP hierarchies such as Profit Centers, Cost Centers, and Material Groups, making them immediately usable in BigQuery.

This semantic consistency is critical. Without it, advanced analytics and machine learning models inherit ambiguity instead of intelligence.

Internal Data Source	Enriched External Signal	Analytical Utility
SAP S/4HANA	Google Trends	Captures real-time consumer intent before the sale
Salesforce CRM	WeatherNext Forecasts	Contextualizes demand against environmental anomalies
IoT/Telematics	Google Maps Geospatial	Optimizes routes based on real-time traffic and road constraints

Demand Sensing: Turning Volatility into Resilience

Demand sensing is a fundamental change in how your business “listens” to the market. Traditional forecasting relies on historical sales—essentially guessing what will happen based on what happened before. Demand sensing, however, focuses on what is happening right now.

By using machine learning within the Cortex Framework, your system can identify complex patterns that are invisible to the human eye. For instance, instead of just seeing a sales average, the system can detect a sudden spike in search interest for a specific product attribute (via Google Trends) and correlate it with unseasonable weather forecasts.* This allows the business to respond before demand reaches the warehouse.

This logic replaces “best guesses” with a system that models the relationships among thousands of inputs—price, channel, weather, and promotions—without requiring manual rules. The impact is immediate: organizations using these real-time signals can improve forecast accuracy by 20–30% improvements in forecast accuracy, fewer stockouts, and meaningful reductions in excess inventory.* Over time, the supply chain evolves into a self-adjusting system that recalibrates safety stock and logistics in near real time.

Suite of Pre-Built ML Accelerators

The core value for executives is speed of implementation. Cortex provides a suite of pre-built ML templates grounded in enterprise data models:*

• Demand Sensing for CPG: This solution consolidates SAP ERP data with search trends and weather to highlight “Impact Alerts”.* It identifies the customer and which external factors are likely to cause a deviation from plan.
• Assortment Recommendation Engine: Beyond forecasting, this template optimizes product placement. By incorporating planogram performance and sales metrics, it specifies ideal product assortments at the store level.
• Inventory Optimization: These models minimize inventory levels for parts and finished goods while ensuring SLAs are met.

Success Stories: Driving Business Value Global leaders are already operationalizing these tools to drive value: Carrefour Belgium: Unifying the Retail Vision* Facing aging data centers and fragmented systems, Carrefour Belgium migrated its mission-critical SAP S/4HANA environment to Google Cloud. By consolidating data from 700+ stores into a single BigQuery data lake, they gained instant visibility into customer behavior and supply chain efficiency, enabling better assortment planning and seamless omnichannel experiences. Breuninger: High-Touch, Data-Driven Luxury* In Germany, luxury retailer Breuninger uses BigQuery and SAP to harness real-time delivery information. Sales managers can track exactly when premium products reach customers, allowing for personalized follow-ups that strengthen loyalty—a key requirement in the “customer-centric” 2026 market. Unieuro: Speed at Scale* Italy’s leading electronics retailer, Unieuro, used BigQuery to process over 20 billion price combinations to meet regulatory demands in just two months—a 60% reduction in time. The integration of SAP with BigQuery democratized data access, turning innovation into business value across the organization. Power International: Real-Time Sales Automation* Power International modernized its sales reporting by replacing static PDFs with a real-time BigQuery and SAP Datasphere solution. This gives their teams a “live” view of the sales pipeline, allowing for proactive inventory allocation and more precise financial planning. The Home Depot: Accurate Supply Chain Planning The Home Depot partnered with Google Cloud to support its SAP transformation, migrating SAP S/4HANA, CAR, and additional applications to the platform. By establishing a single source of truth and leveraging BigQuery for analytics, financial planning, and demand forecasting, the company strengthened supply chain planning while delivering the flexibility customers expect.

Navigating the 2026 Executive Pain Points

As technical leaders look ahead, three friction points dominate:

1. The Rise of Agentic AI: In 2026, AI is moving from “insight generation” to “outcome orchestration.” AI agents don’t just flag a disruption, but autonomously reroute a shipment or update an ERP record within trusted guardrails.
2. Continuous Planning: The monthly S&OP cycle is too slow for 2026. Enterprises are moving toward “Continuous Planning,” where forecasts are recalculated daily based on real demand signals.
3. Sustainability as a Strategy: ESG is no longer just a reporting obligation; it is a growth lever.* Running SAP on Google Cloud’s carbon-neutral infrastructure helps you meet strict mandates while optimizing resource use.*

Strategic Objective	Technical Requirement	Solution
Operational Continuity	High-frequency scenario planning	BigQuery ML & Cortex Accelerators
Customer Connection	Personalized engagement via live data	Salesforce Agentforce + BigQuery Zero-Copy
Supply Chain Resilience	Real-time multi-tier traceability	SAP BDC Connect + Google Maps API
Efficiency & TCO	Scalable infrastructure / pay-per-use	Data Lake Modernization

Modernize Your Data Infrastructure with Kartaca

Building a 360-degree demand signal is a complex engineering task. As a Premier Google Cloud Partner, Kartaca delivers the precision required to operationalize it at scale.

We specialize in:

• Data Lake Modernization: Breaking down the silos between your SAP, Salesforce, and public datasets.
• Cortex Implementation: Speeding up your transition to AI-ready data models with pre-built templates.
• Real-Time Analytics: Enabling streaming data to BigQuery so your decisions reflect what is happening now, not what happened last week.

Ready to see the full signal? Contact us today to modernize your data infrastructure and turn your data into your greatest competitive edge.