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The consumer packaged goods industry is experiencing a fundamental change that is forcing even the most established brands to rethink how they operate. It’s what some people call the CPG squeeze, or a convergence of margin compression, trade policy headwinds, and the sobering reality that price-led growth is no longer a viable strategy. For companies that rely on price increases to drive revenue, this is a structural change that requires new approaches to operations, strategy, and competitive positioning.
Today’s CPG companies must achieve annual productivity gains of 5% or more just to stay competitive. Traditional cost-cutting measures like travel freezes, hiring freezes, and other old efficiency drives from simpler times may yield a couple of percentage points at best. The solution lies in a more sophisticated approach: identifying which processes can be digitized before making organizational changes, dealing with questions about process efficiency, manual workflows, and opportunities for automation.
But piecemeal solutions that address isolated problems won’t deliver the systemic efficiency gains that CPG companies need today. This drives more interest in integrated technology platforms that can support decision-making and implementation in all functional areas simultaneously.
The data challenge at the heart of CPG decision making
Modern CPG operations run on data, but of course not all data strategies are created equal. Companies face a dual-barreled challenge: they need deep insight into their internal operations, while simultaneously understanding external market dynamics and consumer behavior. Historically, this meant capturing operational data, which meant losing critical business context in the process, and then having to invest heavily in reconstituting that context so it could be analyzed alongside consumer and retail data.
The disconnect creates real problems. When data is lost in business context during an acquisition, companies spend a lot of time and money trying to rebuild an understanding of what the numbers mean. Meanwhile, market conditions change, promotional windows close, and opportunities disappear. In an industry where timing often determines success or failure, this lag in analytical capability becomes a competitive disadvantage.
To meet this challenge, advanced data platforms such as SAP’s Business Data Cloud enable the import of external data with internal SAP operational data with a full business context. CPG brands can combine point-of-sale data from retailers, consumer behavioral insights, and internal transactional information without the traditional extract-and-reconstruct workflow – fundamentally changing the speed at which companies can move from analysis to decision to action.
Impact is especially important for promotion planning and revenue management. Instead of spending weeks preparing data for analysis, companies can run scenarios, model results, and adjust strategies in near real time, which is great in an industry where promotion windows are measured in days or weeks.
Promotion strategy in a high-stakes environment
High-stakes promotional moments like the Super Bowl expose how fragile CPG operations can be. Demand spikes are severe, localized, and short-lived, leaving little margin for delayed visions or interrupted execution. In this environment, promotional success depends less on creative marketing and more on how quickly companies can sense demand, model results, and align pricing, inventory, and execution while the window is still open.
The decision-making behind these promotions involves a complex analysis of many variables: what products to feature, optimal discount levels, store-specific positioning, and even regional differences in consumer preferences. What resonates with shoppers in one geography may fall flat in another, so an effective promotional strategy requires granular analysis down to individual store locations.
Tools like SAP’s Revenue Growth Management solution enable this level of sophistication, helping brands calculate and model promotional lifts and translate those insights into execution-ready decisions. The analysis accounts for regional taste preferences, local competitive dynamics, and historical performance data to optimize each promotional decision.
But promotional planning is valuable only if it can be effectively implemented. This is where many CPG companies encounter friction between strategy and operations. Data analysis can focus on perfecting the promotion mix, but without ensuring product availability, maintaining shelf presence, and executing physical sales, the analysis is somewhat academic. That’s why integration between promotional planning systems, supply chain and financial planning systems and ERP platforms is critical.
Distribution execution: The make-or-break for promotions
For high-speed promotional periods, companies must forecast demand accurately, position inventory strategically, and execute distribution without error. It’s especially complicated for categories like snacks and beverages, where direct-to-store delivery models are common. Managing shelf presence is important, because an empty shelf means consumers will switch to competing products or abandon the purchase altogether. And it requires real-time visibility across multiple layers of the supply chain across multiple data sources, and the operational capabilities to act quickly.
Modern warehouse management systems, including SAP Extended Warehouse Management, provide the granular visibility needed to track inventory in these multiple states. When combined with DSD-specific applications, such as SAP’s last-mile distribution solution, which optimizes driver routes, delivery schedules, and in-store execution, CPG companies can maintain the shelf presence that drives promotional success. Sales execution tools, such as SAP’s retail execution offering in the SAP Sales Cloud, allow field teams to audit stores and report on actual conditions. This helps give the principal a clear, accurate view of what is happening at the point of purchase.
How AI is changing CPG operations
Artificial intelligence is moving beyond experimental use cases to practical applications in CPG operations. In warehouse environments, AI-enhanced systems can optimize task management, improve forecasting accuracy, and speed up returns processing. For supply chain planning, AI helps create demand scenarios that account for the many variables affecting product movement.
SAP’s integration of Joule into Integrated Business Planning software shows how conversational AI can transform planning workflows. Instead of navigating complex interfaces to access supply chain data, planners can ask natural language questions and receive instant, AI-driven answers based on real-time information. This reduces friction in accessing insights and facilitates decision-making during critical planning cycles.
Advanced warehouse operations benefit from AI agents that can improve inventory risk analysis, optimize task management, and improve forecast accuracy. These are not just faster versions of existing processes. Rather, it represents a distinct qualitative capability that can identify patterns and risks that human analysts may overlook amid the volume and complexity of modern supply chain operations.
Revenue management, or determining the best pricing and promotion strategies, is especially suited to the help of AI, because analyzing how different price points, promotion tactics, and positioning strategies interact with thousands of stores and products is more complex than the capacity of human analysis. Machine learning can identify patterns and optimize decisions at a scale and speed unmatched by manual analysis. AI capabilities built into revenue growth management platforms promise to make promotion planning more sophisticated and more efficient.
Perhaps most important for CPG companies facing the productivity imperative, intelligent inventory management systems use machine learning to predict delivery dates and provide real-time analytics for distribution decisions. Monitoring the fulfillment of sales orders can predict fulfillment risks before they occur, enabling proactive intervention. These AI capabilities address issues such as product availability and reliable delivery during critical promotional windows, which are some of the highest-stakes challenges in CPG operations.
But the most effective applications of AI in CPG are not necessarily the most visible. Rather than consumer-facing features, real value comes from embedding intelligence into core operational processes. Incremental improvements in multiple workflows have compounded many competitive advantages over time.
The CPG squeeze is not a temporary condition that companies can wait out. The structural factors driving margin compression and limiting pricing power reflect fundamental changes in the market. Trade policies will continue to evolve. Consumer behavior will continue to shift. The companies that come out the strongest aren’t just those with the best products, they’re the ones that build the most efficient, responsive operations.
Jon Dano is the Industry Advisor for Consumer Products, at SAP.
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