Presented by SAP
The consumer packaged goods industry is experiencing a fundamental shift that's forcing even the most established brands to rethink how they operate. It's what some folks call the CPG squeeze, or a convergence of margin compression, trade policy headwinds, and the sobering reality that pricing-led growth is no longer a viable strategy. For companies that have relied on price increases to drive revenue, it's a structural change that demands new approaches to operations, strategy, and competitive positioning.
CPG companies now need to achieve annual productivity gains of 5% or more just to stay competitive. Traditional cost-cutting measures like travel freezes, hiring pauses, and other age-old efficiency drives from simpler times might yield a couple of percentage points at best. The solution lies in a more sophisticated approach: identifying which processes can be digitally enabled before making organizational changes, confronting questions about process efficiency, manual workflows, and opportunities for automation.
But piecemeal solutions that address isolated problems can't deliver the systemic efficiency gains that CPG companies now require. This is driving increased interest in integrated technology platforms that can support decision-making and execution across 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 are facing a dual-barreled challenge: they need deep insights into their internal operations, while simultaneously understanding external market dynamics and consumer behavior. Historically, this has meant extracting operational data, which means losing critical business context in the process, and then needing to invest big on reconstituting that context so it can be analyzed alongside consumer and retail data.
The disconnect creates real problems. When data loses its business context during extraction, companies spend significant time and money trying to rebuild an understanding of what the numbers actually 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 the competitive disadvantage.
To address this challenge, advanced data platforms like SAP's Business Data Cloud are able to import external data with internal SAP operational data that has full business context. CPG brands can combine point-of-sale data from retailers, insights on consumer behavior, 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.
The impact is particularly significant for promotional planning and revenue management. Instead of spending weeks preparing data for analysis, companies can run scenarios, model outcomes, and adjust strategies in near real-time, which is huge in an industry where promotional windows are measured in days or weeks.
Promotional strategy in a high-stakes environment
High-stakes promotional moments like the Super Bowl expose how fragile CPG operations have become. Demand spikes are intense, localized, and short-lived, leaving little margin for delayed insights or disconnected execution. In this environment, promotional success depends less on creative merchandising and more on how quickly companies can sense demand, model outcomes, and align pricing, inventory, and execution while the window is still open.
The decision-making behind these promotions involves complex analysis of multiple variables: which products to feature, optimal discount levels, store-specific positioning, and even regional variations in consumer preferences. What resonates with shoppers in one geography may fall flat in another, so 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 every promotional decision.
But promotional planning is only valuable if it can be executed effectively. This is where many CPG companies encounter friction between strategy and operations. Data analysis might pinpoint the perfect promotional mix, but without ensuring product availability, maintaining shelf presence, and executing physical merchandising, the analysis is pretty much academic. That's why integration between promotional planning systems, supply chain and financial planning systems and ERP platforms are critical.
Distribution execution: The make-or-break for promotions
For high-velocity promotional periods, companies must forecast demand accurately, position inventory strategically, and execute distribution flawlessly. This is particularly complex for categories like snacks and beverages, where direct store delivery models are common. Managing shelf presence is critical, because an empty shelf means consumers will switch to competitive products or abandon the purchase entirely. And it requires real-time visibility into multiple layers of the supply chain across a variety of data sources, and the operational capabilities to act upon quickly.
Modern warehouse management systems, including SAP Extended Warehouse Management, provide the granular visibility needed to track inventory across these multiple states. When combined with DSD-specific applications, such as SAP’s last mile distribution solution, that optimize 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 SAP Sales Cloud, allow field teams to audit stores and report on actual conditions. This helps gives headquarters clear, accurate visibility into what’s happening at the point of purchase.
How AI is changing CPG operations
Artificial intelligence is moving beyond experimental use cases to practical applications across CPG operations. In warehouse environments, AI-enhanced systems can optimize task management, improve forecasting accuracy, and streamline returns processing. For supply chain planning, AI assists in generating demand scenarios that account for multiple variables affecting product movement.
SAP's integration of Joule into Integrated Business Planning software demonstrates 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 immediate, AI-driven responses based on real-time information. This reduces the friction in accessing insights and accelerates decision-making during critical planning cycles.
Advanced warehouse operations are benefiting from AI agents that can enhance inventory risk analysis, optimize task management, and improve forecast accuracy. These aren't just faster versions of existing processes. Instead, they represent qualitatively different capabilities that can identify patterns and risks that human analysts might miss amid the volume and complexity of modern supply chain operations.
Revenue management, or determining optimal pricing and promotional strategies, is particularly well-suited to AI assistance, because analyzing how different price points, promotional tactics, and positioning strategies interact across thousands of stores and products is complex beyond human analytical capacity. Machine learning can identify patterns and optimize decisions at a scale and speed that manual analysis cannot match. AI capabilities being built into revenue growth management platforms promise to make promotional planning both more sophisticated and more efficient.
Perhaps most significantly for CPG companies facing the productivity imperative, intelligent inventory management systems are using machine learning to predict delivery dates and provide real-time analytics for distribution decisions. Sales order fulfillment monitoring can predict fulfillment risks before they materialize, enabling proactive intervention. These AI capabilities address issues like product availability and reliable delivery during critical promotional windows, which are some of the highest-stakes challenges in CPG operations.
But the most impactful AI applications in CPG won't necessarily be the most visible. Instead of flashy consumer-facing features, the real value comes from embedding intelligence into core operational processes. Incremental improvements across dozens of workflows compound into substantial competitive advantages over time.
The CPG squeeze isn't a temporary condition that companies can wait out. The structural factors driving margin compression and limiting pricing power reflect fundamental market changes. Trade policies will continue evolving. Consumer behavior will keep shifting. The companies that emerge stronger won't be those with the best products alone, they'll be those that built the most efficient, responsive operations.
Jon Dano is Industry Advisor for Consumer Products, at SAP.
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