Presented by SAP
The consumer packaged goods industry is experiencing a fundamental shift that is forcing even the most established brands to rethink how they operate. This is what some call the consumer goods squeeze, or a convergence between margin compression, trade policy hurdles and the sad reality that price-led growth is no longer a viable strategy. For companies that have relied on rising prices to generate revenue, this is a structural change that requires new approaches to operations, strategy and competitive positioning.
Consumer goods companies now must achieve annual productivity gains of 5% or more simply to remain competitive. Traditional cost-cutting measures, such as travel freezes, hiring pauses and other time-honored efficiency measures from simpler times, might yield a few percentage points at best. The solution lies in a more sophisticated approach: identifying processes that can be digitally enabled before making organizational changes, answering questions about process efficiencies, manual workflows and automation opportunities.
But piecemeal solutions that tackle isolated problems can’t deliver the systemic efficiencies that FMCG companies now need. This is driving increased interest in integrated technology platforms that can simultaneously support decision-making and execution across all functional areas.
The issue of data at the heart of CPG decision-making
Modern CPG operations run on data, but of course, not all data strategies are equal. Businesses face a dual challenge: they need deep insight into their internal operations, while simultaneously understanding external market dynamics and consumer behavior. Historically, this meant extracting operational data, which meant losing critical business context in the process, and then having to invest heavily in rebuilding that context so it could be analyzed alongside consumer and retailer data.
This disconnect creates real problems. When data loses its business context during extraction, companies spend a lot of time and money trying to rebuild an understanding of what the numbers really mean. During this time, 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 address this challenge, advanced data platforms such as SAP’s Business Data Cloud are capable of importing external data with internal SAP operational data that has complete business context. FMCG brands can combine retailer point-of-sale data, consumer behavior insights, and internal transactional insights without the traditional extract-and-rebuild workflow, fundamentally changing the speed at which businesses can move from analysis to decision and action.
The impact is particularly important for promotional planning and revenue management. Instead of spending weeks preparing data for analysis, businesses can run scenarios, model results, 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 reveal just how fragile CPG operations have become. Demand spikes are intense, localized and short-lived, leaving little room for delayed information or disconnected execution. In this environment, promotional success depends less on creative merchandising and more on how quickly companies can sense demand, model results, and align pricing, inventory, and fulfillment while the window is still open.
The decision-making behind these promotions involves a complex analysis of several variables: which products to feature, optimal discount levels, specific store positioning, and even regional variations in consumer preferences. What appeals to shoppers in one geography may not work in another, which is why an effective promotional strategy requires granular analysis down to each store’s location.
Tools like the SAP Revenue Growth Management solution enable this level of sophistication, helping brands calculate and model promotional increases and translate that information into decisions ready for execution. The analysis takes into account regional taste preferences, local competitive dynamics and historical performance data to optimize each promotional decision.
But promotional planning is only valuable if it can be executed effectively. This is where many FMCG companies encounter friction between strategy and operations. Data analysis can identify the perfect promotional mix, but without ensuring product availability, maintaining shelf presence and executing physical merchandising, the analysis is rather academic. This is why integration between promotional planning systems, supply chain and financial planning systems, and ERP platforms is essential.
Distribution Execution: The Success or Failure of Promotions
During high-speed promotional periods, businesses must accurately forecast demand, position inventory strategically, and execute distribution flawlessly. This is particularly challenging for categories like snacks and beverages, where direct-to-store delivery models are common. Managing shelf presence is essential 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 a variety of data sources, as well as operational capabilities to act 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 fulfillment, FMCG companies can maintain the on-shelf presence that drives promotional success. Sales execution tools, such as SAP’s Retail Execution offering in SAP Sales Cloud, enable field teams to audit stores and report on real-world conditions. This helps give head office clear and precise visibility into 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 streamline returns processing. For supply chain planning, AI helps generate 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 questions in natural language and receive immediate, AI-driven responses based on real-time insights. This reduces friction in accessing information and accelerates decision-making during critical planning cycles.
Advanced warehouse operations benefit from AI agents that can improve inventory risk analysis, optimize task management, and improve forecasting accuracy. It’s not just about faster versions of existing processes. Instead, they represent qualitatively different capabilities that can identify patterns and risks that human analysts might overlook in the volume and complexity of modern supply chain operations.
Revenue management, or determining optimal pricing and promotion strategies, is particularly well suited for AI assistance because analyzing how different price points, promotional tactics, and positioning strategies interact across thousands of stores and products is complex and beyond human analytical capacity. Machine learning can identify patterns and optimize decisions at a scale and speed that manual analysis cannot match. AI capabilities built into revenue growth management platforms promise to make promotional planning both more sophisticated and more effective.
Perhaps most importantly for consumer goods companies facing a productivity imperative: intelligent inventory management systems use machine learning to predict delivery dates and provide real-time analytics for distribution decisions. Monitoring customer order execution can predict execution risks before they materialize, enabling proactive intervention. These AI capabilities address issues such as product availability and delivery reliability during critical promotional windows, which are among the most significant challenges in CPG operations.
But the most impactful AI applications in the CPG sector won’t necessarily be the most visible. Instead of flashy consumer-facing features, the real value comes from integrating intelligence into core business processes. Incremental improvements to dozens of workflows translate into substantial competitive advantages over time.
Restricting consumer products is not a temporary condition that businesses can wait for. Structural factors that drive margin compression and limit pricing power reflect fundamental market changes. Trade policies will continue to evolve. Consumer behavior will continue to change. The companies that emerge stronger will not only be those that offer the best products, they will be those that have built the most efficient and responsive operations.
Jon Dano is an industry advisor for consumer products at SAP.
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