Every supply chain leader in the consumer packaged goods (CPG) industry understands the frustration: you might be viewing a dashboard that shows healthy inventory levels, yet your retailer has just reported a stockout.
Alternatively, your forecast may have predicted stable demand, but actual sales tell a different story. The issue isn’t the expertise of your supply chain team or the effectiveness of your demand planning software; it lies in the foundation that everything is built upon: the quality and timeliness of your retail data.
In today's highly competitive CPG landscape, the key difference between achieving supply chain excellence and constantly dealing with crises often boils down to one crucial factor: the accuracy and reliability of your retail data. While most companies have access to point-of-sale (POS) and inventory data from retailers, not all data is equal. The quality, consistency, and timeliness of that data can determine whether your supply chain is a strategic asset or a daily liability.
The Hidden Cost of Fragmented Retail Data
Many CPG companies and consumer brands work with dozens, if not hundreds, of retail partners. Each retailer has its own data format, delivery schedule, and quality standards. One retailer may send weekly reports in Excel, another may provide daily API feeds, while a third might email PDFs requiring manual data entry. The result is a patchwork of inconsistent and often incomplete information that creates dangerous blind spots.
Research from the Supply Chain Resource Cooperative found that only 15% of respondents believe their existing systems can produce clean, trustworthy data. This lack of reliable data has widespread consequences throughout the organization. When supply chain teams cannot trust their data, they resort to workarounds, maintain shadow spreadsheets, and rely on gut feelings rather than analytics.
The time drain is also significant. According to research from McKinsey, employees spend an average of 1.8 hours each day—9.3 hours per week—searching for and gathering information. For supply chain professionals, this often means manually reconciling data from different retail sources, validating suspicious numbers, and tracking down colleagues who might have the "real" story. That time could otherwise be spent on strategic optimization, scenario planning, or proactive problem-solving.
The financial impact is staggering. Poor data quality can lead to overstocking, which ties up capital and risks obsolescence, or understocking, which results in lost sales and damaged retailer relationships. Fragmented data forces supply chain teams into a reactive mode, as they constantly respond to surprises that accurate POS and inventory data could have predicted weeks in advance.
Consider the challenges faced by a typical CPG company: they manage data feeds from over 50 retailers, each with unique formats and quality standards. Some provide daily updates, while others do so weekly. Product codes may not match across systems, and one retailer's "units sold" might include returns while another's does not. Additionally, geographic hierarchies can vary. By the time the data is cleaned and harmonized manually, it is often too late to inform proactive decisions.
The Competitive Advantage of RETAIL Data Accuracy
When retail data is accurate, harmonized, and timely, it fundamentally transforms supply chain strategy. This transformation occurs at multiple levels, each building on the foundation of trusted data.
Superior Demand Forecasting for CPGs
Accurate demand forecasting is essential for efficient supply chain operations. Research indicates that improving forecast accuracy can lead to significant financial benefits. For example, a 15% increase in forecast accuracy can result in a pre-tax profit improvement of 3% or more. Additionally, studies by Deloitte show that machine learning models can enhance forecast accuracy by 30% to 50% compared to traditional methods, but only if they are fed high-quality data.
Many organizations overlook a crucial point: forecast accuracy is not solely about better algorithms or more sophisticated models; it also depends on the quality of the input data and when that data is received. Research from McKinsey indicates that supply chain statistical models that utilize high-quality data can save 3% to 8% in costs, while flawed data results in unreliable outcomes and wasted resources.
Having clean, harmonized POS and inventory data at the SKU and store level, via a solution like Retail Velocity's VELOCITY® retail data platform, enables supply chain teams to detect demand signals earlier and accurately distinguish genuine trends from one-time anomalies. This enables them to adjust production and distribution plans with confidence, resulting in reduced safety stock requirements, fewer expedited shipments, and improved service levels.
Optimized Inventory Positioning
Inventory optimization involves a delicate balance between carrying costs and the risk of stockouts. Accurate retail data can decisively tip this balance in your favor. With reliable, detailed insights into what is selling and where, you can position inventory strategically instead of defensively.
Improvements in demand forecast accuracy can lead to revenue increases of 0.5% to 3% due to better inventory availability. Research by Deloitte also shows that effective demand planning and forecasting can reduce inventory costs by up to 20% and enhance supply chain efficiency by 15%.
The benefits go beyond mere numbers. Accurate data allows supply chain teams to implement advanced strategies, such as multi-echelon inventory optimization and dynamic safety stock calculations—approaches that are unattainable with fragmented or unreliable data. You can confidently reduce inventory in slow-moving locations while ensuring that you have sufficient stock where demand is high, all without increasing overall risk.
Faster, More Confident Decision-Making
One of the most transformative advantages of accurate daily POS and inventory data is the speed and confidence it brings to decision-making. When data quality is poor, teams must often rely on gut feelings or outdated methods, leading to delayed decisions, constant firefighting, and increased operational risk.
In contrast, trusted data enables proactive strategies rather than reactive scrambling. Your team can identify promotional opportunities, detect emerging trends, and respond to competitive actions while there's still time to act. During supply chain disruptions—which are becoming increasingly common—accurate data is critical, enabling rapid pivots and alternative sourcing decisions based on real-time or near real-time visibility.
A supply chain survey by McKinsey reveals that high-quality data is associated with lower levels of recent supply chain disruption, demonstrating the tangible protective value of data accuracy during volatile times.
Breaking Free from the Reactive Supply Chain
Transitioning from a reactive to a proactive supply chain management approach begins with closing the trust gap in your data. When supply chain planners doubt the accuracy of their data, the entire planning process falters. As a result, teams may hedge their demand forecasts, maintain excessive safety stock "just in case," and postpone important decisions until they can manually verify the information.
According to various research, just over half of supply chain respondents describe the quality of data in their planning systems as "sufficient" or "high." This indicates that many companies have opportunities to enhance their data collection and management processes. This gap between the current state and best practices creates systemic vulnerabilities that competitors with more reliable data can exploit.
To break this cycle, it is essential to address data quality at its source. This involves standardizing disparate retailer data formats into a consistent structure, validating data accuracy through automated quality checks, and ensuring timely delivery of information when it can still inform decisions. For most CPG companies, this is not a do-it-yourself project—the complexity of managing hundreds of data sources, each with unique formats and update schedules, necessitates specialized infrastructure and expertise.
Enabling Cross-Functional Excellence
The advantages of accurate retail data extend well beyond the supply chain department. When everyone in the organization relies on the same trusted data source, it fosters a level of alignment and collaboration that would otherwise be unattainable.
Sales teams can pinpoint growth opportunities and craft data-driven pitches to retailers. Marketing can assess the effectiveness of promotions with precision, allowing them to differentiate between successful campaigns worth repeating and costly failures to avoid. Finance can confidently forecast cash flow requirements, knowing that inventory and sales projections are based on reliable information rather than guesswork.
Research has shown that companies with highly integrated supply chain operations achieve 20% higher efficiency rates compared to those with fragmented structures. This integration relies on a common language—accurate, harmonized data that everyone can trust and utilize for their specific needs.
The collaborative benefits increase further when considering external partnerships. Programs like vendor-managed inventory, collaborative planning with key retailers, and joint business planning all require shared visibility into accurate POS and inventory data. When both parties trust the numbers, these initiatives yield significantly better results than when each side maintains its own version of the truth.
Building the Business Case
Investing in data quality infrastructure may seem like a back-office expense, but the return on investment (ROI) tells a different story. Organizations that implement data harmonization and quality improvement initiatives typically see returns within months rather than years.
The savings come from various sources. For example, reducing expedited freight costs allows companies to plan shipments proactively instead of rushing to resolve stockouts. Lower inventory carrying costs result from being able to operate with less safety stock, while improved working capital efficiency occurs when cash isn't tied up in slow-moving inventory. Additionally, companies can increase revenue by ensuring products are available where and when customers want to buy them.
Consider the broader economic context: research indicates that for a company with $50 million in turnover, even a one-percentage-point improvement in under-forecasting error can yield savings of up to $1.52 million. When this is multiplied across larger organizations or when accounting for improvements beyond just under-forecasting, the business case becomes very compelling.
The Data Foundation of Future Capabilities
Accurate retail data is crucial in creating a foundation for advanced capabilities that will define competitive advantage in the coming years. Artificial intelligence (AI) and machine learning applications have the potential to revolutionize supply chain planning, but their effectiveness relies heavily on the quality and granularity of the data used.
Nearly half (47%) of AI professionals are concerned that their companies have invested too much money into AI models that underperform due to poor data quality. The sophisticated control towers, predictive analytics platforms, and autonomous planning systems promoted by vendors all share a common requirement: clean, consistent, and historical data to effectively train their algorithms.
By investing in data quality now, you are not only addressing today's challenges but also building the infrastructure necessary for tomorrow's innovations. Companies that postpone this investment may find themselves unable to utilize next-generation technologies, no matter how much they spend on advanced software. The principle of "garbage in, garbage out" remains true, even with the most sophisticated AI systems.
Starting Your Journey
Transforming data chaos into a competitive advantage doesn’t have to involve a massive overhaul. Most successful implementations begin with a clear assessment of current data quality, followed by quick wins that demonstrate value and build momentum.
Start by cataloging your retail data sources. Document their formats, update frequencies, and any quality issues. Next, identify the pain points where data problems are causing significant operational challenges—these present your best opportunities for impactful improvement. Common areas to focus on include:
- High-Velocity Products or Categories: In these areas, forecast accuracy directly affects service levels and costs. Improving data quality here can yield quick, measurable ROI and build credibility for broader initiatives.
- Key Retailer Relationships: Data quality issues that strain partnerships or limit collaborative planning can be detrimental. Addressing these specific data flows can unlock substantial benefits in joint business planning.
- Promotional Planning: Inaccurate baseline data can lead to poor promotional forecasts and costly inventory misalignments. Ensuring clean data in this area enhances both trade promotion optimization and ROI and overall supply chain efficiency.
Consider partnering with a specialist and expert like Retail Velocity, who understands and can address the unique challenges associated with retail data ingestion, cleaning, and harmonization. The complexity of managing numerous diverse retail data sources, each with its own quirks and requirements, makes in-house development less advantageous. Retail Velocity, with its cloud-based VELOCITY® retail data platform, can implement solutions more quickly and comprehensively than internal IT teams, while also providing ongoing support and advisory services as retailers' needs evolve.
Conclusion: Data as Your Strategic Differentiator
In an era where every CPG company has access to advanced planning software and methodologies, the real competitive advantage comes from the quality of execution. This execution quality is fundamentally built on the often-overlooked tasks of automatically collecting, cleaning, and harmonizing POS and inventory data. This critical process sets industry leaders apart from those who lag behind.
Your competitors are collaborating with the same retailers, encountering similar challenges, and applying comparable strategies. The main distinction lies in whether they are making decisions based on precise, timely data or dealing with fragmented information that has been a hindrance for your organization.
The evidence is clear: companies that invest in retail data quality achieve significantly better forecast accuracy, lower inventory costs, faster decision-making, and improved collaboration across different functions. These benefits accumulate over time, widening the gap between data-driven leaders and brands that are still struggling with poor data quality and spreadsheet chaos.
The real question is not whether to invest in retail data quality; it’s whether you will take proactive steps to gain a competitive advantage or wait until your competitors have already taken the lead. In today’s fast-paced retail-CPG landscape, the opportunity to make this choice is becoming increasingly limited.
Are you ready to transform your supply chain with accurate and unified retail data?
At Retail Velocity, we specialize in automatically collecting, cleaning, and consolidating retailer data from multiple sources. We provide CPG companies and consumer brands of all sizes with a reliable data foundation essential for strategic decision-making. Contact us to learn how we can turn your data chaos into a competitive advantage.
