Imagine a world where your forecasts are consistently accurate, inventory is perfectly optimized, and promotional strategies hit their mark every time. While this vision is exciting, many companies find themselves facing a fundamental hurdle: data readiness.
As explored in a recent webinar “Data Readiness: How to Build a Foundation for AI-Driven Demand Planning,” featuring Algo experts Samuel Parker and Tom Bond, building a strong data foundation is not just important, it’s non-negotiable for successful AI implementation.
What Does “Data Readiness” Truly Mean?
It’s more than just clean spreadsheets. Data readiness for AI-driven demand planning encompasses several critical aspects:
- Completeness Across Systems: Is your data fully integrated across all your ERP, POS, and other internal systems? Can you connect the dots between promotional data in one system and master data in another? Disparate systems and siloed information are common challenges that hinder a holistic view.
- Accessibility in Real-Time: Is your data available when you need it? Delays in data feeds can significantly impact forecasting accuracy, especially for time-sensitive events like Black Friday or holiday seasons. Near real-time access is key.
- Structured for Business Needs: Is your data organized in a way that aligns with your business knowledge and planning processes? Hierarchical data structures, for example, allow for more efficient planning by enabling roll-ups and focusing on anomalies rather than individual SKUs.
- Trusted by Your Team: This is perhaps the most crucial element. Do your internal teams trust the data they are working with? If data quality is questionable, confidence in forecasts and demand plans will naturally erode, leading to reliance on manual overrides and gut feelings.
The Challenges of Manual Data Processes
Many mid-market companies, and even some larger ones, rely heavily on manual processes to compensate for data shortcomings. This often looks like:
- Working across multiple Excel tabs, painstakingly trying to consolidate disparate data.
- Rebuilding forecasts weekly from scratch due to unsynchronized systems.
- Spending an inordinate amount of time reconciling definitions (e.g., “units sold” vs. “units shipped”).
- Entire teams being consumed by data curation rather than strategic analysis.
These challenges are often a natural byproduct of growth, mergers and acquisitions, or internal resource constraints. The good news is that they are addressable.
Algo’s Approach: Crawl, Walk, Run Towards Data Readiness
At Algo, our goal is not to force a complete overhaul of your entire tech stack, but to help you strategically navigate these waters. Our approach focuses on three key pillars:
- Discovery & Prioritization: We start with thorough discovery sessions to understand your current data landscape and planning processes. We help identify where the biggest challenges lie and prioritize “quick wins” – mandatory data points that will deliver the most immediate impact.
- Normalizing the Data: Our team works hand-in-hand with yours to fix data structures, clean up inconsistencies, and perform comprehensive data mapping. We leverage best practices from across the industry to ensure your data is accurate, complete, and usable. This often involves three weekly sessions, ensuring we’re actively helping you correct and refine your data.
- Operationalizing for Automation: Once the data is clean and structured, we build the necessary connectors and pipelines to automate data ingestion. This reduces the need for manual uploads and ensures that your forecasting engine is always working with the most current and reliable information.
Real-World Success Stories
We’ve seen tangible results with our clients:
- Unlocking Promotional Insights: A CPG client was blind to their retailer’s promotional activities, impacting forecast accuracy. By helping them leverage API feeds, Algo enabled them to capture crucial promotional data previously unavailable, leading to a significant improvement in their forecasting.
- Optimizing Perishable Goods: For a food and beverage client dealing with short shelf lives and high spoilage, we helped them move beyond historical shipment data. By re-evaluating return data and integrating it with sales, we enabled a more accurate understanding of true demand, leading to reduced waste.
- Bridging the EDI Gap: A large enterprise client faced 5-7 day delays in their sales data via EDI. Algo helped them transition to API feeds, drastically reducing the lag and providing near real-time data for improved forecasting accuracy.
The Bottom Line: Trust Your Data, Trust Your Forecast
You don’t need perfect data to start, and nobody has it. Data readiness is a journey, not a destination. But with structured, usable data that your internal team trusts, you can unlock the full potential of AI for demand planning. AI is a powerful multiplier, and when built on a solid data foundation, it can truly transform your business planning, drive significant ROI, and empower your teams to focus on strategic insights rather than manual reconciliation.
Ready to build your foundation for AI-driven demand planning? Let’s talk about your data.
About the author
Algo
Combining human centered AI with deep domain expertise, Algo’s analytics enriched supply chain intelligence platform helps suppliers and retailers plan, collaborate, simulate and execute a more efficient supply chain.
