Tl;dr: Improving forecasting with AI-powered Intuendi, especially during times of unpredictable demand, reduced forecast error by 36% to 62% for La Casa de las Baterias across nearly 1,000 SKUs, all while strengthening demand planning accuracy across four countries and cutting reliance on spreadsheet-based planning.
La Casa de las Baterias, one of Central America’s leading battery and energy solutions providers, operates in a supply chain environment where forecasting accuracy directly impacts profitability, service levels, and growth.
With nearly 1,000 SKUs distributed across multiple countries and branches, the company faced increasingly complex inventory planning challenges. Long supplier lead times of two to six months, container-based purchasing requirements, and highly variable customer demand made accurate forecasting difficult using traditional planning methods.
For Juan Raúl Gómez, Regional Procurement Manager, it became clear that spreadsheet-based forecasting could no longer support the scale and complexity of the business. Hundreds of formulas, manual adjustments, and disconnected data points created a planning process that was slow, reactive, and vulnerable to costly errors.
The company needed more than better spreadsheets. It needed a modern demand forecasting system capable of transforming unpredictable demand into reliable, data-driven decision-making.
The Challenge with Unpredictable Demand
Demand forecasting in supply chain management becomes exponentially more difficult when businesses must balance long lead times, fluctuating demand patterns, minimum order quantities, and inventory investment constraints simultaneously.
La Casa de las Baterias faced several interconnected forecasting challenges:
- Supplier lead times ranging from two to six months made inaccurate forecasts expensive and difficult to correct
- Large minimum order quantities and container-based purchasing increased the financial impact of forecasting mistakes
- Demand fluctuations varied significantly across branches, product categories, and seasonal cycles
- Spreadsheet forecasting required extensive manual intervention, limiting speed, scalability, and consistency
- Inventory imbalances created operational risk, resulting in both overstock and understock situations
Traditional forecasting methods struggled because they relied heavily on static historical analysis and human interpretation. As the business expanded, planners had to process increasingly large volumes of data while trying to account for external variables, seasonal demand shifts, supplier constraints, and changing customer behavior.
The result was forecasting uncertainty that affected every stage of the supply chain:
- Purchasing decisions became harder to validate confidently
- Inventory investments carried higher financial risk
- Stockouts and excess inventory became more difficult to prevent
- Planning cycles consumed valuable operational time
- Forecast reliability varied significantly across SKUs
The company needed a forecasting solution that could continuously adapt to changing conditions and improve decision-making at scale.
The AI Demand Planning Solution
To modernize its forecasting capabilities, La Casa de las Baterias implemented Intuendi’s AI-powered demand forecasting and inventory planning platform.
Rather than relying solely on spreadsheets and historical averages, the company adopted a predictive forecasting approach powered by artificial intelligence and machine learning.
Intuendi AI enabled the company to:
- Analyze historical sales trends alongside seasonality, external causal factors, and evolving demand signals
- Generate SKU-level demand forecasts tailored to product behavior, sales velocity, and supply constraints
- Continuously refine forecasting models as market conditions and purchasing patterns changed
- Provide planners with actionable forecasting insights that supported faster, more confident purchasing decisions
- Improve alignment between inventory planning, procurement strategy, and branch-level demand realities
This represented a major shift from reactive forecasting toward intelligent demand planning.
Instead of manually building spreadsheets and attempting to interpret hundreds of variables independently, planners gained access to continuously updated forecasting intelligence designed to reduce uncertainty and improve accuracy across the supply chain.
As Gómez explained:
“Before, we spent tens of hours building spreadsheets just to guess what to order. Now, we can trust the forecasts and focus on growing the business.”
The company was no longer forecasting based on assumptions alone. It was forecasting using adaptive AI models capable of learning from real operational behavior.
The Results
By implementing AI-powered demand forecasting, La Casa de las Baterias significantly improved forecast accuracy, operational efficiency, and inventory alignment across the business.
Key results included:
- A 36% reduction in forecast error during the initial implementation period
- A 62% decrease in absolute forecast error for the year
- Improved alignment between supply and demand across all branches
- Faster, more reliable purchasing decisions supported by data-driven forecasting insights
- Reduced time spent on manual spreadsheet calculations and forecast adjustments
The operational impact extended beyond forecasting metrics alone. More accurate demand forecasting helped the company reduce inventory risk, improve service levels, and strengthen confidence in procurement planning.
The results also demonstrated a broader industry reality: AI demand forecasting systems can outperform traditional spreadsheet forecasting methods in complex, multi-variable supply chain environments.
The Advantage Gained
La Casa de las Baterias recognized that forecasting is no longer simply a planning exercise — it is a strategic capability that influences revenue protection, inventory optimization, customer satisfaction, and long-term scalability.
By combining the expertise of its procurement and planning teams with Intuendi’s AI-powered demand forecasting technology, the company transformed forecasting uncertainty into actionable supply chain intelligence.
The result was a more agile and resilient operation capable of:
- Making smarter purchasing decisions
- Reducing forecast-driven inventory risk
- Improving supply chain efficiency
- Supporting continued business expansion with greater confidence
In an increasingly volatile global supply chain environment, La Casa de las Baterias demonstrated how AI demand forecasting and predictive inventory planning can help distributors move beyond reactive planning and build a scalable competitive advantage.
