Optimizing Inventory Forecasting with Time Series Models
How we helped an SME improve their inventory strategy using Prophet and data-driven insights.
Background
Inventory planning is a recurring challenge for small and medium-sized enterprises (SMEs). Too much stock increases holding costs and cash lockup, while too little stock leads to missed sales and operational disruption.
In this project, an SME approached us with a problem: inventory planning relied heavily on manual judgment and historical averages, which failed during demand fluctuations and seasonal changes.
The Problem
- No reliable demand forecast
- Overstock during low-demand periods
- Stockouts during peak demand
- Manual, intuition-based planning
- Limited visibility into trends and seasonality
Approach
- Clean and aggregate historical sales data
- Analyze trends, seasonality, and volatility
- Apply Prophet for time series forecasting
- Generate forecasts with confidence intervals
Why Prophet?
Prophet was chosen for its interpretability, robustness to missing data, and ease of communication to business stakeholders. It allows teams to understand why forecasts change, not just see the output.
Results
- More stable inventory levels
- Reduced overstock
- Fewer stockouts
- Improved planning confidence
Business Impact
The forecasting pipeline reduced holding costs, improved cash flow, and enabled proactive inventory decisions. The SME moved from reactive planning to data-driven strategy.
Conclusion
This project highlights how practical time series forecasting can deliver real operational value for SMEs when models are aligned with business needs and decision-making processes.