Retail & eCommerce
Demand Forecasting
Meeting customer demand without overstocking is one of retail’s perennial challenges. Predictive demand forecasting models use machine learning to analyze sales history, seasonal trends, weather patterns, promotions, and external signals like competitive insights, social sentiment or local events.
These models provide granular forecasts by product, store, region, and channel—empowering inventory planners to make smarter replenishment decisions. Retailers can reduce stockouts, avoid overstock penalties, and keep fulfillment costs under control.
Most importantly, forecasting models continue to learn over time. They adjust to new buying patterns, regional shifts, and customer preferences, keeping your supply chain nimble and customer-ready.
.Client Success
Change Management Strategy for a Successful ERP Business Transformation
In any large-scale business transformation, the goal isn’t just to solve immediate challenges—it’s about driving the organization toward its long-term vision.
The Integration Challenge Making Sense of Fragmented Cybersecurity Solutions
With an ever-evolving cyber threat landscape, organizations are juggling a growing number of cybersecurity tools and specialized teams to manage them. From basic endpoint detection
The Evolution of Supervised Learning From Data Labeling to Annotation for RLHF
We humans have experienced forms of supervised learning throughout our lives, starting from hearing “good job” from our parents to receiving “employee of the month” awards at work.
