On the stage at ERPNext Conference 2022, the co-founder of Invento Software Limited, Aubdulla Al Munim delivered a very insightful session on how machine learning can truly revolutionize retail operations in Bangladesh and beyond. His talk mainly focused on equipping retailers with intelligent tools to anticipate demand, streamline inventory management, and develop the overall business efficiency.
Aubdulla Al Munim began his talk by highlighting common frustrations for retailers, such as:
- overstocking, outdated stock, and missed sales opportunities sometimes.
- introduced a machine-learning system that can dynamically predict well:
- Which products will sell out soon
- When to reorder and in what quantities
This system uses basically historical sales data and seasonal trends which can forecast future demand. Also to help retailers stay ahead without manual guesswork.
Behind the Model: Data-Driven Forecasting
Aubdulla Al Munim further offered solutions in his talk, which are a true high-level view of the model’s architecture:
1. Data Collection – Pulling in past sales, promotional campaigns, and seasonal patterns.
2. Labeling – Defining the forecasting targets (e.g.,) daily or weekly sales per product.
3. Model Training – Using regression-based algorithms and time-series techniques.
4. Continuous Improvement – Retraining the model with the new sales data for adaptive accuracy.
He further emphasized that ERPNext absolutely ensures seamless integration for users already managing sales and inventory in one platform.
Presented a Live Demo: Forecasts in Action
In a live demonstration, Munim walked the audience through:
- Uploading sales data into ERPNext
- Generating the original product-level demand forecasts
- Reviewing visual forecast summaries
- Adjusting reorder points and minimum stock levels as well
Retail teams then saw how forecasts could finely reduce stockouts and overstocking—with just a few clicks.
How It Matters for Bangladeshi Retailers
Since many retailers are operating on tight margins and limited infrastructure, small errors in inventory planning can cause big losses. Invento’s solution is particularly for:
- Small-to-medium retailers seeking automation
- Businesses wanting to reduce waste and enhance profitability
- Local enterprises ready to embrace AI-driven efficiencies
Pilot & Collaboration
Finally retailers and developers are invited to join the early pilot program. Aubdulla Al Munim encouraged collaborative feedback to fine-tune the model for diverse retail segments, from groceries to apparel.
Final Word
Aubdulla Al Munim’s session overall showcased how machine learning, when embedded into a familiar ERP like ERPNext, and can transform retail operations. By combining predictive analytics with the intuitive dashboards, Bangladeshi retailers eventually can optimize inventory, reduce financial risk, and modernize their approach to business planning.
