ML-Powered Demand Forecasting For a Canadian Ecommerce Company
Challenge
Our client — a Canadian clothing manufacturer and online retailer — wanted to go beyond Excel-based analytics and myopic demand forecasting, solely grounded on historical dynamics and static consumer buying patterns. With garment factories in Vietnam and a customer base in Canada, they expected an intelligent forecasting model to optimize inventory planning and supply chain management.
Our client — a Canadian clothing manufacturer and online retailer — wanted to go beyond Excel-based analytics and myopic demand forecasting, solely grounded on historical dynamics and static consumer buying patterns. With garment factories in Vietnam and a customer base in Canada, they expected an intelligent forecasting model to optimize inventory planning and supply chain management.
Solution
The client was already using Microsoft corporate products, such as Dynamics365, Azure SQL database, etc., so they wanted to keep their tech stack consistent and opt for Azure ML. Moreover, they were also leveraging Power BI for visual insights in other departments.
However, they lacked in-house capacity and expertise to manage this software implementation initiative. Therefore, the *instinctools team was tasked with developing and integrating demand forecasting and data visualization tools into the client’s software ecosystem.
The client was already using Microsoft corporate products, such as Dynamics365, Azure SQL database, etc., so they wanted to keep their tech stack consistent and opt for Azure ML. Moreover, they were also leveraging Power BI for visual insights in other departments.
However, they lacked in-house capacity and expertise to manage this software implementation initiative. Therefore, the *instinctools team was tasked with developing and integrating demand forecasting and data visualization tools into the client’s software ecosystem.
Results
Accurate demand forecasting means nothing if not followed with timely actions. However, outputs generated by ML models are primarily designed for data scientists, limiting their accessibility to other employees. To democratize ML tools across various departments, the client decided to use Power BI for data visualization.
Both Azure ML and Power BI being part of the Microsoft stack allowed for seamless integration, enabling automatic updates to visualizations whenever the prediction model is re-trained.
Results:
- Advanced demand forecasting with a deep learning engine at its core
- Holistic ML model incorporating multiple internal and external data sources
- Automated data processing and forecasting
- High-fidelity long-term demand forecasts
- Mitigated shortages and overstock
Business value:
- 92% demand forecasting accuracy
- Stockouts reduced to 10%
- Overstocks below 5%
Accurate demand forecasting means nothing if not followed with timely actions. However, outputs generated by ML models are primarily designed for data scientists, limiting their accessibility to other employees. To democratize ML tools across various departments, the client decided to use Power BI for data visualization.
Both Azure ML and Power BI being part of the Microsoft stack allowed for seamless integration, enabling automatic updates to visualizations whenever the prediction model is re-trained.
Results:
- Advanced demand forecasting with a deep learning engine at its core
- Holistic ML model incorporating multiple internal and external data sources
- Automated data processing and forecasting
- High-fidelity long-term demand forecasts
- Mitigated shortages and overstock
Business value:
- 92% demand forecasting accuracy
- Stockouts reduced to 10%
- Overstocks below 5%