Sales and Demand Forecasting powered by Machine Learning
Aavatar’s predictive analytics solution leverages machine learning to analyze both external and internal business data, delivering precise demand and sales forecasts. This enables businesses to make informed decisions, optimize inventory management, and enhance overall operational efficiency. By harnessing the power of data, Aavatar helps companies stay ahead of market trends and improve their strategic planning.
WHY FORECASTING
Sales & Demand?
To reduce operational costs:
Optimal warehouse stock management is crucial for maintaining efficiency and profitability. By leveraging demand prediction, businesses can manage their warehouse inventory more effectively, reducing the amount of illiquid items while ensuring they meet customer demand. Machine learning techniques play a vital role in this process, as they account for seasonal fluctuations and overarching trends, significantly enhancing the accuracy of forecasts.
To increase sales:
Efficient product range management is key to optimizing inventory and maximizing sales. By accurately understanding the demand for specific products, businesses can identify and eliminate illiquid items from their offerings. This strategic approach allows companies to introduce more popular, high-turnover products, ultimately enhancing overall profitability. By focusing on the most liquid items, businesses can ensure they are catering to customer preferences while improving inventory turnover and operational efficiency.
Business applications
Demand forecasting, leveraging predictive analytics, is a versatile tool applicable across various business sectors and industries. The underlying mathematical methods and engineering techniques remain consistent, but the specific tasks and goals may vary based on industry requirements. Here are some industry-specific applications of demand forecasting:
Selling products to end customers.
Introducing new products to the sales channel.
Dispatching goods and materials from warehouse inventory to production.
Transferring products from production to the warehouse.
Other related business tasks
Businesses often face challenges when dealing with new products that lack historical demand data, especially if previous sales at a particular point of sale were minimal or nonexistent. In such scenarios, predictive models can leverage generic statistics from similar product categories to generate insights. Here’s how this process works:
SOURCE DATA
Historical data on demand and sales volume
Reference data on product characteristics, purposes, and interchangeability
- Production offering volume and competitors’ pricing
- Statistical data
- Currency exchange rates
- Additional data relevant to the specific industry
- Historical weather patterns
- Forecasted weather conditions
- Environmental regulations and changes
- Market trends related to sustainability
- Resource availability and depletion rates