Inventory and stock management

Artificial intelligence

In forecasting and optimizing hobby industry

Inventory management in the hobby goods sector has its own peculiarities, different from other industries. Such dissimilarity requires flexibility, consideration of seasonality and current trends, and careful data analysis. All of this is done to ensure optimal inventory levels and to meet customer needs.

branza hobby

Demand forecasting process

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Process improvement challenges are related to the following areas, among others:

- Seasonality and trends: The hobby goods industry is characterized by volatility and seasonal peaks in demand. For example, items for outdoor activities are more popular in spring and summer. The key here is to skillfully and precisely adjust inventory to cyclical changes and consumer preferences.

- Wide assortment: Companies in this industry offer products in a variety of categories, from sports equipment to art supplies. Managing such a diverse assortment requires taking into account many factors, such as the impact of calendar events, such as the timing of sports competitions, which can determine the need for different strategies for different product categories.

- Some hobby items are characterized by longevity and do not spoil, which theoretically allows them to be stored for longer. However, it is important to keep in mind the dynamic volatility of consumer trends, which results in the emergence of new product versions. Selling products from previous collections may therefore require organizing a sale.
- Seeking the point of minimum total costs: Companies operating in the hobby industry must constantly balance inventory costs with customer service quality (Service Level), while taking into account various constraints such as seasonality, storage capacity and available budget. The inventory optimization process should take all these elements into account to find the optimal point that balances cost and customer satisfaction.

Dature uses advanced technologies in the form of artificial intelligence (AI) and machine learning (ML) to accurately forecast demand and optimize inventory. The application uses specialized algorithms that allow personalized forecasts for individual SKUs. They take into account not only internal company data, such as sales, promotions or new product releases, but also external information, such as industry events or specific calendar days. Such a holistic approach significantly improves forecast accuracy, minimizing the risk of out-of-stock and overstock). The system also offers tools for simulation and inventory optimization, allowing for continuous improvement of the entire forecasting and optimization process. This directly translates into financial benefits, by increasing sales, margins and improving liquidity, and strengthens the company's image in the market. In effect, the company is gaining a significant competitive advantage.

Potential business benefits

Reduce forecasting errors by 30% to 50%.

Reduce lost sales by up to 65%,

Reduction in transportation and administration costs by 5-10% and 25-40%, respectively.

Reduce excess inventory by 20-50%.

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