Retail Sales Management System "PARS Automatic Ordering"

For over 20 years, PALTAC has been developing and enhancing automated ordering systems based on our unique demand forecasting concept. In recent years, PALTAC has introduced AI machine learning technology to further improve ordering accuracy, mainly for product categories that are affected by seasonality and weather conditions.

Automatic ordering option (standard function)

Optimizes inventory storage and operation costs by categorizing sales volume and frequency of sales and optimizing the frequency of ordering (number of orders) and the number of inventory storage days.

Optimization of man-hours for receiving and productivity improvement of replenishment work

To improve store operation efficiency, it is important to understand the productivity of replenishment work, and analysis of sales data for the past 8 weeks supports the review of order placement units.

Control of inventory, shortages, and man-hours for receiving goods

Recommendations are made based on the company's inventory standards. Control is possible based on the company's standards, rather than on the judgment of each store.

Optimization of shelf allocation

By comparing the maximum number of inventory items that should be available with the actual number of items that can be displayed, appropriate shelf allocation can be determined and standardization can be achieved.

Continuous elimination of dead stock

To optimize shelf space allocation, we support the expansion of the face of hot-selling products and the elimination of dead products.

Realization of inventory and work cost optimization

PARS Automatic Ordering makes it possible to control the optimal order quantity according to the products handled and the display space in the store.

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Graphs showing optimal inventory and work cost values for each store

AI Automatic Ordering Option (additional functionality)

The AI machine learning engine creates and registers a demand forecast model, and is linked to the retailer's core system to narrow down target categories where AI can improve forecasting accuracy.

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Diagram showing the creation and registration of a demand forecasting model by an AI machine learning engine, its linkage to the retailer's core system, and the composition of the order quantity calculation.

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