Kajal improves order management for stores using Frogtek

“Effective order planning is critical to ensuring product availability for our grocers and providing an exceptional shopping experience for their customers. Our commitment to excellence in demand management, inventory control and order planning led us to seek a state-of-the-art solution that would increase our visibility into demand and stock, thereby driving grocers’ growth.”

Guillermo Caudevilla, Frogtek CTO

Frogtek, in its commitment to support small stores in Latin America, has partnered with ITA to provide intelligence to its procurement system, which records sales and stock data. However, it needs an improvement in the ability to plan orders to suppliers in real time. This is especially relevant for small stores in the region, many of which are not yet computerized.

The Kajal tool’s real-time ordering proposal is adapted to the reality of the stores. For example, consider the storekeeper’s liquidity at any given time.

This collaboration promises to transform procurement management and further strengthen local communities throughout Latin America.

About Frogtek

The Frogtek Group is a socially responsible company focused on empowering shopkeepers in developing nations to improve their economic conditions and compete more effectively in the marketplace.

Its main tool, Tiendatek, is an application designed to help shopkeepers manage and optimize their businesses. As a result, they achieve an average 15% increase in sales during the first year of use.

With approximately 4,000 stores in Mexico already benefiting from Tiendatek, and more than 20 leading global companies relying on the data provided by Frogtek to improve their operations and strategies.

The Frogtek Group has established itself as a profitable and growing company. Its store network and turnover have experienced an annual increase of more than 40% in recent years.

With a global presence and a distributed work structure, Frogtek operates in Mexico and Colombia for sales, training and shopkeeper support, while technology development is led from Spain.

Initiating the Transformation

Frogtek decided to improve its procurement management process to meet changing market demands and ensure an optimal experience for its customers.

The company needed to have visibility of the products that stores buy from each supplier, for which a fully parameterizable algorithm was created.

With it we obtain the sales for each supplier by zone, as well as the costs and prices at which they are usually bought and sold.

In order to improve the supply required by Frogtek, it was essential to forecast the demand for each product.

For this purpose, the Kajal Forecasting® methodology makes it possible to characterize products based on the value they provide and their demand for forecasting.

In response to Frogtek’s need to calculate inventories for each store, the Kajal Inventory® system was used.

By means of probabilistic calculations and taking into account the service level chosen for each product, stock sizing is carried out.

The Kajal Replenishment® methodology was used to plan orders in real time based on demand forecasts and stock calculations.

A solution that obtains the order for each product in the store based also on the purchase history of each shopkeeper.

In addition, it incorporates an algorithm that allows to obtain the most suitable order according to the economic constraints of each store.

Challenges of the highest caliber

• Overcoming the Lack of Computerization: Addressing the lack of computerization in these stores is essential to implementing effective technology solutions.
• Adapt to Market Diversity: The variety of products and formats in small Latin American stores demands a versatile sourcing strategy.
• Demand Modeling: Demand forecasting is a critical aspect of supply management. The project faced the challenge of developing different models based on statistical calculations, machine learning or deep learning.
• Order Planning Optimization: Design an efficient system to determine appropriate inventory levels and generate optimal orders with a limited budget. This required the incorporation of logic and algorithms to consider the stores’ purchase history and existing stock levels.

Results obtained

• Obtaining the demand forecast for each product based on its history.
• Automatic selection of algorithms, according to a model benchmarking system.
• Inventory sizing according to probabilistic models that adapt to the pace of demand.
• Dynamic calculation of target stock (and safety stock) for each product for more than 4000 stores.
• Obtaining a logical order based on the needs of the store and its purchase history.
• Creation of an algorithm to optimize orders according to a maximum economic quantity.
• Obtaining a filtered product catalog of each supplier by zone.
• Obtaining prices and costs for each product based on statistical measures.
• Characterization of each store’s products based on the added value they provide, their variability and intermittency.

Authors: Guillermo González and Lorena Polo.