Lama Group

For a long time, the Lama chain's inventory management was based entirely on orders from the merchandisers working in stores. When generating orders with such arrangements in place, the inventory management process had a number of flaws:

1. the quality of orders for goods was completely subject to the professional skills of the merchandiser with stores, whose merchandisers were not very competent, more frequently facing a shortage of popular items or, alternatively, warehouses being overstocked with certain, not popular goods;
2. despite the fact that good merchandisers are able to effectively create orders, the abilities of even an experienced merchandiser are limited and in practice, high-quality orders were created for the most popular items while the rest of the product mix was ordered in a manner that was far from perfect;
3. there were often mistakes due to poor attention to detail. As a result, sometimes they would simply forget to order necessary goods.

As the chain developed and competition on the part new market players toughened, the Lama retail chain was forced to optimize its efficiency and one of the priorities was to improve the quality of inventory management. To address the above-mentioned order-generation problems, it was decided to automate this process as much as possible and to implement an inventory management system, based on consumer demand forecasts.

Basis of the approach

GoodsForecast (called Goods4Cast at that time and headed by its parent company Forecsys) was chosen to automate the inventory management process.

 The GoodsForecast.Replenishment system features a vast library of forecasting algorithms and enables to automatically select the best forecasting model for each ‘product-store’ combination.

Thanks to the features of its forecasting algorithms, GoodsForecast’s solution enables calculating the optimal level of safety or insurance stocks. Optimal inventory levels are achieved using a quality functional that evaluates relationships between the demand fluctuations risk and costs to be incurred to keep additional inventory in the store.

Effect from implementing the system

The pilot operation of GoodsForecast’s System resulted in a significant improvement of the main quality indicators of the inventory management process with inventory balances decreasing by 9% and service level in stores increasing from 90% to 95%.

In addition, the solution managed to reduce payroll costs (due to the essential automation of the order generation process in stores).