We use machine learning to ensure maximum efficiency in replenishment and transfers between stores in an omnichannel scenario.
Planners know that accuracy in forecasts is essential to increase profitability. This enables you to avoid lost sales due to stockouts, increase margins by not doing any unnecessary promotions, and reduce overstocks.
Our predictive algorithms offer daily suggestions that take into account trends, seasonality, peak demand and the estimated impact of promotions to ensure the ideal stock in each store. Plus, with complete responsiveness to anticipate changes in demand at each product level, channel and point of sale, you can guarantee that the correct level of product is available.
With our consultants you have a strategic route to improve the profitability of your company through stock management.
Our KPIs (product mix, turnover, payback, GMROII and potential lost sales from stock-outs) are the most effective ones for assessing operational and financial efficiency. They let you identify where problems are and indicate how to correct them.
In-Season predicts the demand for each product-size-store every week throughout the product lifecycle up to a maximum of 52 weeks so you can manage your inventory well in advance.
Our automatic purchase order calculation tells you which products the company’s total stock (current + pending) cannot meet demand for during the lifecycle, so you can avoid stock-outs.
Identify the replacement products within a collection and coordinate managing them to prevent lost sales at moments of scarcity.
Identify the products which cause sales cannibalization: you can include the cannibalization percentage into the demand calculation to avert overstocks.
In-Season monitors the levels of lost sales and takes this into account in producing the forecast. It also identifies the articles affected by stock shortages so this can be corrected.
We monitor how the collection mix behaves over the season and raise alerts about the products causing problems. We show you the optimal levels of assortment per store to avoid overstocks from excess horizontality, telling you the Ideal Horizontality.
The system has functionality to raise an alert when it detects that stock shortages may prompt lost sales over the next few weeks, enabling proactive purchasing decisions.
We monitor the saturation level per store, in accordance with your sales area/volume rules and indicating the best option.
These alerts flag anomalous behavior in the sell-through comparison for each product in the store compared to the total for the company and its group.
Warning of possible speedups and slowdowns in sales with respect to historical/expected behavior, letting you take action in advance.
In-Season lets you create all kinds of dynamic online reports with all the dimensions and expressions used by the tool, to streamline decision-making.
This includes issues such as defining different VM stock levels and managing the display rules to facilitate control of the brand image.
This offers automated calculation of the amount of stock we should bring in for a new product at a given moment of the collection.
This lets you automate this complex operation by identifying the best options to ensure the extinction of toxic stock and fewer lost sales. There are different kinds of transfers: necessity, to avert size stock-outs, or concentration to satisfy the RVM rules.
Identify the products with forthcoming promotions: you can include the expected growth percentage to guarantee correct supply of the goods and take this into account in the future demand to avert overstocks.
The tool offers automated calculation for replenishment from warehouse to store. It takes into account the forecast levels of demand, based on historical data and trends, added to the different service lead times, the coverages defined, VM criteria and other aspects.
In-Season has an algorithm that helps us to optimize scarce resources, automatically assigning a reception priority based on the projected rate of sales.
This enables us to identify and track articles for which the units available exceed real demand over the lifecycle, thus averting medium and long-term overstocks.
This allows the lifecycle for each product to be adjusted to take decisions that maximize profitability.
It is possible to define variable coverages driven by business needs for the different product categories and stores. This is how we ensure the proper flow of goods to stores.
This sets an initial stock proposal for store openings, which can be modified as determined by the opening period, the mirror stores assigned and the initial coverage values.
This allows changes to be made to the envisaged seasonality if this is anomalous for some product so that changes in the seasonality curve can be corrected.
The tool analyzes the available overstocks at all stores and produces a daily report of the units to be returned to the warehouse to reach ideal stock levels, having counted transfers and the VM rules.