Automate order quantities, delivery split and dynamic article clusters
Display your products at the right time in the right place
The primary challenge is accurate demand forecasts – especially in volatile environments with highly seasonal assortments and constantly changing trends. Sophisticated causal models, which incorporate price, promotions, seasons, holidays and other events in the calculation of demand developments, are key.
How we get you ready for the next season with inventory allocation
Internal data, enriched by external data – for example on weather and competition – are the raw materials for calculations that are adjusted to all eventualities. Like the human brain, the algorithms react to changes. They are constantly learning new things and are able to solve tasks more accurately. At the same time, a comprehensive set of rules in the background ensures consistent implementation of the corporate strategy.
Benefit from inventory optimization through smart allocation
As a result, exactly the right items are allocated to each individual store at exactly the right time, thereby increasing availability for your customers. This is how retailers exploit their full sales potential! Are you ready to get your performance back on track?
Initial and subsequent allocation as well as the final push place very different demands on the calculation of the optimal order quantity over the course of the season: The initial allocation is determined by pre-season planning, in the subsequent allocation additional stocks are allocated in accordance with current sales forecasts. The final push ensures the reduction of residual stocks. Depending on store-specific capacities and requirements analyses, the algorithm calculates the optimum order quantities for each individual allocation level.
In the price-driven retail industry, with its short lifecycles and rapidly fluctuating trends, merchandising must react quickly to changes. Sometimes this can mean a high degree of uncertainty. The aifora allocation is able to reliably forecast the sales behavior of an item without a demand history. Using similar and existing articles, product groups or categories, sales figures are projected, and well-founded forecasts are modelled.
Surpluses occur if the articles that are delivered to a store do not correspond to the actual demand of that store. In order to specifically avoid surpluses, it may make sense to hold back part of the delivery. Based on demand analyses, and taking logistics and handling costs into account, the software calculates holdbacks that deliver real, verifiable benefits. In this way, the risks caused by surpluses can be ruled out.