Inventory optimization through smart allocation: Automatic determination of order quantities, delivery split and dynamic article clusters.
Allocation is a fundamental discipline for retailers of all formats: Whether department store or verticalized manufacturer, discount or premium, the decisive factor is to place each individual item in exactly the right place at the right time. This is the only way to achieve the lowest possible costs, minimum discounts and maximum profit. The particular challenge for the allocation of every retailer is accurate demand forecasts – in volatile environments with highly seasonal assortments and constantly changing trends.
Sophisticated causal models, which also include price, promotions, seasons, holidays and other events in the calculation of demand developments, are elementary for precise forecasts.
Using self-learning algorithms, it is possible to forecast the exact demand and thus determine the optimum order quantity and the right time for the allocation of seasonal articles. And this is more accurate, faster and more effective than with any conventional calculation method. Internal data, enriched by external data – for example weather and competition – are the raw material for calculations that are prepared for all eventualities. Like the human brain, the algorithms react to change, learn continuously and can solve tasks more precisely and accurately thanks to the way machine learning works. A comprehensive set of rules in the background also ensures consistent implementation of the corporate strategy.
From the initial allocation to the final push, the system determines the optimum allocation quantities, always adjusted to the available capacities and sales areas. In order to avoid surpluses, deliveries are sensibly divided and holdbacks are formed based on demand analyses as well as logistics and handling costs.
As a result, exactly the right items are allocated to each individual store – and at exactly the right time. This is how retailers exploit their full sales potential!
Key Feature 1 – Multilevel Allocation
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.
Key Feature 2 – Predictive Analytics
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.
Key Feature 3 – Holdbacks
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.