Case study: Stock management in retail

In the fashion industry, it is of vital importance to stock and replenish stores optimally and in accordance with market demand. COSMO CONSULT offers a dynamic and flexible solution tailored precisely for this challenge. It provides yield-optimized proposals for reordered or redistributed quantities, without having to maintain costly target stock level

• Stock management that responds to market demand
• Yield-based stock optimization that takes delivery times and delivery conditions into account
• Increasing margins and reducing residual stocks

In the fashion industry, replenishment and redistribution are often carried out on the basis of manually defined target stock quantities. Delivery times and conditions –  such as minimum order quantities - or short-term changes in demand are usually ignored. COSMO CONSULT has thus developed a dynamic and flexible solution for such automated redistributi

A time series analysis method was used to define effects such as season, cycles, and special offers on the merchandise category level. The current stock situation is then analyzed per article, store and store size. The measured effects can then be used to forecast demand over the rest of the season. The forecasts are included in the optimization model along with parameters such as delivery times and delivery conditions, which provide yield-maximizing replenishment and redistribution quantities based on this information and additional rules, such as minimum stock quantiti

Our solution provides yield-optimized suggestions on reordered or redistributed quantities. Depositing and maintaining target stocks per SKU (Stock Keeping Unit) is thus not necessary any more, and the associated time expenditure is eliminated. Nevertheless, the parameters are only taken into account by the optimization model if they are specified. In addition, seasonal effects and actions can be explicitly considered. The algorithm thus reacts in a proactive manner. Delivery times and complex delivery restrictions such as minimum order quantities can also be incorporated into the model. This results in a yield-optimized solution that takes into account all relevant underlying conditions.