One of the main challenges for many businesses is the inability to upgrade legacy systems typically due to cost, complexity and risk to ongoing operations. This used to mean having to live with limitations particularly around lack of key data and process inefficiencies. However, modern data analytics tools are designed to address these issues. Firstly data can be extracted from legacy systems and cleansed, transformed, enhanced and merged with other data sources to meet reporting and analytics needs. Secondly, data management processes can be automated so that data can be extracted, decisions taken, and outputs re-entered back into legacy systems in the required format.
One example from recent experience is Auto Replenishment. The replenishment module of the legacy system was extremely limited, so we built a more sophisticated replenishment model using Power BI. This took performance data (sku details; daily unit sales, intake and stock; orders) then combined it with other data sources (such as competitive pricing) and applied a series of calculations and filters to generate a recommended order. These calculations included a highly accurate rate of sale (ROS) based on the item being in-stock; a target stock cover level based on a user-defined weeks cover input; a minimum profit requirement; and a re-order level adjusted for case packs and minimum order quantities. Finally, the order was re-formatted to be able to input back into the legacy system for approval.