Inventory Service Levels and Exceptions
Detailed evaluation of inventory management processes and systems that dramatically improved the retailer's inventory position
A $2+ billion, nationally recognized multi-channel retailer of softlines and hardlines product using a recently installed ‘best of breed’ replenishment system.
The inventory planning & replenishment organization was faced with increasing pressure to reduce inventory and drive efficiency into their replenishment practices. In addition, their replenishment system was operating under the same specification under which it was installed. The executive team challenged the replenishment team with fixing their replenishment system as well as implementing replenishment management processes that would improve future system and inventory performance.
The Parker Avery Solution
The Parker Avery Group leveraged expertise in replenishment systems to evaluate 4 key areas of the replenishment system including target service levels, forecast exceptions, seasonal profiles, data integrity and store order cycles.
In order to define a solution, Parker Avery began by developing several hypotheses based upon initial observations of the inventory organization and systems. This was followed by a detailed analysis of several key areas of the replenishment system and inventory management processes, including:
- Analysis of the replenishment system source data
- Conducting simulations on service-level
- Evaluating parameters for safety stock
- Reviewing merchandise planning processes
- Evaluating forecast parameters and seasonal profiles
- Developed strategies to reduce forecast exceptions
Through this process, Parker Avery developed tactical recommendations for optimizing the replenishment system and a roadmap for improving the organization's replenishment processes practices.
The client was able to selectively reduce safety stock by 10-30% and store inventory by 2.5-7.5% and redeploy the savings to increase sales by 30-60 basis points. The client has also experienced a significant reduction (>50%) in the number of forecast exceptions that were caused by incomplete partitioning of promotional demand and inappropriate parameters that were set by default during system installation.