| Inventory accuracy is a critical concern in most industrial environments. Specifically, when on-hand inventories don't match recorded inventories, time is spent rectifying observed problems. Even though the activities, which span corrections to the data base to expedited replenishment, demand resources to rectify human error, they are necessary to satisfy customer expectations. If the inventory accuracy is poor, tangible costs (e.g., the loss of customer good-will) are realized. Cycle counting is a proven methodology used to monitor inventory accuracy on a continuous basis. It requires that items kept in inventory be counted periodically to ensure an accurate inventory. This approach requires 100% inspection of all stock keeping units maintained in inventory on a periodic basis. This work demonstrates the effectiveness of a statistical process control (SPC) approach to monitoring inventory accuracy as an alternative to cycle counting. The benefit of such an approach is that random samples are utilized in lieu of 100% inspection. In this research, we document the unique statistical properties of inventory scenarios found in large retail/warehousing environments. The robustness of SPC, specifically the p chart, in these environments is measured through computer simulation and resulting probabilities of Type I and II errors are presented. |
