Case Study – Manufacturing: Back Order Issues
Business situation: Company had issues with certain products going on back-order. The sales forecast was generally good in aggregate but was highly variable within the quarter and by SKU (n > 50). SNOP focused on point estimates (averages) without consideration of the variability week to week and by SKU.
The approach: Developed a Monte Carlo model that highlighted the limitations of using point estimates for highly variable inputs. The model identified the products that are most likely and least likely to be on back-order.
Business outcome: Individual SKU level inventories were adjusted (+/-) to decrease likelihood of back-orders while maintaining overall inventory levels that met guidance set by Finance. Revenue impact of +$2M-$4M a quarter (~5% of total revenue)
