Burgh Threads is a small clothing manufacturing business that produces simple, durable garments including T-shirts, jeans, and dresses. After being mentioned by a popular vlogger in a widely viewed video, the company experienced a significant increase in sales accompanied by declining fulfillment rates. The owner is hesitant to invest in additional equipment or workers due to concerns that the increased demand may be temporary. However, the company recognizes they are losing sales opportunities by having items out of stock and is seeking alternative ways to improve their fulfillment rates.
The factory operates as a make-to-stock system with finished goods inventory used to fulfill customer orders. When inventory for any of their 52 unique SKUs falls below the reorder point, a manufacturing order is released to the factory for the specified reorder quantity. The industrial engineer has identified substantial setup time for manufacturing orders and believes productivity could improve by increasing lot sizes. However, larger lot sizes may increase overall lead times and total inventory levels, creating a complex optimization challenge.
Management has agreed to allow the average finished goods inventory to increase to no more than 1000 items while aiming for a minimum 95% fulfillment rate across all SKUs. The case study requires developing a simulation model of the manufacturing process to determine optimal lot sizes and reorder points for each SKU. The model must demonstrate that the recommended strategy will maintain inventory below the specified limit while maximizing fulfillment rates and revenue over a 120-day period. Key performance metrics include average inventory levels, SKU-specific fill rates, and expected revenue.