Luggage and handbag company improves forecast accuracy and planning visibility 

The Challenge

A U.S. luggage and handbag design company that produces a wide range of products—including women’s handbags, luggage and travel items, fashion and home accessories, and gift items—needed to improve forecast accuracy and planning at the SKU level. The organization initially relied on Excel spreadsheets to plan pre-season and in-season buys, limiting its ability to consistently measure forecast accuracy and plan effectively across channels. 

While the company had implemented enterprise planning to standardize processes across channels and support active SKU-level planning, planners lacked controlled yearly and monthly forecast snapshots to accurately measure performance. Forecasted inventory calculations also did not account for minimum presentation quantities (MPQ) or safety stock, and allocators had limited visibility into in-stock percentages when reviewing and prioritizing worklist lines. 

The Solution

Highspring supported the six-month engagement using the Blue Yonder PlanNow methodology, enhancing forecasting and allocation processes to improve accuracy and visibility. Two forecast versions—Original SKU Plan (OKP) and Revised SKU Plan (RKP)—were introduced, along with the required code to submit working plans through an admin-controlled process. Forecast calculations were expanded from three variables to four, with parameters configured to support more accurate inventory planning. In addition, two new columns were added to the allocation worklist, enabling business intelligence values to flow directly into allocation workflows.

Our Impact

With locked plan versions in place, the company gained the ability to measure forecast accuracy more effectively. Splitting the inventory calculation allowed planners to more accurately account for both safety stock and minimum presentation quantities (MPQ), leading to more precise buys and fewer stock overages and outages. 

Increased visibility into in-stock percentages also enabled allocators to better prioritize worklist items and respond more quickly to products at risk of selling out. As a result, the engagement delivered higher demand forecast accuracy, more responsive allocation planning, and a reduction in overall stockouts across stores.