Our client, an American luggage and handbag design company produce a variety of products, including women’s handbags, luggage and travel items, fashion and home accessories, and unique gifts. They partnered with Plantensive to address their forecast accuracy issues. The project lasted 6 months using the Blue Yonder PlanNow methodology.
Challenge
Our client had planned originally with the use of Excel spreadsheets to determine their pre-season and in-season buys. They used Enterprise Planning to standardize their business process and to be able to appropriately plan by each channel of their business along with a focus on active SKU level planning. The client’s planners needed a controlled yearly and monthly snapshot of their SKU level forecasts in order to measure forecast accuracy more effectively. The current calculation for forecasted inventory did not allow for planners to address product with Minimum Presentation Quantities (MPQ) and Safety Stock. Also, the allocators did not have visibility to the In-Stock percent of the product when reviewing and prioritizing their Worklist lines.
Our Solution
Plantensive managed this project by assisting the client with the following:
- Two versions, Original SKU Plan (OKP) & Revised SKU Plan (RKP), were added along with the required code to submit the working plan to these versions under an admin-controlled process.
- The current calculation was enhanced from 3 variables to 4 variables and the parameters were configured accordingly.
- Two additional columns were added to the Worklist table to allow the values from BI to be sent to Allocation.
Our Results
With locked versions in place, the forecast accuracy was able to be measured more accurately. Also, splitting the calculation allowed for the required inventory to be accurately planned by both Safety Stock and Minimum Presentation Quantities (MPQ). This resulted in more accurate buys and fewer stock overages/outages. Finally, the visibility to the In-Stock percentage of their product allowed the Allocators to more accurately prioritize and react to those products that were in danger of selling out in their stores. The lasting impact of the project includes higher demand forecast accuracy, more responsive planning in Allocation, and a reduction in overall stockouts across all stores.
