Lila

Plan your product assortment and split your budget by category

Purchase order management

Variant management

Demand forecasting

Product analysis

Budget management

Assortment planning

About Lila

Launched Oct 16, 2024

Built by Lila Solution Inc

Related Categories

Description

Lean inventory and lifecycle automation. A purchase planning app for fashion or seasonal products

Reduce analysis time and make smarter product decisions with Lila. We developed a tool to help you conceive an optimal assortment planning. Lila make you save 40 hours of analysis per month and help you reduce your end-of-year inventory. Lila helps you forecast the demand, work your budget split and gives you an open-to-buy to follow for your new collections. Lila gives you hard to get insights of your product performances and suggestions based on the history of comparable products.

Determines optimal purchase budget by category with the Open to buy
Forecast your sales and see the impact on your yields and budget
Consult different insights with the Product Analysis feature
Send purchase orders to you suppliers and receive inventory
Know how to split your purchases per sizes or any variants

Pros

Reduces analysis time by 40 hours per month, making it efficient for users
Helps in optimal assortment planning which aids in better product decisions
Assists in reducing end-of-year inventory, potentially lowering costs
Provides demand forecasting and budget split assistance for more accurate planning
Offers open-to-buy guidelines, helping businesses with new collection launches
Delivers insights into product performance and suggestions based on historical data
Includes features for sending purchase orders to suppliers and inventory management
Allows users to distribute purchases by sizes or other variants

Cons

Complex functionality could pose a learning curve for new users
May require integration with other systems, potentially complicating setup
Might not be suitable for smaller businesses with less need for extensive analysis
Relies heavily on the accuracy of historical data, which could affect forecasting