https://store-images.s-microsoft.com/image/apps.6051.372294dd-5191-49a0-a24e-54694a7dba72.9b19d2a7-df77-47dd-94f2-5fabce46f440.ebfc6a86-a23b-4d37-adbf-f1dd1891e521

ML-powered Dynamic Order Allocation Solution for Global Enterprises by Tredence

by Tredence Inc

An ML-based solution that can help firms deal with significant demand variability across channels

Develop a dynamic sourcing mechanism that provides the best sourcing options for customer orders based on inventory availability, labor, and shipping capacity constraints and considers trade-offs on shipping costs, penalties, service level impacts, and lost sales.

This solution can address the following challenges:
1. Increased variability in demand, resulting in stock outs
2. Difficulty in sourcing from alternate locations; manual rerouting of orders is cumbersome, costly, and time-consuming.
3. Labor and shipping capacity issues at DCs result in challenges in fulfilling orders from DCs with stock availability.

How do we address your challenges?
The solution will fulfil customer orders using the following modules and functionalities:
1. An optimization model that allocates customer orders to DCs based on set business rules and assigned constraints
2. Provide trade-off options for planners to approve or reject system recommendations with reason codes.
3. Build dashboards to track KPIs and their financial impact.
4. Automate the system output to ERP systems.

At a glance

https://store-images.s-microsoft.com/image/apps.12993.372294dd-5191-49a0-a24e-54694a7dba72.bc0ab6ee-e653-49d8-97e2-771ae151bd3d.11745f01-affa-4991-927e-697b90b2eec2