Order Fulfillment: 12 week pilot implementation

Tredence Inc

An ML based solution that can help firms deal with significant demand variability across channel & geographies

Objective: Develop a dynamic sourcing mechanism which provide best sourcing options for customer orders based on inventory availability, labor and shipping capacity constraints and consider trade-offs on Shipping costs, Penalties, Service Level Impacts and Lost sales

Key Challenges Addressed:

  1. Increased demand variability resulting in stockouts
  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 resulting in challenges in fulfilling from DCs with stock availability

How do we address your challenges: The solution will fulfill customer orders using the following modules/ functionalities

  1. Optimization model which allocates customer orders to DCs based on set business rules and assigned constraints
  2. Provide trade-off options for planners to approve/reject system recommendations with reason codes
  3. Build Dashboards to track KPIs and $ impact
  4. Automate the system output to ERP systems

Pilot outcome: An automated tool which will recommend an optimal alternate location for order fulfillment in case of stockouts at default location so to maximize the order fill rates and minimize costs

Implementation Plan The break-up of the implementation plan is as below:

  1. Week 1- Week 3 : Discover as-is processes
  2. Week 4 - Week 5 : Data Understanding and Exploratory Data Analysis
  3. Week 6 - Week 9: Design, Build and Test pilot Optimizer
  4. Week 10 - Week 12: Measure value delivered and enhance Optimizer for most optimal solution

This implementation uses the following native Azure components:

  1. Azure Databricks
  2. Azure Datalake