Inventory Allocation Optimization Starter Kit

Features

OPTIMIZATION

Allocate inventory to meet demand

Get prescriptive guidance on how your inventory should be allocated across warehouses, distribution centers, and stores to meet forecasted demand and account for uncertainties.

SCALABILITY

Manage multiple warehouses, carriers, and products

Designed for multi-region, multi-market, and multi-carrier environments with complex capacity constraint and shipping cost structures, you can manage inventory across your entire supply chain ecosystem.

Managing multiple warehouses, carriers, and products

COST REDUCTION

Prevent order splits

Prevent order splits and minimize shipping costs by optimizing inventory allocation according to machine-learned insights about item sets that are frequently purchased together.

ANALYSIS

Get deep insight into optimization results

Leverage comprehensive visualization and what-if analysis capabilities to interpret optimization results and evaluate alternative scenarios.

Dataiku сustomized inventory optimization solution

CUSTOMIZATIONS

Extend and customize

Jointly developed by Grid Dynamics and Dataiku, the starter kit leverages the powerful AI and ML capabilities of the Dataiku platform. The solution is implemented as a workflow where each step can be customized or extended to accommodate your business requirements.

Inventory Allocation Optimization Demo

Demo

Grid Dynamics and Dataiku teamed up to create the Inventory Allocation Optimization Starter Kit, which will help you minimize shipping costs, order splits, and stock-outs by optimally allocating inventory across multiple warehouses, distribution centers, or stores. Watch this video demo and learn how to integrate this powerful solution into your business.

Clients

RETAIL

HI-TECH

MANUFACTURING & CPG

FINANCE & INSURANCE

HEALTHCARE

How the Inventory Allocation Optimization Starter Kit works

Inputs

The solution can incorporate procurement costs, storage capacities, network topology, shipping costs, and expected demand levels as inputs for optimization.

Optimization

The optimization process is designed to handle multiple locations, shipping options, products, and constraints. The optimization procedure is completely automated and can be scheduled for regular execution.

Outputs

The workflow outputs the recommended inventory allocation levels for each facility. Each recommendation can be readily operationalized and includes comprehensive diagnostic information.

Industries

Retailers

Inventory allocation is a common problem in retail, especially in the apparel and fashion segments. We help retailers minimize shipping costs for both digital and brick-and-mortar channels.

Brands

Inaccurate inventory allocation can be a major problem for international brands because of the high costs of rebalancing and long-range shippings. Our starter kit helps to minimize these costs and improve SLAs.

Manufacturers

The starter kit helps manufacturers to implement and optimize direct-to-consumer (D2C) capabilities efficiently, and stay ahead of the competition.

Logistics

The starter kit can be used as a component by 3rd party logistics (3PL) companies to reduce storage and shipping costs, improve SLAs, and leverage the economy of scale.

Our latest innovations in inventory optimization

Insights

Inventory Allocation Optimization: A Starter Kit for Dataiku

In this article, we describe an inventory allocation optimization solution developed by Grid Dynamics in collaboration with Dataiku. The starter kit provides advanced features such as capacity constraints and order splits minimization out of the box, and can be extended in a number of ways.

Insights

How explainable AI helped reduce warehouse order picking time by 1/4

There are two components of the order picking process that need to be optimized in order to improve efficiency: order picking time and order sorting time. In this article we will describe the business case of how we managed to reduce average order picking time (OPT) in a warehouse by 23%.

Insights

Ship from store and save

Traditionally, OMSs recommend optimal routes for each order independently, often with suboptimal results. In this article we present an alternative approach that resulted in 50% less order splits, double-digit EBIT improvement, and multi-million annual savings for one of the largest department store chains in the US.

Case study

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