AI-supported Planning for Omni-channel E-commerce

Features

Omni-channel sales requires channel-specific analytics and predictions.

Let AI help to understand your assortment and performance to optimize demand planning.

Connections to all source systems like Shopify or AmazonSeller drives consistent data.

Use algorithms to generate sales scenarios for new channels or new products.

Run multiple supply scenario with different purchasing budgets to optimize economic outcomes.

Use different ready-to-use algorithms to optimize your reorder policy.

Set and compare multiple scenarios in real-time observing all effects when modifying parameters of demand and supply
Planning horizons can be adapted to the required scope
Define your parameter setting on category, SKU or channel level
DispodyneR automatically analyses the assortment and recognizes the SKU planning character such as obsolete
DispodyneR proposes a baseline utilizing advanced algorithms that can be manually re-selected
Scenarios can be developed easily, stored and retrieved for further modifications
All planning is conducted on the newest data extracted from e-shop and inventory holding systems

Instant illustration of historical sales per SKU or aggregated by category or channel
Algorithmic demand planning scenario to judge plausible forecasts
Planners can adjust forecast proposals resulting from 'backcasting' to inject more context
Forecast algorithm options are also illustrated to switch to other scenarios easily

Adjustments can be made as multiplier or using absolute volumes
Overrides are often conducted to adjust for single sales events such as Prime day or promotions
Adjustments can be conducted on SKU, category or channel level
The consequences can be visualized immediately to decide if extra volumes are covered by inventory

DispodyneR allows for different replenishment policies such as reorder point method or fixed cycle reordering
Based on the selected method, DispodyneR calculates optimized re-order point and volumes
Planers can see the immediate impact of changed demand scenarios on their inventory and delivery performance
Risk for stock-outs and overstock can be detected far in advanced to allow for counter-measures if necessary