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Optimize your inventory and boost e-commerce profits

BOOK DEMO

The hidden cost of ignoring forecasting and inventory management

Decision makers are screaming for the right tools to optimize their supply chain planning

67%

Organizations that lack a formalized forecasting approach

Forthcast addresses this gap with Al-driven demand planning

80%

Sales organizations that do not have a sales accuracy >75%

50%

Increase in lead times of goods shipped from China to US since 2019, disrupting timely stock availability

Decrease of sales and a 3-5% increase in costs due to delays and supply chain disruptions

Why waiting is not an option. The imperative need for forecasting and inventory management.

Inventory

Having surplus inventory eats up cash which is stuck and can not be spend to further company growth

Increases risk of obsolescence

Decreased agility to act elsewhere

Additional inventory holding cost at the warehouse

Stockouts

Having surplus inventory eats up cash which is stuck and can not be spend to further company growth

Affects customer satisfaction

May affects COGS due to expedited shipping or alternative sourcing

Missed opportunity to capture more market share

71% of customers will switch brand or retailer when faced with a stockout (McKinsey)

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Discover Forthcast in action

Watch our demo to explore how Forthcast simplifies inventory tracking, optimizes stock levels, and boosts your business efficiency.

Revolutionize your forecast with Forthcast

3 key aspects

DATA

The ability to capture leadtime per item

Supported anomaly detection for outliers

Ability to break down bundles to single SKU forecast

New product forecast substitutions to mature products

A forecast should capture unconstrained demand, not constrained sales

Set your desired service level

METRICS

A proper forecast will have a self-assessing feature, i.e. forecast accuracy. It will measure:

Bias: direction of error

Accuracy: magnitude of error

This way you can assess the efficacy of the model and adjust accordingly

BASELINE MODEL

We utilize a blend of statistical analysis and machine learning to optimize predictions:

Statistical methods ensure a solid foundation by recognizing patterns in historical data.

Machine learning adds adaptability, learning from complex trends and adjusting predictions based on your shop's unique data.

This strategy dynamically chooses the most effective method tailored to your shop's specifics, maximizing forecast accuracy by combining the strengths of both approaches.

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About Us

With a Master’s degree in Supply Chain Management, I’ve driven inventory excellence at Shell Recharge Solutions, Verisure, and Stryker. I’ve consistently outperformed forecasting benchmarks, grew high-performing teams, and transformed complex supply chain challenges into strategic opportunities.

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