Organizations that lack a formalized forecasting approach
Stock Sage addresses this gap with Al-driven demand planning
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
Watch our demo to explore how Stocksage simplifies inventory tracking, optimizes stock levels, and boosts your business efficiency.
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
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
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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