Forthcast Demand Forecasting

Name: Forthcast Demand Forecasting Introduction Video
Uploaded: 2025-04-01T14:42:05Z
Duration: 1 min 33 s
Description: "Streamline inventory with AI-driven forecasts and proactive reorder alerts."

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"Streamline inventory with AI-driven forecasts and proactive reorder alerts."

Anomaly detection

Sales analysis

Ai-driven forecasts

Proactive reorder alerts

Sku-level projections

Custom forecasts

About Forthcast Demand Forecasting

Launched Mar 03, 2025

Built by Shopify

Address

Rancaekek 354, Bandung, JB, 40349, ID

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Introduction Video

Description

AI predicts demand 6 months ahead. Get smart reorder alerts before stockouts cost you sales.

Forthcast uses AI to forecast demand 6 months ahead at the SKU level, so you order the right stock at the right time. Get smart reorder alerts before you run out. Track lost sales from past stockouts to see revenue impact. Detect demand anomalies like spikes or gaps automatically. Include bundle tracking, forecast accuracy reports, and customizable safety stock. Replace spreadsheets with real-time insights that sync with Shopify. Built for DTC brands wanting fewer stockouts and better cash flow.

AI‐powered demand forecasting per SKU (6-month horizon) with seasonal trends
Smart reorder alerts: get notified before stockouts happen, not after
Lost sales tracking: see exactly how much revenue stockouts cost you
Anomaly detection: auto-detect demand spikes and stockout periods
Bundle support: track kits & multi-packs without double-counting stock

Pros

Advanced forecasting using AI models provides precise inventory predictions.
Proactive reorder alerts help prevent stockouts.
Allows customization of service levels to manage risk and plan effectively.
Includes 6-month SKU-level stock projections for better planning.
Enables anomaly detection for improved inventory management.
Facilitates the exchange of new products with mature ones to improve forecast accuracy.
Offers the ability to manage and forecast bundles.
Incorporates accuracy insights and tracks errors to refine forecast reliability.

Cons

May require a learning curve to effectively utilize advanced features.
Effectiveness depends on the quality of data inputted.
Potentially high dependency on machine learning models, which might not capture all real-world nuances.
Requires continual monitoring and adjustment to maintain forecast reliability.
Custom settings and advanced analytics might be complex for small business users with limited tech expertise.