Predictive Analytics Apps
Analyze trends, forecast future demand, and make proactive decisions that drive supply chain efficiency and resilience. Strengthen your planning with data-driven insights and predictive accuracy.

Demand Planning Accuracy
Enhance planning precision with SKU-level demand forecasts that reflect seasonality, promotions, and external factors.
Get optimal SKU levels
Balance inventory levels by predicting demand fluctuations and avoiding costly overstock or stockouts, driving higher service levels and lower carrying costs.
Get optimal ordering and production plans
Use predictive insights to fine-tune production schedules, and capacity allocation, reducing waste and improving responsiveness.
What-if forecast modeling
Simulate multiple demand scenarios to assess risks and opportunities, allowing you to build agile, future-ready strategies for your supply chain.
Demand Planning Accuracy
Enhance planning precision with SKU-level demand forecasts that reflect seasonality, promotions, and external factors.
Get optimal SKU levels
Balance inventory levels by predicting demand fluctuations and avoiding costly overstock or stockouts, driving higher service levels and lower carrying costs.
Get optimal production and ordering plans
Use predictive insights to fine-tune production schedules and capacity allocation, reducing waste and improving responsiveness.
What-if forecast modeling
Simulate multiple demand scenarios to assess risks and opportunities, allowing you to build agile, future-ready strategies for your supply chain.
Testimonials
Our Customers
Predictive Analytics Solutions
OPTIMAL PROCUREMENT DECISIONS
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PREVENT OVERSTOCK & STOCKOUTS
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PREPARE FOR PEAK
SEASONS
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SMARTER FORECASTING
Precise Predictive Analytics
Forecast Demand Precisely
Use historical and external data to predict demand at SKU level and improve service planning.
Plan Inventory Strategically
Adjust stock levels and safety inventory to meet future demand without overstocking.
Simulate Scenarios Realistically
Test multiple “what-if” scenarios with promotions, holidays, or supply changes to make informed decisions.
Visualize Insights Clearly
Interactive charts and dashboards highlight trends, peaks, and areas requiring attention.
Enhance Decision-Making
Leverage predictive insights to make informed decisions across your supply chain.
Optimize Resource Allocation
Use forecasted demand to reduce costs & improve service levels, and allocate production, distribution and workforce more efficiently
Get To Know More Predictive Apps
The Predictive Analytics module includes a suite of specialized apps designed to help you anticipate demand, optimize inventory, and make smarter, data-driven supply chain decisions. Explore each app to see how they can provide actionable insights, improve planning accuracy, and drive efficiency across your operations.

Demand Forecasting
Forecast demand and adapt to shifting customer needs.

Forecasting ARIMA
Predict future values based on the historical data.




Scenarios run
Unique analysts
Unique use cases
Take the first step toward optimizing your supply chain.
FAQ
Can I purchase only the Predictive Analytics category?
No, Log-hub’s Supply Chain Apps are offered as a complete portfolio. The strength of our platform lies in its integrated ecosystem — giving you access not only to Predictive Analytics, but also to complementary apps for transport optimization, inventory planning, and more. This ensures you have all the tools needed to approach supply chain challenges holistically, without limitations.
My forecast output shows unexpected low or high demand. How do I address this?
Review the SKU Parameters and ensure that trend limits and seasonality settings are realistic. Additionally, check if external factors (e.g., promotion intensity) are accurately represented in both historical and future tables.
Why is it important to use the same naming convention for external factors across tables?
Consistency in naming ensures that the forecasting model correctly associates historical impacts with future events, leading to more accurate predictions.
What if my input data has missing periods?
The ARIMA model requires a complete series with no gaps. Ensure that all periods are filled consecutively by double-checking your data before execution.
How do I know which seasonality setting to choose?
Analyze your historical data for any recurring patterns. If your data is monthly and exhibits annual cycles, a seasonality of 12 is appropriate. For other periodicities, adjust accordingly.
Forecast Smarter Today

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