Every IBP module includes an intelligent core powered by Jumetelligence – machine learning, optimization engines, and generative AI.
This enables dramatic improvements in forecast accuracy, error reduction, and decision speed – from strategic to daily operations.
Machine and deep learning enhance the quality of critical business forecasts: demand, sales, orders, prices, traffic, breakdowns, etc.
Advanced algorithms solve complex optimization and decision making problems in planning and supply chain management.
Large language models power Jenius, your intelligent assistant for data, knowledge, and insights across Jume.
Jume’s machine learning and deep learning algorithms address dozens of forecasting tasks in planning, commercial and supply chain operations - faster and more accurately than traditional statistical methods or manual expert adjustments.
Forecasting demand, sales, orders
Traffic forecasting
Forecasting prices for raw materials and finished products
Forecasting resources and costs
Predicting employee attrition
Predicting incidents and equipment failures
The Jume ML Forecast Engine has been trained and tested on real data from more than 30 leading companies across diverse industries.
Automatic selection of forecast horizon and granularity
Automatic generation of optimal forecasts for each SKU without manual model selection
Automatic conversion between primary, secondary, and tertiary sales and back
Automatic data cleansing and outlier removal
Automatic built-in seasonality detection and irregular sales forecasting
Forecast decomposition into drivers and cannibalization effects
Automatic guidance for planners on forecast quality and areas needing review
Probabilistic Forecasting – showing forecast ranges and the likelihood of each outcome
IBP modules in Jume powered by the ML engine consistently deliver strong improvements in forecast quality.
Traditional forecasting methods typically rely on a single input – historical sales – and heavily depend on expert adjustments.
In contrast, Jume’s ML and DL algorithms can incorporate any internal or external data available, enabling smarter relationships and more optimal forecasts.
The result: A more accurate forecast with much less manual intervention.
Macroeconomic indicators
Sales history
Weather forecast
Price changes
Distribution
Assortment changes
Promotion costs
Inventory level
Advanced mathematical optimization methods are deeply integrated into Jume’s modules, helping to solve complex planning, commercial operations, supply chain, and decision challenges efficiently.
Expert-led decision-making
Optimizer-based decision-making
Optimization of production resource loading schedules
Warehouse inventory optimization
Optimization of promotional plans and budgets
Optimizing employee schedules
In planning and supply chain decisions, results are often limited by expert capacity and cognitive bias - never fully optimal, and not scalable for scenario planning.
Jume’s optimizers explore billions of possible combinations to always deliver the best possible solution, aligned with defined constraints and goals. Scenario modeling happens in minutes - not days.
From isolated optimization tasks to continuous, cross-functional supply and value chain optimization - all on a single platform.
Jume’s optimization-powered modules provide the most substantial efficiency gains out of across all intelligent components in the platform.
Jenius, the platform’s Gen AI assistant, uses large language models to support users in analytics, data interaction, and knowledge management.
Provides smart suggestions to save time and enhance decision quality
Instantly retrieves information from the knowledge base and presents ready-to-use answers
Consolidates all system alerts in a single view for the user
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