It leverages the latest advances in AI/ML time series models, handles large datasets efficiently, and performs rigorous model comparisons. This blend of advanced AI and statistical methods enables forecasting at scale, while providing an excellent balance of accuracy and interpretability.
Automated tuning streamlines the optimization process, fine-tuning parameters
without human intervention, ensuring the forecasting model is always aligned with
the latest patterns. This not only saves time but also enhances accuracy by swiftly
adapting to shifts in the business environment.
Supply chain priorities for Forecasting
- Understand sales and marketing sub-processes that feed into demand planning
- Understand the supply chain manufacturing and distribution strategies
- Understand product lifecycle management strategy
- Segment forecasting items using the above context and formulate strategy
Automation at scale
- Analyze master data and hierarchical relationships
- Analyze transactional data for hierarchical violations, holes, outliers, events, etc.
- Self-tuning, adaptive AI algorithms using a robust backtesting framework
- Distributed execution engine that divides and conquers large number of combinations across cloud servers
Smart workflow
- Focus human effort and deep computing resources based on business importance
- Manage by smart exceptions
- Guide planner to identify what is leading to lowering of forecast quality
Performance measurement
- Consistent, high quality measurement of accuracy, forecastability and forecast value add
- Simplified, rapid analysis of evolution of measures over time to detect trends
- Measurements at detailed as well as summary levels
User-Centric Software Application:
- Intuitive user interface, designed to effortlessly handle substantial datasets, striking a superb balance between accuracy and interpretability.
- Collaborative data ensures the integrity of master data and hierarchical relationships, identifying and addressing any hierarchical violations, gaps, outliers, events, and more within transactional data
- Comprehensive reporting capabilities offer a comprehensive analysis of accuracy, forecastability, and the forecast value, providing valuable insights for informed decision-making.
AI forecasting, propelled by adaptive learning, automated tuning, and attribute-
based learning, redefines the landscape of predictive analytics, particularly in the
realm of sales projections. The cornerstone of this approach lies in its adaptive
learning capabilities, enabling the system to evolve and refine its models based on
real-time data changes and market dynamics.
Automated tuning streamlines the optimization process, fine-tuning parameters
without human intervention, ensuring the forecasting model is always aligned with
the latest patterns. This not only saves time but also enhances accuracy by swiftly
adapting to shifts in the business environment.
Attribute-based learning is a game-changer in providing detailed sales predictions.
By considering a multitude of attributes such as customer behavior, market trends,
and product features, the system gains a nuanced understanding of the factors
influencing sales outcomes.
The swiftness, precision, and detail offered by AI forecasting are instrumental for
businesses seeking a competitive edge. Whether navigating market fluctuations or planning inventory, this advanced forecasting leverages sophisticated learning
mechanisms to deliver insights that drive strategic decision-making, contributing to a
more agile and responsive business ecosystem.