Alloqis provides intelligent resource allocation software that empowers manufacturing planners to balance competing business targets like profit, stock, and CO2 emissions. Their algorithms optimize the flow of physical and financial resources to generate optimal production and sales plans. The solution integrates with existing planning systems to deliver measurable impact on profitability and sustainability.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturers face challenges in balancing competing business objectives such as sales targets, inventory levels, profit margins, and sustainability goals within complex operational constraints. Traditional planning methods often struggle to systematically optimize resource allocation across these diverse and sometimes conflicting priorities, leading to suboptimal outcomes and reduced profitability.
Solution
Alloqis provides an intelligent resource allocation platform that empowers manufacturing planners and decision-makers to achieve optimal business outcomes. The platform leverages multi-objective optimization algorithms to simultaneously balance competing targets like sales, stock, profit, and CO2 emissions. It offers advanced scenario generation and comparison capabilities, allowing users to model various demand, lead time, and production footprint scenarios. Furthermore, Alloqis employs intelligent constraint handling to ensure efficient utilization of scarce resources and AI/ML-based data validation to harmonize diverse data sources for robust decision-making. The solution is designed for containerized deployment within the client's secure environment, ensuring data sovereignty.
Target Audience
Primary customers are manufacturing companies, ranging from large corporations to mid-sized enterprises, seeking to enhance their planning and decision-making processes for improved profitability, sustainability, and resilience.
Features
- Multi-objective optimization engine for balancing competing targets (e.g., sales, stock, profit, CO2 emissions).
- Advanced scenario modeling for demand, lead times, and production footprint variations.
- Intelligent constraint handling for optimizing resource utilization against targets like stock levels and CO2 regulations.
- AI and ML-based data validation, including plausibility checks, outlier detection, and missing value prediction.
- Containerized deployment option for on-premises or private cloud execution, ensuring data security.
- Modular architecture allowing for incremental data integration and scope expansion.
- Augments existing planning systems with an intelligent optimization layer.
- Configurable boundary conditions and input adjustments to maintain planner control.