SimOpti

Our platform is developing a collaborative decision-making solutions across multiple activities and processes at each level of the organization. Focusing on Autonomy, Continuous Improvement and Decarbonization Transition across value chain using dynamic simulation, based on a system approach.

Machine learning is good at rapidly revealing patterns within value chain data through the application of algorithms and constraint-based modeling.

The potential of AI-powered predictive analysis is harnessed across various domains, including planning, operations, asset management, material flow, transformation processes, customer demand, and disruption management.

While data-driven techniques hold merit, simulation offers an edge by enabling projections of unprecedented events and scenarios outside historical bounds, even in situations starved of operational data. This is especially pertinent for novel technology deployment, strategic shifts, or unforeseen events.

Stream’s SimOpti platform introduces cloud-based simulation and optimization for holistic views of industrial asset networks while maintaining the individual integrity of each agent within.

Stream’s agent-based simulation models facilitate the creation of individual agents for each pivotal asset within the study scope. This approach proves indispensable in modeling extensive mobile mining fleets. These agents, originating from data, function individually while also being influenced by their counterparts (fix or mobile).

Noteworthy input variables encompass equipment and asset performance, event attributes, mine layout, operational plans, and cost considerations.

Corresponding outputs encompass asset performance, streamlined systems, production analytics, energy profiles, financial projections, risk mitigation and optimized schedules.