Optimization and reasoning
Agents & Beliefs
We believe that supply chains are composed of something more than just departments or components. They are agents with specific, potentially conflicting goals and behavior patterns. Using our proprietary machine-reasoning technology, we can model agents and their underlying belief systems, making them work together and coordinate towards organizational goals.
In order to fulfill requirements of a modern supply chain network, our algorithms must consider an enormous combination of complex scenarios, bound by business requirements and resource availability over a time period. Classic silo-based optimization algorithms tackle products one at a time, overlooking optimizations at the global level. We tackle this difficult optimization problem with a proprietary space-time representation of network requirements.
Built on top of the distributed computing engine Apache Spark, our algorithm not only solves complex network problems, but does so in a scalable way. Your supply chain will never grow too big for our technology.
Machine learning meets reasoning
Leveraging our machine learning and reasoning algorithms, we generate digital models of your supply chain agents that coordinate to find strategies to improve the network’s performance.
As a result, Aleph agents are capable of:
Rescheduling orders in risk to leverage productions and ongoing inventory transports
Identifying opportunities to improve daily capacity usage
Changing shipping points to eliminate unnecessary inventory movements
Finding the most cost-effective way to maximize service
Relaxing constraints intelligently to solve order risks
Prepare for risks before they materialize
Take your planning to the next level by combining our optimization and demand sensing technologies. Aleph OSC senses the demand patterns and suggests the necessary steps to be prepared, before it’s too late.