Parallelized & In-memory Architecture for On-demand & Event Driven Planning & Scheduling Optimization

Optessa offers planning and scheduling optimization products for manufacturing companies, specifically the auto industry. These products are:

·      Optessa MLP: enterprise level planning

·      Optessa MLS: detailed scheduling and sequencing

·      Optessa RTS: real-time scheduling and sequencing in an automated environment

Optessa has been successful in developing business with global manufacturers. The functional and technical advantage that the company’s products have over competitors has been critical to obtaining these customers.

In the auto and consumer products manufacturing industries, there is increasing pressure to be flexible and reactive in terms of business processes. The ideal state would be to have complete flexibility in order fulfillment while maintaining business process stability.

Optessa sees new business opportunities in enabling and positioning Optessa MLP and Optessa MLS to meet this need. The new business challenges require on-demand planning and scheduling of business processes. With this aim, Optessa has embarked on a project to bring together Optessa’s optimization technology and a platform based on in-memory database technology and concurrent optimization to develop an on-demand and event driven planning and scheduling architecture for Optessa MLP and MLS.

There are two significant challenges to achieving near real-time planning and scheduling business processes based on Optessa MLP and MLS.

The first challenge is data marshaling: data base operations involving large data volumes are necessary to extract, map and transfer the data required by Optessa to generate optimized solutions. These are typically client-side processes. Up to 50% of the entire process time could be due to data marshaling.  In-memory databases offer the capability to significantly speed up data marshaling lead times. Optessa has released an in-memory data marshaling and parallelized version of Optessa products while preserving the generic platform independent nature of the products.

The second challenge is computational: Optessa algorithmic engines generally execute in a serial manner. Optessa is parallelizing the design of the computational engines to make use of the multi-threading capabilities offered by current multi-core CPUs.

By addressing these two challenges Optessa seeks to reduce solution generation times by 66% to 90%. Such speeding up will not only benefit existing customers but also enable new on-demand / real-time use cases that are presently intractable. Optessa will be able to offer a unique set of solutions, currently unmatched by competitors.