Emerging simulation technologies with large object libraries have so significantly reduced the coding cost of replicating an enterprise in a simulated environment that it is becoming possible to "digitize the C-Suite decision framework". The historic enterprise software model is to develop a product and then customize and manage on top of the out-of-the-box software solution. We believe that it has become cost effective enough to replicate the business in software and then build applications to that framework as they evolve the platform over time through flight simulator-type/war gaming interaction with managers, systems integration, and a continual improvement process of measuring, collecting and digitizing various components and processes of their business. By utilizing a private equity due diligence methodology of dissecting a client company, tearing apart business processes and quickly replicating them in a java platform, we create a company "source code framework" that replicates the financial, operational and market environment in which the company operates. Human business processes, enterprise systems and external data sources can be pulled into this framework on a real-time and managed basis to give the most comprehensive real-time view as is possible to encapsulate a business into a framework. We have then developed a portfolio of learning algorithms that can search through the encapsulated and simulated business to provide extraordinarily smart applications that have heretofore been unavailable without a holistically and digitally mapped framework.
Manufacturing scheduling software is almost universally detested in the industry. We've been told 3rd hand that American Italian Pasta here in KC for instance spent $2 Mn on software and 9 month with a huge team of people working to implement a comprehensive scheduling platform into their operations and are extremely frustrated with the ultimate capability. The challenge for this type of software is that the software has to understand all of the product pathways through the manufacturing system, bottlenecking and financial implications of the ordinal nature of a product run to arrive at any kind of optimal schedule solution. By first replicating the system in simulation form (utilizing real-time, collected and human based data through "war gaming"), we can utilize our learning algorithms to sit on top of the simulated environment and iterate through a product schedule searching for the optimal gross margin ordering through the system. Because we have already mapped the process logic, operational and financial dynamics within the simulation engine, the algorithm doesn't have to contemplate the entirety of these calculations as the business has been abstracted to its engine.
To date, we have positioned our technology as a desktop application. With Google Fiber and hosted solutions of our applications "big data" and Screampoint type technologies could be utilized to create very interesting and real-time management platforms.