Analysing & optimising processes
Wouldn´t it be great to have a software that can analyse large data volumes, simulate decisions and on that basis select the optimum variant? And in the process taking account not only of content clusters but also developments and trends as well as business processes?
ApolloSimulation makes is possible for the first time to optimise in one decision several parameters simultaneously. For example, xost, reliability and punctuality aspects of resource management decisions in the logistics field.
ApolloSimulation makes is possible for the first time to optimise in one decision several parameters simultaneously. For example, xost, reliability and punctuality aspects of resource management decisions in the logistics field.
Up to now, decision simulations of management decisions have only existed at universities. Truly effective decision optimising systems that also can take account of qualitative factors are not available on the market.
Nevertheless, this would provide an way of cutting costs and generating additional profits.
ApolloSimulation now offers solutions for exactly this field of application.
Nevertheless, this would provide an way of cutting costs and generating additional profits.
ApolloSimulation now offers solutions for exactly this field of application.
Basis for decision
No optimum decision without a comprehensive information basis – this is a basic principle, and is why an Apollo-based system first considers all avenues of acquiring information: internal data and of course web data. This automatic capture and continuous updating alone represents a substantial improvement.
Of what use are loads of current data, though, without the possibility of grouping them as well as recognising and analysing trends and structures?
After all, the aim in any decision is to optimise several parameters simultaneously. That is a mathematically complex task and until now has been an obstacle to developing demanding decision simulations. Apollo solves this multiple optimisation task not mathematically but with feedback loops – much like the human brain, except objectively.
Of what use are loads of current data, though, without the possibility of grouping them as well as recognising and analysing trends and structures?
After all, the aim in any decision is to optimise several parameters simultaneously. That is a mathematically complex task and until now has been an obstacle to developing demanding decision simulations. Apollo solves this multiple optimisation task not mathematically but with feedback loops – much like the human brain, except objectively.
Information evaluation
This system is suited for medium-sized and large companies as well as OEMs. Given the individualistic and complex nature of these tasks, the specific development takes between six and twelve months. Analysing and comparing existing business processes can be done much faster.
Apollo can train itself for documents of an existing business process – as for corporate compliance – and in this way compare the desired processes with those followed in practice. In this way qualitative deviations and unexpected differences can be detected.
The thing of particular interest here is which processes currently in place at the company depart significantly from the desired processes.
Apollo can train itself for documents of an existing business process – as for corporate compliance – and in this way compare the desired processes with those followed in practice. In this way qualitative deviations and unexpected differences can be detected.
The thing of particular interest here is which processes currently in place at the company depart significantly from the desired processes.
