Our life is driven by the constant pursuit of optimization. Even happiness in the form of long-term satisfaction and contentedness roots in optimization.
Optimization describes a perfectly balanced system in a moment in time, defined as the rationale of energy and information input into the system and its information and energy output.
A system can be any entity that is built from dynamic variables. In this sense humans are systems just like machines and even processes and any scaled combination thereof. The lower the maintenance and the more stable the system the less energy it consumes given the same output. An entity that produces the same output using less energy and information obviously is a more productive system.
A very important aspect are disturbances in the form of events that impact the systems in equilibrium. One key question in complex systems optimization therefore is: What is the inherent response to a disturbance or trend-change from outside the system or from within that can lead to a collapse. And, further, how much resilience needs to be built into a system to keep it stable and make it resilient and in its highest form to learn from its environment and adapt to it?
The main problem of complex business system optimization is constraints which can have different forms and which need to be addressed before any optimization is attempted.
Constraint variable modelling enables to design systems that absorb very rare and complex events much better than classic methods and which are much more resilient to events and auto-adaptive to their environment.
The Steering Lab has developed an extensive Optimization Analytics framework called OPTAAURUM. It combines classic Operations Research models and state-of-the-art BDML system modeling.
OPTAAURUM has proven vastly superior to classic operations research approaches and far less expensive.
This is where Big Data and Machine Learning make the difference compared to classic Operations Research. BDML Optimization can define very event-resilient systems. The more complex the system, the more BDML-based optimization approaches are required to design stable and energy-efficient systems. This concerns any transportation systems as well as complex information management systems.
Dynamic offering solutions are one very important field where BDML-based Optimization Analytics has proven superior. In combination with AI models we have been able to design highly effective pricing and promotion models for hundreds of products across multiple sales channels.
Predictive Systems Maintenance is the combination of two BDML domains: Predictive and Optimization analytics. The challenge is not whether we can foresee that a system will break down, but which part of the system and why. This is a quantum leap in predictive maintenance in itself.
Quantitative optimization business cases are clear cut and easy to validate. However, the models and algorithms behind are complex and resemble black boxes. More so, many of the models deliver counter-intuitive results from a human perspective, for example when it comes to work-force optimization. This is why these solutions often are perceived as magic or even spooky. An emotional perception that leaves great business value potential locked-up in inefficient organizations and machines.