TECHNOLOGIES FOR DIGITAL DECISION MAKING

Operations Research (OR) applies advanced analytical methods and algorithms to help make better decisions - a uniquely powerful approach to decision making.

Using techniques such as mathematical modeling and optimization, OR leads you to the very best choices. The power of OR algorithms comes from their ability to quickly compare an incredibly large number of feasible options to find solutions that are often overlooked by mere human intuition.

By combining OR with machine learning, we can mine data to gain insights and make forecasts that fuel our OR optimization algorithms.

OR puts data to its most valuable use: Sophisticated algorithms leverage today's computing power to suggest optimized options for decision makers.

“All traditional logic habitually assumes that precise symbols are being employed. It is therefore not applicable to this terrestrial life but only to an imagined celestial existence.” [Bertrand Russell]

Building upon the scientific work of our company's founder H.J. Zimmermann, recipient of the prestigious IEEE Fuzzy Systems Pioneer Award, we apply Fuzzy Logic for solving the fundamental challenge of deriving correct decisions from imprecise or uncertain input.

In traditional bivalent logic, any statement is either true or false, without a possibility in between. Fuzzy logic, in contrast, allows intermediate values, making it suitable for representing linguistic information and expert knowledge. Decisions are taken in a manner similar to that used by humans.

Everybody regularly makes fuzzy decisions, for example when parking their car. Instead of measuring the parking space exactly, you make a rough guess on all sides as to whether the car fits in the gap.

Fuzzy Logic lets us transfer our human ability of drawing the right conclusions from imprecise information into software.

Data is the oil that powers the digital economy of the 21st century. Like crude oil, it must be refined to fuel the algorithm engines that can turn it into competitive advantage.

Machine learning is about refining data. It blends concepts and techniques from different fields - such as mathematics, statistics, and computer science - to look for structural patterns in data to find valuable connections and insights and make reliable predictions.

Machine learning is a multi-step process, from data engineering (harmonization, transformation, and enrichment) and feature selection over the training and evaluation of predictive models to the final execution and monitoring of models in challenging real time environments.

The focus always is on model accuracy as well as on comprehensibility, with a preference for models that are understandable and produce justifiable results.

Agile Optimization is a management strategy for dealing with complexity and uncertainty in the planning and control of business processes. By responding quickly to ad-hoc changes and disruptions in complex business environments, it helps to increase a company's efficiency, agility, and resilience.

More traditional management strategies, such as Lean Management, sophisticated planning, or ad-hoc improvisation, are ill suited to cope with the challenges of today's VUCA world: volatility, unpredictability, complexity, ambiguity, and ever increasing time pressure. By providing smart, rapid, and interactive computer assistance, Agile Optimization is the strategy of choice to elevate decision making for many dynamic business operations.