Intelligent Optimization and Automated Decision Support
Transform data insights into actionable recommendations with advanced optimization algorithms, simulation techniques, and AI-supported decision systems
In today's complex business world, it's no longer enough to predict trends – companies must also know how to respond optimally. Prescriptive Analytics goes beyond Predictive Analytics by not only making predictions but also suggesting concrete action options and simulating their impacts. With advanced optimization algorithms, simulation techniques, and AI-supported decision systems, we automate and optimize your decision processes.
Our Prescriptive Analytics services encompass the entire process from analyzing your decision processes through developing customized optimization models to integration into your existing systems and continuous improvement. We combine advanced optimization algorithms, simulation techniques, and AI-based decision systems to deliver concrete action recommendations or fully automate decisions.
4 service modules
Bookable individually or as an end-to-end programme.
Development of tailored optimization models to increase efficiency, reduce costs, and improve the quality of your business processes and operational workflows.
Development of intelligent systems that recommend optimal courses of action to decision-makers or partially and fully automate decision-making processes.
Development and implementation of simulation and what-if models that analyze and compare the impact of various decisions and external factors.
Development of self-learning optimization systems that continuously adapt to changing conditions, with performance steadily improved through machine learning.
5 phases
We follow a structured yet agile approach in developing and implementing Prescriptive Analytics solutions. Our methodology ensures that your optimization models are not only mathematically correct but also deliver measurable business value and are successfully integrated into your processes.
Your contact
Asan Stefanski
Head of Digital Transformation
11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI
Prescriptive Analytics represents the highest form of data analysis by combining predictions with action recommendations. However, the true value lies not in mathematical complexity, but in the ability to integrate optimal decisions into real business processes. The connection of advanced analytics with deep business understanding is the key to sustainable success.
The success of Prescriptive Analytics initiatives depends significantly on the right balance between automation and human expertise. Start by automating well-defined, repetitive decision processes while initially supporting more complex scenarios with recommendation systems. Companies that follow this staged approach achieve on average 40% higher acceptance rates and faster ROI realization.
7 QUESTIONS, BRIEFLY ANSWERED
Prescriptive analytics is the most advanced stage of data analysis. While descriptive analytics summarizes past data and predictive analytics forecasts future events, prescriptive analytics goes further by recommending specific actions and answering the question "What should we do?". It combines mathematical optimization, simulation and machine learning to identify the best decision given all constraints and business objectives.
Successful prescriptive analytics requires three foundations: first, a sufficient data base with high quality and integrated data sources; second, analytical maturity with functioning predictive models as a baseline; and third, clearly defined business goals and optimization criteria. On the technical side, adequate computing resources and the ability to integrate into operational systems are essential.
The most common applications include supply chain optimization (15–30% inventory reduction), route planning in logistics (8–15% cost savings), dynamic pricing in retail (2–7% margin increase), portfolio optimization in financial services, and workforce planning. Adoption is also growing in healthcare and energy.
Prescriptive analytics relies on mathematical optimization (linear and integer programming), simulation techniques (Monte Carlo, discrete-event simulation), reinforcement learning and heuristic methods such as genetic algorithms. The choice of method depends on problem complexity, data volume and required response times.
ROI is measured through before-and-after comparisons of key metrics such as inventory levels, transport costs, utilization rates or conversion rates. A/B testing between the optimized and the traditional process provides reliable evidence. Typical improvements include 10–25% revenue uplift in marketing and 15–30% reduction in supply chain costs.
Integration follows a phased approach: first in shadow mode running alongside the existing process, then as decision support with human validation, and finally as semi-automated or fully automated decisions. Technically, the connection is made via APIs, embedded analytics in business applications or event-driven architectures.
A proof of concept typically takes 4–8 weeks, while a production-ready solution requires 3–6 months. Costs depend on data quality, problem complexity and integration effort. Starting with a focused use case and a clear business objective minimizes risk and delivers measurable value quickly.
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