We translate your specific business setting into a mathematical model that can be used for advanced analytics applications with the objective of customized decision-support in the fields of procurement and SCM.
We identify, collect and analyze large amounts of relevant internal and external data (e.g. real-time weather data or macroeconomic indicators) that is valuable to optimize your decisions in the procurement and SCM context.
We develop algorithms that combine machine learning (supervised learning, unsupervised learning and reinforcement learning) with operations research to exploit Big Data for optimal decisions in procurement and SCM.
We analyze and benchmark the saving potential of various data-driven solution approaches for your specific challenges in procurement and SCM.
We design customized software solutions for advanced analytics and data-driven decision-support in procurement and SCM.
We offer individual training and support for our customized data-driven decision-support tools for decision makers in procurement and SCM.
Founded as a spin-off from Technical University of Munich (TUM), prelytico offers data-driven decision-support solutions for procurement and supply chain management. Processes and performance in both areas are significantly affected by external risk factors such as commodity price risk, exchange rate risk, freight rate risk, demand risk or supply risk. At prelytico, we combine methods from Operations Research (OR) with techniques from Machine Learning (ML) for optimal decision-making under uncertainty by exploiting large amounts of internal and external data such as real-time weather information or micro- and macroeconomic indicators. More specifically, our customized solutions support decision makers in the fields of commodity procurement, inventory control, supplier selection and supply network planning. Our prescriptive analytics tools are successfully in use in different industries.
Operations Research + Machine Learning
Minimizing purchase cost and risk
Maximizing customer service levels
Maximizing supply chain efficiency
Von Predictive zu Prescriptive Analytics: Big Data in der Rohstoffbeschaffung