Enterprise Resource Planning (ERP) encompasses all core processes required to run a business: Procurement, production planning, order processing, service, human resources and finance. Even simple, conventional ERP integrates all these processes in a single system. However, new ERP systems go far beyond this. With the help of the latest technologies, such as machine learning and artificial intelligence, they create additional transparency, efficiency and intelligence in all areas of the company.

Partner: SAP, SAP UCC
Software: SAP S/4HANA

Mathematical optimization deals with finding optimal expressions of decisions in a – usually complex – system. "Optimal" means that an objective function (or objective system) is minimized or maximized under given constraints. In this process, the decision problem is first formulated mathematically, mapped in a defined development environment, and then solved analytically using a mathematical optimization engine.

Partner: IBM, GAMS
Software: IBM ILOG CPLEX Optimization Studio, GAMS, Open Source Solutions

Discrete event simulation and statistical analysis can be used to optimize material handling, logistics, machine utilization, and labor requirements. It becomes possible to quickly identify bottlenecks, validate transported materials, and view resource utilization over time for multiple process alternatives. The use of stochastic tools with object-oriented and 3D modeling capabilities makes it possible to increase manufacturing accuracy and efficiency while improving throughput and overall system performance.

Partner: Siemens, Tecnomatix
Software: Plant Simulation

Life Cycle Assessment (LCA) is an approach to environmental management that considers and characterizes all aspects of resource use and substance release of a product, process or industrial system from cradle to grave. It is a holistic view of all environmental interactions that occur directly in the value chain or are indirectly induced in other value chains, from the extraction of raw materials from the earth, through the manufacture, use and reuse of the product, to its disposal.

Partner: ESU-services, PRé Sustainability
Software: SimaPro, openLCA

Machine Learning (ML) describes the "artificial" generation of knowledge from experience. In this process, large amounts of data are first analyzed using statistical methods. The findings are used to train special algorithms, such as artificial neural networks or random forests, to make predictions about specific issues. The application areas of ML are extremely versatile and range from decision support to complete automation. Possible application scenarios in the context of production management are, for example, "predictive maintenance" or the prediction of production parameters.

Software: TensorFlow, XGBoost, scikit-learn