Evaluation and characterization of advanced algorithms for applications in operational processes (manufacturing – warehouses – logistics) and demand forecasting.

Evaluation of the performance, quality, and accuracy of results offered by different advanced algorithms for specific applications in industrial operational processes using synthetic data generated by digital twins based on process data under study. The evaluation of performance, quality, and accuracy of results offered by different advanced algorithms for demand forecasting in manufacturing or distribution companies is also considered. As an alternative to digital twins or dedicated data sets, two options are considered: 1. the use of previously curated data from the customer’s operating processes can be used for evaluating specific AI based process optimization solutions. In these cases and initial evaluation of the data and process characteristics through advanced data processing and analysis (using ML techniques, among others) will be done. 2. the use of real time data sets obtained from the installation and set-up of sensor networks, communication technologies, and contactless identification systems for tracking materials/assets/people in industrial plants as pilot developments. In general the result will be a technical report describing the algorithm results based on predefined indicators to evaluate their performance. Target audience: Industrial/logistics companies – AI solutions developers (in the area of ERP or decision support tools)