Project examples

Power Grid of Luxembourg City

Imagine your goal is to forecast the impact of electric vehicle (EV) adoption on Luxembourg City’s power grid by the year 2040. A potential solution is a model that computes the increased electricity demand in year t+1 based on the demand in year t, using available data from years 2019 to 2023 to initiate and calibrate the model. Ideally, the model includes parameters to assess how infrastructure improvements, such as the addition of charging stations, and policy decisions, like incentives for EV purchases, influence the predicted load on the power grid by 2040. This analysis can assist in ensuring the reliability of the power supply in light of the growing popularity of electric vehicles, and predicting the effects of policy decisions.

Supply Chain Vulnerabilities

Facing the challenge of optimizing stock levels amidst unpredictable supply chain disruptions, a manufacturing firm seeks to delve into historical incidents of production halts – ranging from local natural disasters like flooding to global health crises like pandemics. The firm is set to examine past disruption patterns to estimate the timing and duration of future interruptions with a 95% confidence level. This effort includes a thorough assessment of all costs associated with such disruptions. By comparing these costs against the expenses of maintaining increased inventory, the firm aims to pinpoint the most economically viable stock levels. The objective of this strategy is to strike a balance between minimizing risk and managing costs to create a resilient supply chain.

Urban Air Quality

In an urban setting, a municipal agency has deployed a network of 100 air quality monitoring stations to track concentrations of five key air pollutants: carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM10). The mission is to identify, for each pollutant, the six most significant contributors from urban pollution sources, which include vehicular emissions, industrial emissions known for their pollutant output, residential heating systems, and localized sources such as construction dust and traffic congestion areas. The analysis will scrutinize 20 parameters, focusing on measures of factors directly impacting urban air quality like vehicular traffic density, emissions from specific types of industrial activities, meteorological conditions affecting pollutant dispersion, and the role of urban green spaces in air quality improvement. The goal is to methodically rank these variables for each pollutant according to their significance, providing a clear priority list for targeted pollution reduction strategies.

Optimal Drug Formulation

In the critical phase of developing a new drug, a company must optimally allocate resources between two promising drug formulations. The goal is to optimize efficacy and minimize side effects. This decision’s complexity is heightened by regulatory requirements, financial stakes, and the urgency to market a safe and effective drug swiftly. By employing probabilistic quality management to assess the ‘success probability’ of each drug formulation, the company can make informed decisions, strategically guiding their resource allocation.

Our training approach usually begins with an industry-relevant problem, providing intuitive insight into the required mathematical concepts. Next, we guide participants through the generation and explanation of code to solve the problem, employing the discussed technique. Finally, we interpret the solution, ensuring comprehensive understanding and practical application.

Here are two examples of training options, each offering three different focuses: