The focus of this research is to explore new ways of applying analytics, to work on real data sets that are tied to organizational improvement initiatives, or to examine the structuring of analytics processes that can be applied specifically for the private sector.
Partner organizations are connected to professors and students. Depending on the initiative underway, they could be from the Telfer School of Management, the School of Electrical Engineering and Computer Science, or other facilities as required.
Developing Robust Measures for Quantitative Risk Management
Dr. Jonathan Li and Mohammad Saeid Rahmani, M.Sc. in Management
The current risk measures used in industries have been found to be overly simplified, and they underestimate the actual risks involved in many businesses. The goal of this research project is to develop risk measures that account for risk factors that have previously been overlooked and to provide quantitative estimates for them.
Optimization and simulation will be two of the main methods used in developing the new risk factors. In particular, risk factors will be modelled based on the principle of robust optimization (RO). Detailed simulations will be conducted to examine the improvement of the new risk measures. In addition, financial theory such as real option analysis will be incorporated to quantify the risk factors monetarily.
Accounting properly for risk factors involved in business is essential to the improvement of business performance. This project touches on the most essential component of risk analytics, namely, the measurement of risks.
This project will shed light on how modern quantitative approaches can be exploited to revamp how we measure and manage risk. These approaches are well-suited to efficiently processing the massive amount of data that is available today and thus can be helpful in providing a more holistic view of the risks involved.
Next-Generation Product Cost Tracking, Analysis and Forecasting Solution – Mitacs Converge
In order to ensure the cost effectiveness of next-generation products, companies want to drastically improve their ability to track, analyze and forecast the cost of their products. Building on experience with the current tool suite and business practices, the aim is to develop a next-generation product cost analytics solution that will run on existing IT infrastructure and allow the capture, analysis and modelling of historical as well as simulated material and transformation cost data.
The project therefore aims to find solutions to various challenges by researching approaches and techniques used in other areas and determining which ones can be most effective. The project will also explore ways of adapting those techniques to the supply chain situation, validating them with historical data and then defining the technology to best employ them within the company-specific operational framework.