The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend.
This internship is part of CRIAQ's structuring project "CRIAQ-1645 Digital Aerospace", whose goal is to identify converging trends between aerospace and information and communication technologies (ICT). This project will help in program orientation, for inferring R&D needs in the short, medium and long-term, so to develop digital capabilities that will be more and more important for aerospace companies.
Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach.
This project's overarching goal is to spur cooperative enterprises to move into the collaborative economy. Even though the "collaborative economy" shares many values with cooperative enterprises, too few of them have entered the pace in Quebec and Canada, but also globally. This paradox puzzles Quebec's cooperation and mutuality council (CQCM). They want to reverse this trend and help old and new cooperative companies to offer services linked to the collaborative economy platforms. In order to do so, the CQCM want to know why there is such a paradox, and how to act upon it.
This field research project is a continuation of an on-going multi-year action-research program, undertaken in a large Manufacturer of Industrial products in the Energy Sector. Like many Canadian corporations, faced with pervasive globalization, economic uncertainty, fierce competition and strict legislations, this Family-owned Company aims at revitalizing its product lines, entering new specialized market niches and upgrading the technological level of its offering through the introduction of Internet of things (IoT).
The project is a partnership between Polytechnique Montréal, HEC Montréal, UQAM and JDA Canada. JDA Labs is investigating new approaches to help incorporate "big data" science and analytics into everyday supply chain decisions. It relies on new approaches that employ sensor technologies, new analytic capabilities and simulation techniques to not just sense and respond, but anticipate and act by making complex decisions while decreasing the risk exposure.
Energy companies are in the business of turning energy from one form into another. For example, a gas-fired power station turns chemical potential energy stored in the natural gas into electrical energy. A natural gas storage facility allows energy (held in the form of natural gas) to be stored at one point in time and recovered at a later time. A gas pipeline moves energy from one location to another. The result is that the financial risks faced by an energy company involve a large portfolio of spreads â differences between energy prices.
This project concentrates on the scenario optimization method which does not need to make any assumption for the underlying asset distribution and directly incorporate such uncertainty into the objective or constraint functions through stochastic programming. The scenario optimization is performed under different parameters and constraints while Markowitz and Black-Litterman model are taken as the benchmarks to evaluate if the scenario optimization can outperform the traditional methods with the same input exchange-traded funds (ETF) data.
The QWeMA division of CANNEX develops solutions for the financial and insurance industry of North America. Our analytics play an important role in determining the value proposition of investment products. Our solutions help the financial community and public through their financial advisors to be able to make informed decisions.
We work at the intersection of finance, mathematics, actuarial science, and computer science. Our solution strategies require us to solve complex mathematical and optimization problems in a finite amount of time.