Daniel Frances, University of Toronto Rate Professors

Social Profile:

University of Toronto St. George Campus - Industrial Engineering

University of Toronto
Professor, Teaching Stream
Daniel worked at University of Toronto as a Professor, Teaching Stream
University of Toronto
Senior Lecturer
Daniel worked at University of Toronto as a Senior Lecturer
University of Toronto
Associate Professor, Teaching Stream
Daniel worked at University of Toronto as a Associate Professor, Teaching Stream
Hydro One Networks
Manager Information Assets
Responsible for management of IT investments, including the power system data center for provision of complete and up-to-date information on all power system components and their inter-connectivity. Accountable for NT and UNIX servers (e.g. database, applications, Web), development and maintenance of applications (using VB, C, MS Access), interfaces with other enterprise databases (e.g. SAP, People Soft, Passport) and database management (e.g. Oracle).
Ontario Hydro
Section Head, Power Systems Operations Division
Responsible for identifying opportunities for reducing the overall electricity production costs for the province of Ontario and specifying, developing, implementing and maintaining IT applications to facilitate such savings.

Models for Predicting Critical Blood Product Shortages
in Modelling Efficiency & Quality in Health Care: Proceedings of 29th meeting of the EURO Working Group on Operational Research Applied to Health Services
The paper describes a study to develop prototype simulation models to predict the likelihood, and assess the subsequent medical consequences of, blood product shortages. The models account for uncontrollable factors such as random donation and patient demand, as well as policies controlling blood product expiries and hospital restocking. The complexities in blood type matching and issuing policies which depend on inventory levels were captured where appropriate. The study was conducted in three settings: urban, semi urban and rural. The model quantifies the hourly inventory patterns of blood centers and specific hospitals, as well as predicting the risks of shortages and subsequent medical consequences of these shortages. The model was validated against inventory levels and outdates and was considered to be sufficiently accurate by the blood bank and hospital staff for use of the model as a decision support tool.