Graduation Year

2008

Document Type

Thesis

Degree

M.S.E.M.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Kingsley A. Reeves, Jr., Ph.D.

Committee Member

Paulo Sergio Franco Barbosa, Ph.D.

Committee Member

Paul McCright, Ph.D.

Keywords

Supply Chain Management, Demand Forecasting, Electric Industry, Mathematical Model, Deregulatory Process

Abstract

Considering the international scenario, in a recent past, the electrical industry was based on the concepts of monopolistic concessions and vertical utilities structures. In Brazil, until recently, the electricity companies were all governmental properties that served restricted monopolized areas. In a similar manner, in the United States, monopolies for certain concession areas were assigned to vertically integrated electric utilities. This monopolistic portfolio brought to the industry, in a generic sense, a lack in the interface between companies and consumers. This fact established a low capacity of obtaining consumer's information and consequently, a low capacity of developing precise demand forecasts.

Lately, the industry of electrical energy around the world has passed through immense structural changes, not only in developed countries, but also in developing countries. In this new environment, competition and private capital are fundamental agents. Now, demand forecasting represents a key factor to support decision-making for planning strategies of electricity utilities. In addition to immediate potential benefits to commercial decisions, a framework for electricity demand forecasting can help to take actions aiming to develop more precise yearly budgets as well as to make accurate investments in the infrastructure expansion.

With the objective of improving the supply chain perception of the electricity industry, this work analyzes the electricity industry and develops a mathematical tool to accurately support the decision-making process of electricity utilities. CPFL Energy Co., a holding that controls companies and private enterprises in the generation area, electric power distribution, and trading in Brazil, was chosen as the case of study. CPFL Energy Co.'s supply chain was studied to find out the right explanatory variables and an electricity forecasting mathematical model was created through the stepwise regression procedure.

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