Forecasting and estimation techniques

Abstract

It is very common in any project or program that deviations occur on different scales between what is expected and reality. On many occasions, these deviations could have been reduced by statistical analysis of past examples and their respective forecasts. Throughout this article, we will focus on studying the main statistical methods used when making forecasts and how, by using them, organizations could optimize their supply chain or the management of their projects by forecasting the quantity of units or resources that the market or project in question will demand, based on historical data from previous seasons. As an output, the advanced management of the project will be optimized, resources will be assigned to the tasks or processes that really require them, unnecessary costs will be eliminated, and the time used to complete the project will be reduced.

The term "forecast" can be associated with different fields, such as business, engineering, politics, project management ... In this article we will approach the term forecast from an operations management perspective, defining the term as an accurate prediction of the future, based on past events and whose objective is to give the company valuable time to face future events.

From the beginning of the 20th century to the present day, the importance of forecasts has grown exponentially, since it allows to anticipate events, coordinate the team and thus better plan resources, raw materials, personnel, work shifts, etc. when carrying out a project.

Forecasting approaches

The forecasting process is summarized in taking historical data and launching future forecasts. It can be projected into the future in two ways: through qualitative methods, in which we rely on past actions or on the implicit knowledge of the subject to intuit future actions. Or through quantitative methods, in which through the use of statistics or mathematical models, historical data are projected into the future.

Qualitative methods

Quantitative methods