Project management triangle

Résumé

The project management triangle (called also the triple constraint, iron triangle and project triangle) is a model of the constraints of project management. While its origins are unclear, it has been used since at least the 1950s. It contends that:

The quality of work is constrained by the project's budget, deadlines and scope (features).

The project manager can trade between constraints.

Changes in one constraint necessitate changes in others to compensate or quality will suffer.

For example, a project can be completed faster by increasing budget or cutting scope. Similarly, increasing scope may require equivalent increases in budget and schedule. Cutting budget without adjusting schedule or scope will lead to lower quality. "Good, fast, cheap. Choose two." as stated in the Common Law of Business Balance (often expressed as "You get what you pay for.") which is attributed to John Ruskin but without any evidence and similar statements are often used to encapsulate the triangle'

Source officielle

https://en.wikipedia.org/wiki/Project_management_triangle

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Cours associés (2)

Ce cours a pour objectif de présenter l'approche générale du management de projet en intégrant la gestion du risque dans toutes les étapes du projet.

This course will cover all the aspects of product design and system engineering from learning relevant methods to the actual implementation in a hands-on practice of product development.

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Séances de cours associées (13)

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La modélisation des facteurs humains dans la gestion de projets

Roland Sauter

During the past years, project management has been rapidly spreading out into the various domains of human endeavor; project management is becoming the most promising form of management in a changing world. This development increased drastically the need for training programs for project managers, and the main challenge of those training programs is their behavioral aspect, the training of interpersonal skills. The aim of our work is to create a model of the human factors in project management, in order to build a training simulator which would integrate the technical expertise and the human factors. In order to establish a list of the relevant human factors in project management, we conducted a first survey among one hundred project managers. The findings of this first survey allowed us to work out a certain number of hypotheses, which were tested in a second survey on a larger sample of project managers (700). The construction of our model has been done with a precise goal: a training simulator – that's why we could simplify the problems in several ways, especially regarding the circular interactions of some variables in the model. The data gathered in the survey were used to determine the parameters of the model. The construction of such a model is the first specificity of our work. Then we developed a full-scale training simulator for project managers, and we implemented our model of the human factors in this simulator. The simulator is centered on the planing and execution phases of a project. A simple, easy-to-use interface allows the user to visualize the project status, to make adequate decisions and to see the effect of those decisions on the project and on the evolution of the human factors immediately. The integration of the technical tasks and the human factors into a simulator is the second specificity of our work. This simulator has been in use since the beginning of 1995, and 200 project managers have been trained since then. A survey was conducted among the trainees: it showed the high efficiency of this type of training in a virtual world, the very high motivation of the trainees and a high learning effect on the behavioral level.

EPFL1996

Chargement

The project Management (case study Place de la Gare de Lausanne)

Abdelilah Zertiti

1997

Chargement

The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector.

IDIAP1997

Chargement

La modélisation des facteurs humains dans la gestion de projets

Roland Sauter

EPFL1996