The Portuguese Project Management Association (APOGEP) launched in September 2020 the project of implementation of the Portuguese Project Management Observatory (PPMO), a non-profit organization with a focus on the status and evolution of project management in Portugal. The project is being managed using the PM² methodology, developed by the European Commission (EC) that has been available, since 2016, free of charge to the general public. The methodology has few scientific references, so this project was used to contribute to filling this gap. The methodology incorporates best practices from other bodies of knowledge, can be easily applied and allows for customization. The initiating phase has been completed and the details of its implementation are focused on in this paper. The client involvement from an early stage was crucial and it was necessary to use more artefacts than those mentioned for this phase. Therefore, this paper presents the tailoring of the PM² methodology to the PPMO project, for the initiating phase.
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1. Abstract Given a project network under stochastic conditions, the goal is to determine the optimal resource allocation to the activities in order to minimize the total project cost. This cost includes the resource cost and the tardiness cost. In this work we consider the multiple resources case, which is an extension of the models previously developed by the first author and other researchers, considering a single resource. We assume that all the resources are independent and abundant. The work consists mainly of two parts: formalization of the new models, and their implementation in Java. In order to formalize the models, it was necessary to establish an allocation strategy for the multiple resources. This is required to ensure the desired equality of expected durations yielded by each resource in the same activity. We study four different allocation strategies: two of them are derived from the stochastic nature of the work content by equalizing the expected durations, thus determining the allocation vectors; and the other two go down to the level of all possible values to devise an allocation method (among all the allocation vectors, selects those leading to equal expected durations). Then the probability distributions of the variables required for analysis and evaluation were determined. Although the research has covered four strategies, one proved to be inferior compared to the others, and another was too complex to be easily implemented. The remaining two are strong rivals with neither dominating the other, and one of them was arbitrarily chosen for implementation. The implementation covers three algorithms: Dynamic Programming Algorithm, Electromagnetic Algorithm and Evolutionary Algorithm. Concurrent programming was exploited to enhance performance. We report on the performance of our application over a representative set of project networks.
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The optimal resource allocation in stochastic activity networks had been previously developed by applying three different approaches: Dynamic Programming (DP), an Electromagnetism Algorithm (EMA) and an Evolutionary Algorithm (EVA). This paper presents an extension to the initial problem considering the value of money over time. This extended problem was implemented using the Java programming language, an Object Oriented Language, following the approaches previously used (DP, EMA and EVA).
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We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and supportive resource or S-resource) to activities in a project. We developed a mathematical model capable of determining the ideal mixture of resources allocated to the activities of a project, such that the project is completed with minimal cost. This problem has a circularity issue that greatly increases its complexity. We have developed a procedure which we illustrate by application to small instances of the problem, using complete enumeration over the decision space. The development of a more computationally efficient procedure awaits the second phase of this study.
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This paper aims to contribute to both theory and practice by describing current problem-solving practices, proposed by various authors, as part of the quality management process of companies in the automotive industry. The paper, based on proposals from different authors, also includes a set of quality tools that can be used in the different steps of the problem-solving process. The results show that there is general agreement on best problem-solving practices, emphasizing root-cause analysis and the development and implementation of corrective actions, which are included in all the proposals analyzed. The analysis also shows that many quality tools can be used in the different steps of this process. In addition, the paper proposes an initial framework developed by the authors that can be used by organizations to improve their problem-solving practices.
