Selecting an approach to project time and cost planning (original) (raw)

Project management under uncertainty: Solution methods revisited

2016

Project Management involves onetime endeavors that demand for getting it right the first time. On the other hand, project scheduling, being one of the most modeled project management process stages, still faces a wide gap from theory to practice. Demanding computational models and their consequent call for simplification, divert the implementation of such models in project management tools from the actual day to day project management process. Special focus is being made to the robustness of the generated project schedules facing the omnipresence of uncertainty. An "easy" way out is to add, more or less cleverly calculated, time buffers that always result in project duration increase and correspondingly, an increase in its cost. A better approach to deal with uncertainty seems to be to explore slack that might be present in a given project schedule especially when a non-optimal schedule is used. The combination of such approach to recent advances in modeling resource alloc...

A Framework to Predict Time and Cost Risks based on Project Factors

Civil Engineering Journal, 2018

The occurrence and severity of risks are directly associated with the project factors, and in the case of determining a logical and significant correlation between project factors and risks, the final conditions of the project based on the initial and definite project factors can be predicted. In this research, a final list of 112 risks was prepared using the views of experts. Then, the risks’ weight was determined based on their impact on time and cost and according to the repetition of each risk in the earlier studies, as well as the use of 630industry experts using online web-based forms. Afterwards, 21 project factors that could affect the probability of occurrence and severity of risks related to time and cost were identified, and these factors were prioritized for each risk using the views of 25 experts. Then, applying the Pareto Principle, 14 project factors were identified. In all 4cases, including calculating the weight of risks in the technical literature as well as the ex...

Project management under uncertainty: a study on solution methods

2015

Project Management involves onetime endeavors that demand for getting it right the first time. On the other hand, project scheduling, being one of the most modeled project management process stages, still faces a wide gap from theory to practice. Demanding computational models and their consequent call for simplification, divert the implementation of such models in project management tools from the actual day to day project management process. Special focus is being made to the robustness of the generated project schedules facing the omnipresence of uncertainty. An "easy" way out is to add, more or less cleverly calculated, time buffers that always result in project duration increase and correspondingly, in cost. A better approach to deal with uncertainty seems to be to explore slack that might be present in a given project schedule, a fortiori when a non-optimal schedule is used. The combination of such approach to recent advances in modeling resource allocation and sched...

A Multi-level Approach to Project Management under Uncertainty

Project management decisions are made at the strategic, the tactical and the operational level. These decision levels are unarguably interrelated. Uncertainty in project management is primarily dealt with at an operational level by detecting operational risks and deciding on how to respond to them such that the tactical objectives can be met. We stress some major issues regarding the topics of risk management and robust project scheduling and discuss their consequences on more tactical and strategic decisions.

Project costs planning in the conditions of uncertainty

Agricultural Economics (Zemědělská ekonomika), 2012

The fuzzy approach to the project network analysis of the project planning and control is commonly oriented on the fuzzy critical path setting and the project duration monitoring. In the article, this approach is improved by the addition of the project costs perspective to the standard time aspect. The relations for the fuzzy quantity of the total project costs and for its membership function are derived. The example demonstrates the application of the theoretical relations and shows how the enhanced fuzzy approach can be used when different project variants are to be compared. The example also reveals how the fuzzy approach with the project costs monitoring brings new information for the project planning and management and for the risk management.

Discussion with the paper 'Project costs planning in the conditions of uncertainty' by H. Štiková

Agricultural Economics (Zemědělská ekonomika), 2014

In the paper, there is analysed one particular approach to the modelling uncertainty in the project management through an original version of the fuzzy CPM (Critical Path Method). First there is shown the relevance of using the fuzzy CPM in agriculture and the related branches and present the basics of the methods used. Then, there are described the imperfections of the work which is discussed and the impacts of the previously-published approach when applied in project management practice are emphasised. In the original paper, the author uses only the discrete fuzzy numbers for activity time durations which could be considered inappropriate for the time scheduling in project management. Consecutively, the direct application of the extension principle on the comparison of continuous durations could lead to the situation when both numbers can be greater than the second one with possibility equal to one. Moreover, the simple transformation of durations to the costs by linear equations ...

Allocating time and resources in project management under uncertainty

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the, 2003

We define and develop a solution approach for planning, scheduling and managing project efforts where there is significant uncertainty in the duration, resource requirements and outcomes of individual tasks. Our approach yields a nonlinear optimization model for allocation of resources and available time to tasks. This formulation represents a significantly different view of project planning from the one implied by traditional project scheduling, and focuses attention on important resource allocation decisions faced by project managers. The model can be used to maximize any of several possible performance measures for the project as a whole. We include a small computational example that focuses on maximizing the probability of successful completion of a project whose tasks have uncertain outcomes. The resource allocation problem formulated here has importance and direct application to the management of a wide variety of project-structured efforts where there is significant uncertainty.

On the project risk baseline: Integrating aleatory uncertainty into project scheduling

Computers & Industrial Engineering, 2021

Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a well-known repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.

A classification and review of approaches and methods for modeling uncertainty in projects

International Journal of Production Economics, 2020

In this paper, we created a classification for major sources of uncertainty in projects and categorized the studies in project scheduling literature with respect to the uncertainty source(s) they address. In addition, we investigated the approaches and methods to manage uncertainty, and studied the literature regarding these methods. Project management predominantly models the randomness in duration of activities; however, studies modeling the uncertainty due to other sources are scarce. We focused on these sources of uncertainty and highlighted the promising areas of research. The results presented in this paper will help researchers to identify the research gaps in modeling project uncertainty.

Selection of the Desirable Project Roadmap Scheme, Using the Overall Project Risk (OPR) Concept

Risk Management Trends, 2011

Project scoring methods do not necessarily ensure the quality of PRS selection, because they do not explicitly take into account PRS level considerations, such as multiple resource constraints and other project interactions. Too often, financial measures are made based solely on criteria such as Net present Value (NPV) and Internal Rate of Return (IRR). Mathematical programming models often solve an integer linear programming to determine the optimal composition of the options subject to resource and other constraints. MCDM models (Keeney & Raiffa, 1999), on the other hand, consider the multi-criteria project values. For data which cannot be precisely assessed, fuzzy sets (Zadeh, 1965) can be used to denote them. The use of fuzzy set theory allows us to incorporate unquantifiable information, incomplete information, non-obtainable information, and partially ignorant facts into the decision model. The first four approaches offer the ability to rate PRSs with a quantitative monetarily unit. Henriksen & Traynor (1999) found that decisions made by managers and those made by a multi-criteria decision making model differ. These differences reflect that such techniques typically do week in simulation of the reality about the projects. It seems the risky world about the projects is usually neglected during the evaluation. In most of the real-world problems, projects are multidimensional in nature and have risky outcomes and decisions and must consider strategy and multidimensional measures (Meade & Presley, 2002). It is stressed that most significant risks will be subjected to quantitative risk analysis of their impact on project (Project Management Institute [PMI], 2008; United State Department of Energy [US DOE], 2005). Several quantitative models have been introduced to provide valuable predictions for decision-makers. The most common risk valuation technique is expert elicitation. Using this method, the magnitude of consequences may be determined, through the use of expert's opinions. This could be applied using techniques such as interviewing (PMI, 2008). Risks can be represented by probability distribution functions. According to Kahkonen (1999), probability distributions are not widely used, because they are perceived to unlink the assessment from everyday work of project managers. To avoid direct application of probability distributions, the point-estimates (Kahkonen, 1999) are developed such as the Program Evaluation and Review Technique (PERT). Also, Critical Chain Project Management (CCPM) uses the same statistical basis as PERT, but only uses two estimates for the task duration, which are the most likely and the low risk estimates. Many assessment approaches deal with cost and schedule separately in order to simplify the process. Despite this, approaches such as the proposed method by Molenaar (2005) consider both cost and schedule, although schedule modeling tends to be at the aggregate level. Another method to deal with uncertainty is contingency allowance that is an amount of money used to provide for uncertainties associated with a project. The most common method of allowing for uncertainty is to add a percentage figure to the most likely estimate of the final cost of the known works. The amount added is usually called a contingency (Thompson & Perry, 1994). The present paper introduces a technique to identify the PRS efficient frontier and choose the desirable scheme. According to the introduced model, in responding the question of "which PRS is the desirable option to execute the project?" the decision maker wishes to simultaneously satisfy two objectives, time and cost, with considering positive and negative risks. Most often, these multi-objectives will be in conflict, resulting in a more complicated decision making task. For this purpose, a new modeling approach is proposed to estimate the expected impacts of project risks quantitatively in terms of the project cost and the project time. This framework incorporates Directed A-cyclic Graph (DAG) into the Overall Project Risk (OPR) concept.