On the use of multivariate regression methods for longest path calculations from earned value management observations

Vanhoucke, Mario; Colin, Jeroen

doi:10.1016/j.omega.2015.07.013

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…An additional step involved analytical resampling, utilising a nonparametric statistical inference method on the PCC results. This strategic approach aimed to retain highly associated demand variables, ultimately yielding less than 70 random samples for subsequent Energy Value Management (EVM) analysis [27][28][29][30][31][32]. The foundational plan was established by defining daily electricity demand for each end-user, encapsulating timely demand objectives (kWh) and corresponding budgets ($).…”

Section: Methodsmentioning

confidence: 99%

Monitoring Energy-Loss-Driven-Cost by Using Earned Value Simulation in Complex Systems

Zaghwan,

Amer,

Efatmaneshnik

et al. 2024

J Energ Power Technol

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The economic impact of energy loss stemming from end-user electricity consumption is a significant concern, with historical trends revealing escalating costs. Effectively managing both peak and off-peak demands remains a formidable challenge due to the unpredictable nature of consumer behaviors, leading to energy wastage. This study delves into the nexus of demand uncertainty, financial repercussions, and potential strategies to mitigate energy losses in the evolving landscape of electricity consumption.that causes financial loss. This simulation measure of time series data serves the purpose of determining what possibly contributes to policy and regulatory reforms and its notion as an economic growth pathway in Australia. The objective of this study is to build a relationship between social factors and financial aspects and discuss the issue of energy loss that emerges from the lack of leverage between end-users, providers and suppliers of electricity. Recognising the financial burdens associated with energy loss as electricity demand continues to rise, the investigation aims to elucidate the complexities underlying the difficulties in controlling these losses. The distinctiveness of the electricity industry, characterised by its prototypical nature, introduces dynamics that contribute to energy losses, thereby impacting electricity prices. Employing quantitative analysis, this research employs the Earned Value Method (EVM) tool to scrutinise the influential role of consumer behavior in precipitating financial losses. The study provides a comprehensive examination of the interplay between electricity demand and the adverse effects of energy loss during peak and off-peak consumption periods. Utilising time series data through simulation measures, the research identifies key metrics influencing the formation of electricity costs and prices. The findings not only contribute to a deeper understanding of the energy loss parameter but also offer insights into potential policy and regulatory reforms. With a focus on Australia, the research aims to establish a relationship between social factors and financial considerations, emphasising the issue of energy loss arising from the lack of alignment between end-users, providers, and suppliers of electricity. The study concludes by proposing pathways for economic growth through strategic interventions and collaborative efforts within the electricity ecosystem.

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Section: Methodsmentioning

confidence: 99%

Monitoring Energy-Loss-Driven-Cost by Using Earned Value Simulation in Complex Systems

Zaghwan,

Amer,

Efatmaneshnik

et al. 2024

J Energ Power Technol

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“…Content may change prior to final publication. [34] Cumulative Flow Diagram with some metrics [35] EVM & CPM [36] EVM & Use Case Point [37] EVM & WBS [38] CBS & WBS [39] Control Chart & EVM [40] level or at the product level. To measure the progress with multiple releases, the author identified each release (using sprint) in the project backlog and estimated the performance of the project at the end of each sprint when actual sprint velocity and actual costs are known [27].…”

Section: A Project Performance Measurement Techniquesmentioning

confidence: 99%

Measuring the Impact of Scope Changes on Project Plan Using EVM

et al. 2020

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Earned Value Management (EVM) measures project performance against a baseline plan. It identifies deviations in budget and schedule, aids project managers in taking earlier corrective actions against cost and schedule overruns. Although the literature highlights the significance of scope by adopting it as a leading indicator to measure project success or failure. However, EVM does not include scope when evaluating the performance of any software project. While considering the importance of scope and its ever-changing nature, it is imperative to measure the effect of changes in scope on the project plan. To analyse such effects, this study aims to enhance the traditional EVM by incorporating scope into it. The main objectives of this paper are: i) to extract the effects of project scope changes, ii) to map extracted effects of project scope changes with Software Project Scope Rating Index (SPSRI) elements, and iii) to quantify the extracted effects and integrate them with EVM. An extensive literature review is conducted to achieve the first objective, which results in the seventeen unique effects; that were used to map with SPSRI elements. To forecast the variations in scope for a given project budget, Monte Carlo simulations were run on the top eight scope elements, whereas, the results were incorporated with EVM to identify the deviations between actual and projected values of scope's score and cost. Finally, the multivariate regression model was used to evaluate the influence of individual element on the overall estimated cost of the project. The correlation between the independent variables (SPSRI elements) and the dependent variable (overall cost) was calculated along with the valuation of each independent variable on the dependent variable. Moreover, the effects are statistically shown that independent variables have influenced the dependent variable. This technique could assist the project managers to forecast deviations in project scope earlier.

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“…• CPI: Based on parameters BCAC 0 , W C , P C , cost buffer, and formulas presented in Section 3 (EVM/ES), the cost performance index can be calculated as Equation (13). The index is used to update the remainder of the cost buffer: (13) • ECAC 0 : In the phases of project control, estimated cost at completion, regardless of the cost buffer, and only after the control of the project buffer usage is done.…”

Section: Estimate Cost At Completion (Hybrid Efficiency-risk Approach)mentioning

confidence: 99%

“…The index is used to update the remainder of the cost buffer: (13) • ECAC 0 : In the phases of project control, estimated cost at completion, regardless of the cost buffer, and only after the control of the project buffer usage is done. In other words, with the help of the cost buffer usage, the adjusted BCAC 0 is estimated, which is the same as ECAC 0 .…”

Section: Estimate Cost At Completion (Hybrid Efficiency-risk Approach)mentioning

confidence: 99%

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An Integrated Efficiency–Risk Approach in Sustainable Project Control

et al. 2017

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Abstract:The lack of integrated project control techniques covering both qualitative and quantitative indices is one of the most important reasons leading to unfinished projects under predetermined schedules and expected budgets. Two modern techniques proposed in project control-the critical chain method and buffer management (CCM/BM), and the earned value analysis or earned value management and earned schedule (EVM/ES)-both have advantages and disadvantages. Goldratt proposed the CCM/BM method in 1997 based on the theory of constraint (TOC), but this method was not successful despite some improvements in project control because of some executive reasons. The most noteworthy constraint of this method is the management of time and time risks (use of a time buffer) of the project more than the subject. Goldratt believed that time control could be the most critical issue in project control. In other words, the overall problems associated with each project can be solved as long as the buffer time is under control and there is no need to control the other items. EVM/ES is one of the important techniques used to calculate real project development; it has been used for the integrated management of sustainable projects in recent decades. Using this technique, project managers can predict the final status of the project in terms of the necessary time and cost to finish the project. However, this method is limited by the management of the project cost and the lack of interference in the project risks. In sum, the CCM/BM method focuses on time and its risks associated with the project, thus making it advantageous to other techniques. Conversely, EVM/ES focuses on the costs or schedule with non-probabilistic assumptions, giving some interesting results. Therefore, this study aims to represent an integrated framework that considers the advantages of both CCM/BM and EVM/ES, called the efficiency-risk approach, which is implemented to control sustainable projects efficiently. This hybrid form can simultaneously control all the parameters, including both quantitative and qualitative variables, time, cost, and risk in conjunction with the project. Schedule and cost buffers of the project are derived using new formulations that provide appropriate estimations on the duration and cost for completing the sustainable projects and the relevant risks. The proposed ideas are analyzed and described through an industrial case study in the Steel Company, Isfahan, Iran.

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On the use of multivariate regression methods for longest path calculations from earned value management observations

Cited by 14 publications

References 35 publications

Monitoring Energy-Loss-Driven-Cost by Using Earned Value Simulation in Complex Systems

Monitoring Energy-Loss-Driven-Cost by Using Earned Value Simulation in Complex Systems

Measuring the Impact of Scope Changes on Project Plan Using EVM

An Integrated Efficiency–Risk Approach in Sustainable Project Control

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