2,643 research outputs found

    Double Whammy - How ICT Projects are Fooled by Randomness and Screwed by Political Intent

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    The cost-benefit analysis formulates the holy trinity of objectives of project management - cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindness cause forecasts to fall short of actual costs. Firstly, optimism bias has been linked to effects of deception and delusion, which is caused by taking the inside-view and ignoring distributional information when making decisions. Secondly, we argued before that Black Swan Blindness makes decision-makers ignore outlying events even if decisions and judgements are based on the outside view. Using a sample of 1,471 ICT projects with a total value of USD 241 billion - we answer the question: Can we show the different effects of Normal Performance, Delusion, and Deception? We calculated the cumulative distribution function (CDF) of (actual-forecast)/forecast. Our results show that the CDF changes at two tipping points - the first one transforms an exponential function into a Gaussian bell curve. The second tipping point transforms the bell curve into a power law distribution with the power of 2. We argue that these results show that project performance up to the first tipping point is politically motivated and project performance above the second tipping point indicates that project managers and decision-makers are fooled by random outliers, because they are blind to thick tails. We then show that Black Swan ICT projects are a significant source of uncertainty to an organisation and that management needs to be aware of

    Does Infrastructure Investment Lead to Economic Growth or Economic Fragility? Evidence from China

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    The prevalent view in the economics literature is that a high level of infrastructure investment is a precursor to economic growth. China is especially held up as a model to emulate. Based on the largest dataset of its kind, this paper punctures the twin myths that, first, infrastructure creates economic value, and, second, China has a distinct advantage in its delivery. Far from being an engine of economic growth, the typical infrastructure investment fails to deliver a positive risk adjusted return. Moreover, China's track record in delivering infrastructure is no better than that of rich democracies. Where investments are debt-financed, overinvesting in unproductive projects results in the buildup of debt, monetary expansion, instability in financial markets, and economic fragility, exactly as we see in China today. We conclude that poorly managed infrastructure investments are a main explanation of surfacing economic and financial problems in China. We predict that, unless China shifts to a lower level of higher-quality infrastructure investments, the country is headed for an infrastructure-led national financial and economic crisis, which is likely also to be a crisis for the international economy. China's infrastructure investment model is not one to follow for other countries but one to avoid

    Integrate the GM(1,1) and Verhulst models to predict software stage effort

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    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Software effort prediction clearly plays a crucial role in software project management. In keeping with more dynamic approaches to software development, it is not sufficient to only predict the whole-project effort at an early stage. Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process. This can assist the project manager to reestimate effort and adjust the project plan, thus avoiding effort or schedule overruns. This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst. This method establishes models dynamically according to particular types of stage-effort sequences, and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism. We evaluate the proposed method with a large-scale real-world software engineering dataset, and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively. The results indicate that the method can be effective and has considerable potential. We believe that stage predictions could be a useful complement to whole-project effort prediction methods.National Natural Science Foundation of China and the Hi-Tech Research and Development Program of Chin

    An Intelligent Early Warning System for Software Quality Improvement and Project Management

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    One of the main reasons behind unfruitful software development projects is that it is often too late to correct the problems by the time they are detected. It clearly indicates the need for early warning about the potential risks. In this paper, we discuss an intelligent software early warning system based on fuzzy logic using an integrated set of software metrics. It helps to assess risks associated with being behind schedule, over budget, and poor quality in software development and maintenance from multiple perspectives. It handles incomplete, inaccurate, and imprecise information, and resolve conflicts in an uncertain environment in its software risk assessment using fuzzy linguistic variables, fuzzy sets, and fuzzy inference rules. Process, product, and organizational metrics are collected or computed based on solid software models. The intelligent risk assessment process consists of the following steps: fuzzification of software metrics, rule firing, derivation and aggregation of resulted risk fuzzy sets, and defuzzification of linguistic risk variables

    Double whammy – how ICT projects are fooled by randomness and screwed by political intent

    Get PDF
    The Iron Triangle formulates the holy trinity of objectives of project management – cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindness cause forecasts to fall short of actual costs. Firstly, optimism bias has been linked to effects of deception and delusion, which is caused by taking the inside-view and ignoring distributional information when making decisions. Secondly, we argued before that Black Swan Blindness makes decision-makers ignore outlying events even if decisions and judgements are based on the outside view. Using a sample of 1,471 ICT projects with a total value of USD 241 billion – we answer the question: Can we show the different effects of Normal Performance, Delusion, and Deception? We calculated the cumulative distribution function (CDF) of (actual-forecast)⁄forecast. Our results show that the CDF changes at two tipping points – the first one transforms an exponential function into a Gaussian bell curve. The second tipping point transforms the bell curve into a power law distribution with the power of 2. We argue that these results show that project performance up to the first tipping point is politically motivated and project performance above the second tipping point indicates that project managers and decision-makers are fooled by random outliers, because they are blind to thick tails. We then show that Black Swan ICT projects are a significant source of uncertainty to an organisation and that management needs to be aware of. Finally, we draw implications about the underlying generative processes that lead to power law behaviour, which might help to further understand the pitfalls and shortcomings of cost and cost risk management in ICT projects

    Effects of Contract Procurement Factors on Performance of Transportation Projects

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    Cost and schedule savings are the main measures of a project’s success. Several factors affect the cost and schedule performances in a construction project, such as design changes, material, labor and equipment shortages, unpredictable weather conditions, and errors & omissions in contract documents.Some studies have shown that either the construction cost or the schedule performance of a project was dependent on the procurement factors, namely: bid cost, number of bidders, the bid cost deviation between the first and second bidder, the liquidated damage rate per day, the type of a contract, and the project location. However, a comprehensive study on the combined effect of procurement factors on performance metrics has not been yet conducted. Therefore, this study collected all the available contract procurement factors to determine the combined effect of these factors on the construction cost and the schedule performances. In addition, the multiple linear regression models within the study were developed to predict the performance metrics based on these factors. For this study, the project data completed between the year 2000 and 2016 were collected from two state department of transportations (DOTs): Texas and Florida. The results showed that not only cost growth but also schedule growth had a significant correlation between the liquidated damage rate per day, the type of a contract funding, the type of a contractor, and the location of a project. The validation process showed that the models developed during this study could predict project performance metrics accurately. Further research is recommended with more state DOTs data to check whether the relationships between the procurement factors and project performance metrics are similar to those found in this study

    Better than you think? Exploring cost and schedule overruns in government IT projects

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    Information Technology (IT) projects experience cost and schedule overruns, and some fail altogether. We investigated 54 completed government IT projects, completed from 2011 – 2020. We present a mixed-method inquiry into Danish government IT projects. We used archival data to examine cost and schedule overruns in these projects, using measures established by Flyvbjerg. To further inform our understanding of the various drivers that influence these projects cost and schedule overrun, we conducted a qualitative study using interviews and documents analysis. Our findings show that projects in our sample experience much lower cost and schedule overruns than those reported in previous studies. Our qualitative analysis show that projects are more likely to be completed within time and schedule when project managers actively adopt a set of practices that help these projects to perform positively. These practices are: Building one team, accommodating uncertainty, rigorous project management and capitalizing previous domain knowledge

    Technical Report Value of Systems Engineering

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    This report is a follow-on from the June 2004 Air Force/Lean Aerospace Initiative Workshop on Systems Engineering for Robustness

    An Empirical Analysis of DoD Construction Task Order Performance

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    Cost and schedule overrun plague over 50 of all construction projects, engendering diminished available funding that leads to deferred maintenance and impaired award ability for needed projects. Though existing research attempts to identify overruns sources, the results are inconclusive and frequently differ. Accordingly, this research reviews DoD construction contract data from the past ten years to identify the contract attributes of 79,894 projects that correlate with superior performance for use in future project execution. This research starts with creating a database that houses the largest single source of construction contract information. The research then evaluates the data to determine if differences in project performance exist when comparing contracting agents, funding agents, and award months. Next, the research utilizes stepwise logistic regression to determine the significant contract attributes and predict future projects overrun likelihoods. Model accuracy for predicting the likelihood of cost and schedule overrun is 65% and 75%, respectively. Finally, this research concludes by providing insights into efforts that could improve modeling accuracies, thereby informing better risk management practices. This research is expected to support public and private sector planners in their ongoing efforts to execute construction projects more cost-effectively and better utilize requested funds

    Estimating Required Contingency Funds for Construction Projects using Multiple Linear Regression

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    Cost overruns are a critical problem for construction projects. The common practice for dealing with cost overruns is the assignment of an arbitrary flat percentage of the construction budget as a contingency fund. This research seeks to identify significant factors that may influence, or serve as indicators of, potential cost overruns. The study uses data on 243 construction projects over a full range of project types and scopes gathered from an existing United States Air Force construction database. The author uses multiple linear regression to analyze the data and compares the proposed model to the common practice of assigning contingency funds. The multiple linear regression model provides better predictions of actual cost overruns experienced. Based on the performance metric used, the model sufficiently captures 44% of actual cost overruns versus current practices capturing only 20%. The proposed model developed in this study only uses data that would be available prior to the award of a construction contract. This allows the model to serve as a planning tool throughout the concept and design phases. The model includes project characteristics, design performance metrics, and contract award process influences. This research supports prior findings of a relationship between design funding and design performance as well as the influence of the contract award process on cost overruns. While the proposed model captures 44% of actual cost overruns, its application reduces average contingency budgeting error from -11.2% to only -0.3% over the entire test sample
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