Time Elasticity - who and what Determines the Correct Project Duration?

Johansen, Agnar; Landmark, Andreas D.; Olshausen, Fredrick; Kooij, R. van der; Skappel, Sidsel

doi:10.1016/j.procs.2016.09.199

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…The authors then multiplying the total by 1000 as each term's normalized value is a very small value. The normalized value of each term per document is shown in Equation 2and (3).…”

Section: Normalizing Of Text (Tdm) Across Projectsmentioning

confidence: 99%

“…Most of these identified causes of profit loss deficit equate to significant project schedule delays. According to Johansen et al [3], project schedule delay is one of the main causes of project cost increases. Thus, increasing schedule efficiencies to minimize delays is an effective measure to reduce the level of deficit.…”

Section: Introductionmentioning

confidence: 99%

“…version 3.4 3,. an open source platform, for text data importing, pre-processing, and structuring process and the SDEM regression analysis.…”

mentioning

confidence: 99%

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Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process

Son

2019

Energies

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Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant cost overruns. Existing literature has identified one of the major causes of project delays to be the lack of adequate tools or techniques to diagnose the appropriateness and sufficiency of the contract deadline proposed by project owners prior to signing the contract in the bid. As such, this paper seeks to propose appropriate or correct project durations using the research methodology of text mining for bid documents. With the emergence of ‘big data’ research, text mining has become an acceptable research strategy, having already been utilized in various industries including medicine, legal, and securities. In this study the scope of work (SOW), as a main part of EPC contracts is analyzed using text mining processes in a sequence of pre-processing, structuring, and normalizing. Lessons learned, collected from 13 executed off shore EPC projects, are then used to reinforce the findings from said process. For this study, critical terms (CT), representing the root of past problems, are selected from the reports of lessons learned. The occurrence of the CT in the SOW are then counted and converted to a schedule delay risk index (SDRI) for the sample projects. The measured SDRI of each sample project are then correlated to the project’s actual schedule delay via regression analysis. The resultant regression model is entitled the schedule delay estimate model (SDEM) for this paper based on the case studies. Finally, the developed SDEM’s accuracy is validated through its use to predict schedule delays on recently executed projects with the findings being compared with actual schedule performance. This study found the relationship between the SDRI, frequency of CTs in the SOW, and delays to be represented by the regression formula. Through assessing its performance with respect to the 13th project, said formula was found to have an accuracy of 81%. As can be seen, this study found that more CTs in the SOW leads to a higher tendency for a schedule delay. Therefore, a higher project SDRI implies that there are more issues on projects which required more time to resolve them. While the low number of projects used to develop the model reduces its generalizability, the text mining research methodology used to quantitatively estimate project schedule delay can be generalized and applied to other industries where contractual documents and information regarding lessons learned are available.

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“…The authors then multiplying the total by 1000 as each term's normalized value is a very small value. The normalized value of each term per document is shown in Equation 2and (3).…”

Section: Normalizing Of Text (Tdm) Across Projectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process

Son

2019

Energies

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“…Uncertainty is believed to occur because of a variety of ambiguous and complex causal events underpinning an organization's internal processes and external environment (Colorado, 1999;Karlsen, 2011). Additionally, it may emerge because of project delays, quality difficulties, insufficient time and resources spent during the initial pre-planning and planning phases, and an inability to cope effectively with unforeseen events that occur during the project's execution (Johansen et al 2016). Uncertainty is multifaceted and complex and depends on the issue at hand (Johansen et al 2016;Wazed et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, it may emerge because of project delays, quality difficulties, insufficient time and resources spent during the initial pre-planning and planning phases, and an inability to cope effectively with unforeseen events that occur during the project's execution (Johansen et al 2016). Uncertainty is multifaceted and complex and depends on the issue at hand (Johansen et al 2016;Wazed et al 2009). For example, from a situational standpoint, it occurs when (1) the order or nature of things is unknown, (2) the consequences, extent, or magnitude of circumstances, conditions, or events is unpredictable, (3) credible probabilities to possible outcomes cannot be assigned (see "Knightian uncertainty," Alvarez et al 2018, based on Knight, 1921; and (4 (Starbuck and Milliken, 1988).…”

Section: Introductionmentioning

confidence: 99%

A Review and Classification of the Uncertainties in Projects: The Way Forward

Anand

Castello

Lecoeuvre

2021

RFGI

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This article aims to summarize existing research on various sources of uncertainty and to classify them based on the determinants and antecedents addressed in projects. A systematic review is conducted using a total of 140 research published in peer-reviewed scientific journals during the last three decades. We classify various uncertainty based on their individual, relational, group, organizational, project-oriented, and managerial characteristics. Additionally, we uncovered a few gaps such as, how uncertainties differ based on the size of the organization, the need for exploring uncertainty from a more cross-disciplinary perspective, differentiating the concept of complexity and risk with uncertainty and the role of chaos theory, that require future study. By leveraging significant findings, this is study contributes from the perspective of theory and practice to academic, project, and industrial management discipline.

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