Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques

Soheila Sadeghi

REVIEW 4 major objections 5 minor 18 references

Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that machine-learning models, especially XGBoost, can predict the cost impact of project scope changes almost perfectly on simulated construction data, with a Decision Tree model best for schedule impact.

desk verdict Clear ML benchmark on a small construction dataset, but the simulated target variables mean the reported R2 of 0.985 says little about real projects. read the letter →

arxiv 2412.02041 v1 pith:HDOAMZ7G submitted 2024-12-02 cs.LG

Soheila Sadeghi This is my paper · ORCID

classification cs.LG

keywords costimpactschedulescopechangespredictivemodelingmachinelearningXGBoostdecisiontreeconstructionprojects

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Scope changes in construction projects routinely cause cost overruns and schedule delays, but their impact is usually assessed with subjective judgment. This paper tries to replace that judgment with machine learning: it trains six models on a 221-row dataset of construction project tasks, including simulated scope-change variables, to predict the monetary and schedule impact of a change. The author claims that XGBoost predicts cost impact with an $R^2$ of 0.985, and that a Decision Tree model predicts schedule impact better than the alternatives. If those results hold on real projects, project managers could estimate the consequences of a proposed scope change before approving it. The paper itself cautions that the models still need validation on additional datasets.

What carries the argument

The central mechanism is the supervised learning pipeline built around the XGBoost algorithm, an optimized gradient-boosting implementation that combines many shallow decision trees with regularization, and the Decision Tree classifier used for schedule impact. The input features are project task attributes—WBS codes, task type, productivity rate, estimated and actual cost, duration, and task dependencies—plus two scope-change variables (magnitude and timing). The targets are the generated 'impact on cost' and 'impact on schedule' values. The pipeline scales numeric features, splits data 80/20, applies cross-validation and hyperparameter tuning, and uses residual plots and feature-importance rankings to evaluate and interpret the models.

What would settle it

Hold out a set of real construction projects with recorded scope changes and observed cost and schedule impacts, run the published preprocessing and model pipeline on them, and compare predicted versus actual impacts; if the cost-impact $R^2$ falls far below the reported 0.985, or the Decision Tree's schedule predictions do not beat a simple majority-class baseline, the simulated-data result is an artifact of the data generation process.

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Extended reading notes

Core claim

The paper's central claim is that a supervised machine-learning model, XGBoost, predicts the monetary impact of a scope change on a construction project with an $R^2$ of 0.985 on the test set, and that a Decision Tree model gives the best predictions of schedule impact among the six models compared. Feature-importance analysis identifies productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements as the dominant predictors of both cost and schedule impact. The author presents these results as evidence that data-driven models can give project managers objective, early estimates of scope-change consequences, replacing subjective judgment. The work is framed as an extension of earlier machine-learning forecasting to the specific problem of scope-change impact.

Load-bearing premise

The load-bearing premise is that the simulated values for productivity rate, scope change magnitude, timing, and their impacts on cost and schedule are realistic enough to stand in for real project outcomes; if those synthetic relationships differ from reality, the reported accuracy will not transfer to actual projects.

Editorial extensions

If this is right

Project managers could estimate the cost and schedule impact of a proposed scope change before approving it, turning scope-change review from a reactive into a proactive process.
The feature-importance rankings suggest that monitoring productivity rate, scope change magnitude, and task dependencies would catch the largest cost and schedule risks early.
The same pipeline, retrained on richer data, could be extended to predict the probability and magnitude of cost overruns at project completion, as the paper suggests.
Organizations that already track WBS-level task data could use these models to run scenario analyses for stakeholder negotiations and resource allocation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The near-perfect $R^2$ values, combined with the high correlation (0.94) between estimated and actual cost, suggest the cost model may be leaning heavily on near-duplicate cost information; the model trained without actual costs is the more realistic deployment test.
The schedule target appears to be binary (0/1) from the descriptive statistics, yet the paper evaluates schedule models with regression metrics; a fair comparison would use classification accuracy, precision, and recall.
A cheap test of the whole approach is to benchmark against a trivial baseline that always predicts the mean cost impact; if tree ensembles only beat that baseline on simulated data, their practical value is unproven.
Because the data are simulated, the feature-importance findings are really hypotheses about which project attributes matter, and they could be checked against published empirical studies of scope-creep cost drivers.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a clearly described benchmark of six standard ML models on 221 rows of construction task data. That is the main thing to credit: the preprocessing steps, model configurations, and feature-importance analysis are concrete enough to reproduce on the same data. The feature-importance findings (productivity rate, scope change magnitude, task dependencies, WBS elements) are unsurprising but consistent with project management domain knowledge.

The central problem is that the target variables—impact on cost and impact on schedule—are simulated. Section II.A states these values were 'generated to represent realistic project scenarios,' but no generation mechanism, no distributional comparison to empirical outcomes, and no external dataset are provided. So the headline result, XGBoost R2 = 0.985 for cost impact, is a measure of fit to an invented ground truth. The paper itself concedes in the conclusion that the models need validation on additional datasets when they become available. That is honest, but it also means the practical conclusion is unsupported.

The soft spots are serious. First, the schedule-impact analysis is muddled: Impact_on_Schedule is binary (min 0, max 1, mean 0.57), yet the authors run regression and even configure the decision tree with method='class', then evaluate it with a confusion matrix. The reported MSE/R2 for schedule impact are not clearly interpretable. Second, the numbers are internally inconsistent across sections: the XGBoost model gets R2 = 0.985 for cost (Section IV.B), while Section III.C reports an XGBoost R2 of 0.18 for schedule, and the paper mixes the two without making the distinction obvious. Third, there is a clear citation error (reference [3] is a random book entry).

These are not minor quibbles. The simulated-target issue is load-bearing, and the schedule analysis is hard to trust. That said, the paper is not a crank document; it is straightforward and honest about its limitations, and it could serve as a starting point for a real empirical study.

Who is the audience? Someone looking for a cautionary example of why simulated targets need external validation, or a project-management researcher planning a data-collection effort. I would not cite it as evidence of predictive performance, and I would not send it for full peer review as-is; a desk reject with an encouraging note about proper validation would be the right call.

Referee Report

4 major / 5 minor

Summary. This paper trains six machine-learning models on a 221-row dataset of construction project tasks to predict the impact of scope changes on cost and schedule. The features include WBS, task type, estimated cost, actual cost, duration, task dependencies, and simulated values of productivity rate, scope change magnitude, impact on cost, impact on schedule, and scope change timing. The authors report that XGBoost performs best for cost impact (R2=0.985) and that Decision Tree performs best for schedule impact, with feature importance highlighting productivity, scope change magnitude, task dependencies, and WBS elements. The paper concludes that these models can help project managers forecast and mitigate the consequences of scope changes.

Significance. If the reported accuracy held on real project data, the paper would offer a practically useful early-warning tool for scope-change cost and schedule overruns. The paper uses a reasonable preprocessing pipeline, applies multiple standard models, includes cross-validation and residual plots, and explicitly acknowledges the need for additional validation data. However, the significance is currently not established: the target variables are simulated rather than observed, the generation mechanism is undisclosed, the evaluation is entirely internal to the synthetic dataset, and several reported performance figures are mutually inconsistent. As a result, the paper is better read as an illustrative ML workflow on synthetic data than as evidence for predictive utility in real projects.

major comments (4)

[Section II.A, Section V] The central claim of accurate prediction rests on target values that were simulated by the authors ('simulated values for ... impact on cost, scope change magnitude, scope change timing, which were generated to represent realistic project scenarios'), not on observed outcomes from real scope-change events. The paper does not disclose the data-generation mechanism, compare the simulated variables to empirical project data, or evaluate on any external dataset; Section V concedes that 'the models could be further validated with additional data sets when they become available.' Therefore the headline R2 values, such as XGBoost's 0.985 in Section IV.B, demonstrate fit to a synthetic target rather than predictive skill on real projects, and the practical conclusions in Sections IV.C and V are unsupported.
[Section IV.B vs Section III.C] The reported model rankings are internally inconsistent. Section IV.B states that XGBoost achieved R2=0.985 for cost impact, while Section III.C reports an XGBoost MSE of 0.2094 and R2 of 0.1804; the manuscript does not explain whether these refer to different targets or different experimental setups. Similarly, Ridge Regression is reported with R2=0.991 in Section IV.B but with R2=0.0281 in Section III.B for schedule impact. Without a consistent set of metrics for the same prediction tasks, the claim that XGBoost best predicts cost impact and Decision Tree best predicts schedule impact is not reproducible.
[Section III.A and Table I] The Decision Tree is configured as a classifier ('method' set to 'class', evaluation via confusionMatrix), but the stated target, Impact_on_Schedule, is analyzed as a numeric variable with regression MSE and R2 elsewhere. Table I shows this variable has min=0, max=1, and mean=0.57, strongly suggesting a binary outcome. Mixing classification and regression treatments of the same target makes the schedule-impact results ambiguous and prevents meaningful comparison with the cost-impact results.
[Section III.F, Section IV.A, Section IV.B] The feature importance findings are contradictory. Section III.F reports that, for an XGBoost model trained without actual costs, the most influential feature is wbs.86, with scope change magnitude and task dependencies also prominent. Section IV.A and Section IV.B instead state that productivity rate is the most influential feature and that productivity rate, scope change magnitude, and task dependencies are the most critical factors. Because the manuscript does not identify which experimental configuration produced which importance ranking, the feature importance claims are not reproducible and cannot be used as practical guidance.

minor comments (5)

[References] Reference [3] appears to be mis-copied from another bibliography ('Synthetic structure of industrial plastics (Book style with paper title and editor)') and is not a usable citation for scope creep in project management.
[Section III.D] The text states that Fig. 4 shows 'different cp values for the XGBoost model,' but cp is a complexity parameter for rpart-style trees, not an XGBoost hyperparameter; the figure and text should be aligned with the XGBoost parameters (eta, max_depth, etc.) actually selected.
[Section III.D] Several sentences in this subsection are incomplete, for example 'The RMSE values across different cp values, indicating the optimal cp value for minimizing RMSE in the model,' and should be rewritten as full sentences.
[Table I] The column names contain inconsistent spacing and underscores (e.g., 'Productivity_ Rate'), and the table would benefit from consistent naming to match the feature names used in the text.
[Reproducibility] The paper does not provide access to the code, the processed dataset, or the simulation script; for reproducibility, these should be made available or the data citation [18] should be extended with a persistent link.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: model predictions are out-of-sample fits to a disclosed synthetic target; the self-citations are background only.

full rationale

This is an empirical machine-learning paper, so the circularity pass seeks steps where a prediction is forced by construction. The data section states: 'The dataset also includes simulated values for key variables such as productivity rate, impact on cost, scope change magnitude, scope change timing, which were generated to represent realistic project scenarios.' The target variables are therefore synthetic, but no disclosed equation defines Impact_on_Cost or Impact_on_Schedule in terms of the model inputs, and the test-set R2 values (e.g., XGBoost R2 = 0.985 in Section IV.B) are computed on a held-out split after training. No fitted parameter is renamed as a prediction, and no conclusion relies on a uniqueness theorem or an ansatz imported from the author's prior work. The author's self-citations ([7], [8], [16]) appear as background context and are not load-bearing. The lack of external validation against real project data is a serious limitation, acknowledged in Section V ('the models could be further validated with additional data sets when they become available'), but it is a validity concern, not circularity. No specific reduction from target to input or model to fit can be exhibited.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim depends on treating simulated impact values as real ground truth, on a single-source small dataset, and on standard ML assumptions. No new entities are introduced; the key unexamined input is the simulation itself.

free parameters (4)

XGBoost hyperparameters = eta=0.1, max_depth=3, colsample_bytree=0.7, subsample=0.7, nrounds=100
Selected via cross-validation on the simulated dataset (Section III.D).
Random forest tree count = 100
Set as a parameter without tuning (Section III.A).
Ridge regularization alpha = 0
Set to 0 to evaluate without regularization (Section III.A).
Simulated data generation parameters = not disclosed
Impact on cost, scope change magnitude, productivity rate, and scope change timing are simulated; the generation rules are not provided, making the target an arbitrary construct (Section II.A).

assumptions (3)

ad hoc to paper Simulated values for productivity rate, impact on cost, scope change magnitude, and scope change timing represent realistic project behavior
The paper replaces real target measurements with generated values and treats them as ground truth for training and evaluation (Section II.A).
domain assumption The 221-row subset from a single contractor's historical records is representative of construction projects generally
Generalization from one project to all projects is assumed (Section II.A).
standard math Standard supervised learning assumptions: the training and test splits are i.i.d. samples from the same distribution
Required for the reported R2 and MSE to estimate predictive performance (Section III.B).

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Cite this review

Pith. "Pith review of Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques." pith.science (2026). https://pith.science/paper/HDOAMZ7G

@misc{pith2026241202041,
  author       = {Pith},
  title        = {Pith review of: Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDOAMZ7G}},
  note         = {Machine review of arXiv:2412.02041}
}

read the original abstract

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Model robustness and generalization are assessed using cross-validation techniques. To evaluate the performance of models, we use Mean Squared Error (MSE) and R2. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The study identifies the most influential project attributes in determining the magnitude of cost and schedule deviations caused by scope modifications. It is identified that productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are powerful predictors.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [3]

Synthetic structure of industrial plastics (Book style with paper title and editor),

S. Amjad, N. Ahmad, T. Saba, A. Anjum, U. Manzoor, M. A. Balubaid, and S. U. R. Malik, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64

work page 1964

[2] [1]

Managing project scope creep in construction industry,

M. M. Ajmal, M. Khan, A. Gunasekaran, and P. T. Helo, “Managing project scope creep in construction industry,” Engineering, Construction and Architectural Management, vol. 29, no. 7, pp. 2786–2809, 2022

work page 2022

[3] [2]

Identifying stakeholders’ involvement that enhances project scope definition completeness in Saudi Arabian public building projects,

M. K. Fageha and A. A. Aibinu, “Identifying stakeholders’ involvement that enhances project scope definition completeness in Saudi Arabian public building projects,” Built Environment Project and Asset Management, vol. 6, no. 1, pp. 6–29, 2016

work page 2016

[4] [4]

Agile project management challenge in handling scope and change: A systematic literature review,

P. Marnada, T. Raharjo, B. Hardian, and A. Prasetyo, “Agile project management challenge in handling scope and change: A systematic literature review,” Procedia Computer Science, vol. 197, pp. 290 –300, 2022

work page 2022

[5] [5]

Application of KDD techniques to extract useful knowledge from labor resources data in industrial construction projects,

A. Hammad, S. AbouRizk, and Y. Mohamed, “Application of KDD techniques to extract useful knowledge from labor resources data in industrial construction projects,” Journal of Management in Engineering, vol. 30, no. 6, 05014011, 2014

work page 2014

[6] [6]

Application of machine learning in project management,

A. Bidhendi and M. Azizi, “Application of machine learning in project management,” 2021

work page 2021

[7] [7]

Budget allocation problem for projects with considering risks, robustness, resiliency, and sustainability requirements,

R. Lotfi, M. Vaseei, S. S. Ali, S. M. R. Davoodi, M. Bazregar, and S. Sadeghi, “Budget allocation problem for projects with considering risks, robustness, resiliency, and sustainability requirements,” Results in Engineering, vol. 24, 102828, 2024

work page 2024

[8] [8]

Enhancing Project Performance Forecasting using Machine Learning Techniques

S. Sadeghi, “Enhancing project performance forecasting using machine learning techniques,” arXiv preprint arXiv:2411.17914, 2024

work page Pith review arXiv 2024