Unlocking Digital Construction Management Software to Support Cash Flow and Profitability Analysis
Bibliographic record
Abstract
Effective financial planning and control of construction projects depend on proper cash flow and profitability analytics to achieve successful outcomes.Digitalization of construction data, through construction management software tools, has gained momentum in the past 5 to 10 years.Procore is a leading example of such solutions, holding approximately 6.2% of the global construction software market and ranking second in the category.While offering avenues for collecting and organizing the data of construction operations and administrative processes, the full potential of descriptive and predictive analytics of these tools, alongside ERP systems, for managing project cash flows remains untapped.This study explores and discusses the analytics that can be derived from Procore, and one ERP system, to improve cash flow planning and control, and profitability monitoring and improvement for construction contractors.In this regard, an approach is developed to extract and aggregate cashflows from the data of transactions, and three use cases are enabled accordingly: (i) evaluating payment delay's impacts on profit using ERP system data; (ii) evaluating financial impacts of change orders; and (iii) performing cash activityflow cross-analyses to improve project liquidity and resource allocation.Results of applying the proposed method to real MEP-related data and data of a sample project shed some light on how untimely payments, different project activities and types of change orders affect the contractors' cash flow and profitability.By harnessing construction data properly, contractors can be empowered towards improved project planning and control practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".