A Literature Review on Global Construction Productivity Measurement: A Cross-Sectoral Approach
Bibliographic record
Abstract
Advanced construction practices such as Modern Methods of Construction (MMC) have gained significant traction across construction sectors globally with the US and Europe reporting $22 billion in annual cost savings and $1.6 billion in capital productivity gains by scaling MMC practices in 2019 as reported by McKinsey & Co.The Canadian construction sector in recent years has not achieved similar returns to scale on its productive capacity, with the last quarter of 2023 and the first quarter of 2024 marking record decade-lows in construction capacity utilization as reported by Statistics Canada.In the context of this shortfall, this study analyzes how these respective gains in productivity were recorded for construction projects completed outside Canada.To understand how advanced construction practices have enhanced the measurable value of construction projects globally, this paper undertook a literature review of academic and industry literature to analyze the constituent dimensions of productivity across 38 non-Canadian guidelines and frameworks documenting construction productivity practices using either traditional or MMCbased techniques.Reviewing this evidence yielded a final list of 12 empirical productivity frameworks used across real-world projects.Due to their standardized processes and metrics, the productivity measurement practices from these frameworks can be adapted to both conventional and MMC-based projects across sectors spanning building types dedicated for residential, commercial, and mixed-use cases.The subsequent findings identified opportunities to enhance current performance measurement practices for Canadian builders.These findings could potentially guide domestic operators in benchmarking their performance in line with global frameworks and identify areas for further standardization. Research Question 1.What are the key differences in productivity measurement practices across residential, commercial, and mixed-use construction sectors globally?Research Objective (RO) 1. Identify differences in productivity measurement processes across different building types part of construction sectors globally.Research Question 2. How can the productivity Key Performance Indicators (KPIs) from global frameworks be categorized into performance areas specific to each sector?Research Objective (RO) 2. Identify trends in KPIs present among different building types part of construction sectors globally. Research Question 3.What insights can global frameworks provide to improve productivity measurement practices in the Canadian construction sector?Research Objective (RO) 3. Outline research implications from the construction projects completed abroad that can be applicable to the Canadian construction sector.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.063 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".