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Record W4296172102 · doi:10.29173/mocs274

A Fuzzy-AHP and House of Quality integrated approach for Lean Construction Concepts Assessment in Off-site Construction

2022· article· en· W4296172102 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Management Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLean project managementLean manufacturingValue stream mappingAnalytic hierarchy processLean constructionRanking (information retrieval)Pairwise comparisonHouse of QualityProcess (computing)Factory (object-oriented programming)Quality (philosophy)Computer scienceProcess managementManufacturing engineeringEngineeringRisk analysis (engineering)Operations researchBusinessService qualityConstruction engineeringConstruction industryArtificial intelligence

Abstract

fetched live from OpenAlex

Lean Construction (LC) combines theoretical research and industry best practices in an off-site industrialized construction environment that adopts Lean Manufacturing (LM) concepts and the know-how to reduce waste in the end-to-end lean construction process. Off-site construction industries strive to implement lean manufacturing theory and application to maximize the allocation of their resources, reduce construction waste, and optimize processes to be economically competitive. However, decision-makers usually encounter barriers while selecting the best lean tools for successful integration. Those barriers are organizational priorities, mass customization, process limitation, and improvement consensus. As a result, lean practitioners tend to implement tools such as Value Stream Mapping (VSM), Process Activity Mapping (PAM), Root Cause Diagram (RCD), Failure Mode Effect Analysis (FMEA), Pareto Analysis (PA) to analyze and propose improvements to a manufacturing process effectively. However, the construction industry lacks a tool that can assess the effectiveness of the lean construction concepts implementation. Thus, this paper proposes an innovative approach to select and evaluate the appropriate lean concepts implemented in an off-site industrialized factory. Firstly, the assessment matrix utilizes Fuzzy-AHP in a pairwise comparison to determine the relationships and calculate the correlations between lean concepts based on the designed hierarchy structure. Secondly, the House of Quality (HoQ) matrix will be integrated to prioritize the selection criteria based on the company's strategic requirements and customer requirements. Finally, the proposed multi-criteria multi-decision ranking matrix is able to prioritize the top lean concepts and demonstrate their combinational impact by eliminating participant's subjectivity, bias, and preferences. The proposed assessment matrix was implemented in an off-site panelized construction case study to prove its effectiveness and validity. The results presented the synergies between different lean concepts combinations and their importance in a lean construction environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.262
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it