Industry Foundation Processes (IFP): Theoretical and Practical Foundations for the Construction Industry
Bibliographic record
Abstract
Industry foundation processes are formulated to improve capital project process conformance and interoperability. These processes are used to implement key elements of practices. Several research studies confirm that the implementation of best practices drives better engineering and construction project performance. Best practices are defined by the Construction Industry Institute (CII) as processes or methods that when executed effectively, lead to enhanced project performance. Particular organizations, such as the CII, the Construction Owners Association of Alberta (COAA), and the Project Management Institute (PMI), develop and promote best practices pertaining to various aspects of capital project delivery. However, the systematic and consistent implementation of such practices throughout the lifecycle of a construction project and from project to project remains a challenge. \nResearch findings also reveal that improved adoption of best practices, through conformance with their processes, and improved interoperability, are correlated with substantial capital project performance improvements in terms of cost, schedule, and productivity. In many industry sectors, such as health care, manufacturing, and banking, process conformance has been radically improved through the automation of processes via workflow engines, and several efforts are being made to regulate standards to facilitate process interoperability. However, process conformance and interoperability in the construction industry are lagging behind. In the construction industry, a promising solution for facilitating effective and consistent conformance with best practices lies in the employment of workflow processes and workflow engines. \nThe concept of Industry Foundation Processes (IFP) and the theory and framework for IFP development and implementation are established in this research. The objective is to integrate construction industry best practices into Electronic Product and Process Management (EPPM) systems, and improve process interoperability and conformance. EPPM systems, which are increasingly being used for managing mega capital projects, can be described as the meta- managers of other systems, such as document management systems (DMS), building information modeling (BIM), workflow management systems (WfMS), and advanced project management systems. Integration of best practices into EPPM systems facilitates more consistent and scalable adoption of best practices in large-scale construction projects, resulting improved project performance. \nIFPs are defined as standard workflows based on known best practices in the construction industry with certain features and characteristics to improves process conformance and facilitates process interoperability. The research methodology is comprised of four main phases: (1) developing methods and mechanisms that can be used to transform best practices into structured workflow process in such a way as to retain the essence of the best practices, (2) defining the IFP concept and establishing a framework and an ontology for inheritance and customization of IFPs for specific corporate and project circumstances, (3) customizing and implementing particular IFPs in an EPPM system, based on available records for specific construction projects, and investigating the applicability and effectiveness of the IFP concept, and (4) analyzing and validating the value of the IFP system through functional demonstration of the benefits, including process conformance and interoperability. \nThe scope of the thesis is the theoretical development of IFP system, in addition to implementation studies for a limited number of IFP processes within the domain of industrial sector construction projects. The development and application of the IFP system is anticipated to result in more effective adoption of best practices and enhanced process conformance and interoperability, with the end-result of improved capital project performance.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".