Optimizing request for information (RFI) prioritization with machine learning

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Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings As conflict resolution and communication tools, RFI’s are key for tackling issues before they grow, especially in complex projects such as the construction industry. In the article by Yilmaz et al. (2025), the authors found empirically that machine learning models enable the prediction of RFI resolution times with high accuracy, allowing for prioritization of the largest tickets and reducing backlog for optimal resolution times and reduced costs. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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PurposeThe purpose of this paper is to study the nature of request for information (RFIs) on construction projects by using data analytics to understand the frequency of RFIs, when they occur on projects, and the relationship between project characteristics and frequency of RFIs and between project characteristics and RFI turnaround time.Design/methodology/approachA data-analytic approach using RStudio and Minitab software on 168 construction project cases in Australia and New Zealand involving 1,032,949 correspondences and 53,042 RFI event records made available by Aconex, one of the world largest cloud-based project management platform.FindingsLarge and complex projects tend to have significantly larger number of RFI events per day and longer RFI turnaround when compared with smaller and less complex projects. Projects with fewer users per organisation recorded a higher RFI turnaround time when compared with projects with more users per organisation – users mean persons involved in managing the project using the online platform (an index of project complexity). RFIs occur early on less complex projects and occur later on more complex projects.Research limitations/implicationsBenchmarks of RFI incidences and turnaround time have been developed for various project characteristics and, practitioners can use them to monitor the RFI performance of projects. Organisations need to pay greater attention to staffing levels needed to handle RFIs to reduce RFI turnaround time.Originality/valueA data-analytic study of RFI yielded insights for managing RFIs. The findings of previous studies on RFIs are difficult to generalise because they are based on single project case study. The influence of project characteristics on RFI frequency and RFI turnaround time is not yet known.

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Enhancing RFI management in construction through machine learning-driven predictive models
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  • Smart and Sustainable Built Environment
  • Neziha Yilmaz + 2 more

Purpose Request for information (RFI) documents are essential for communication and issue resolution in construction projects; however, prolonged RFI resolution times can impact project timelines. This study aims to predict RFI closure durations as they are created and addressed to help identify and prioritize RFIs likely to remain open longer. Design/methodology/approach A dataset of 3,628 RFI documents from a large-scale airport project was used. Five machine learning (ML) algorithms, support vector machine (SVM), logistic regression (LR), K-nearest neighbors (KNN), decision tree (DT) and random forest (RF), were used to create a multi-model prediction framework for RFI closure durations. The models incorporated both categorical metadata and textual data with a staged input structure simulating real project conditions. Findings The most effective algorithms for predicting RFI closure durations were SVM for the model using only RFI metadata parameters as input, and DT when using RFI metadata parameters together with RFI response durations as input. Prediction accuracy improved significantly after using the first response durations, ranging from 59% to 92% for the different models presented. Practical implications Integrated into common data environments, the models enable real-time prediction and prioritization of RFIs, helping teams reduce delays and optimize resources. They also support digital transformation in construction and suggest potential for policy development around predictive analytics in project management. Originality/value This study created prediction models for prioritizing RFIs based on their expected closure durations and identified the most effective ML algorithms for different input variables.

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Requests for information (RFIs) are frequently used by the architecture, engineering, construction, and operation (AECO) industry to resolve doubts and inaccuracies and to request complimentary information. However, they can be inefficient due to the lag between issuance and response, generating delays and cost overruns. The building information modeling (BIM) methodology allows for the integration of information by using virtual models and facilitating the control and management of RFIs; however, the full potential of BIM is not being exploited. Therefore, this work aims to analyze the RFIs of 17 high-rise building projects in conjunction with the application of BIM for their mitigation. To achieve this, categories of RFI categories were identified, the issuance and response process was analyzed, the building projects’ RFIs were classified, the most frequent problems were identified, the BIM uses that would allow for a reactive or preventive action were identified, and, finally, the potential benefits of BIM use for the reduction of RFIs were analyzed. The research showed that the correct use of BIM applications focusing on RFIs allows for an effective information review, generating preventive or reactive management of problems. Thus, it is possible to identify the recurrent causes of RFIs and those specialties issuing the most RFIs, with the objective of mitigating RFI submission in construction projects.

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Towards digital approach for managing request for information (RFI) in construction projects: a literature review
  • Jan 9, 2024
  • Construction Innovation
  • Muneeb Afzal + 2 more

Purpose Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks. Design/methodology/approach This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge. Findings Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing. Originality/value The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

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The Yorùbá are a set of unique ethnic group with peculiar traditions, values and culture. Their cultural practice, heritage is found on the epitome of their proverbial prowess which are formed within the context of their traditional “Yorùbá language and dialect,” and used as a tool for conflict management and resolution. Conflict by Yorùbá belief is inevitable, but not insurmountable as Yorùbá found conflict always positive. To amplify this belief, Yorùbá at different occasions incorporate some of the proverbs that are relevant and applicable to conflicting situations in their attempt to resolve conflicts. This essay examines some of the selected proverbs while synthesized them as a rhetoric tools and guiding principles to constructive conflict resolution in the Yorùbá traditional Society. Suggestions are raised that application of proverbial maxims with Yorùbá perception on conflict and conflict resolution could be useful for peaceful handling of contemporary conflicts that are rampaging the African nations.

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