Critical Success Factors CSFs to Automation and Robotics in Industrialized Building System IBS

Rashid, Mohd Najib Abd; Abdullah, Mohd Rofdzi; Ismail, Dzulkarnaen

doi:10.6007/ijarbss/v8-i12/5432

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…By automating these procedures, businesses may increase reaction times and lessen the danger of bodily harm or property damage [26]. RPA may be used to evaluate safety data and identify possible risks or improvement opportunities [27,28]. Businesses may swiftly detect and fix safety concerns by assessing data in real-time, minimizing the likelihood of accidents and injuries.…”

Section: Robotic Process Automation Implementationmentioning

confidence: 99%

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Assessment of Barriers to Robotics Process Automation (RPA) Implementation in Safety Management of Tall Buildings

et al. 2023

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Construction is dangerous, making safety management essential. Robotics process automation (RPA) can improve construction project risk management. RPA is hindered by several factors. This study examined the primary technical, economic, legal, privacy, and resource obstacles to RPA adoption for tall building safety management. The pilot survey comprised 161 Malaysian tall building specialists, while the full questionnaire poll included 231 experts. EFA and SEM analyzed the data. Technology, economics, legislation, privacy, and resources prevented RPA from managing tall building safety. Theoretical and empirical breakthroughs in construction safety management and RPA deployment prompted this inquiry. This study illuminates the main obstacles to employing RPA for tall building safety management. The results show where to spend time and money to eliminate the obstacles. The study’s management implications may benefit construction safety managers, project managers, and company owners. The findings may help the building industry plan RPA safety management in tall projects and overcome hurdles. This study contributes to construction safety management and RPA deployment theory by identifying and analyzing the main barriers to using RPA for safety management in high-rise buildings. This research can help solve the problems preventing RPA from being used in construction project safety management.

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Section: Robotic Process Automation Implementationmentioning

confidence: 99%

“…[29,30] 5 Technology that does not conform to the already accepted safety standards in the construction business. [27,28] 6…”

Section: Identification Of Challengesmentioning

confidence: 99%

Assessment of Barriers to Robotics Process Automation (RPA) Implementation in Safety Management of Tall Buildings

et al. 2023

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“…This research strives to collect the critical factors that influence the successful implementation of automation and robotics through a literature review. The majority of the research found was still focused on discussing the challenges and obstacles during the implementation of CAR (F. Bademosi & Issa, 2021;Cai Shiyao et al, 2020;Davila Delgado et al, 2019;Pradhananga et al, 2021;Seyrfar et al, 2022) or the success factors for a specific type of CAR (O'Connor et al, 2014;Rashid et al, 2019;Wuni & Shen, 2020). Only research conducted by Cai (Cai Shiyao et al, 2020) discussed the success factors of CAR implementation in China and classified them into three main groups: technology, organization, and environment (Table III).…”

Section: Critical Success Factors (Csfs) Of Car Implementationmentioning

confidence: 99%

Implementation of Construction Automation and Robotics (CAR) in Indonesian Construction State-Owned Enterprises: Position in Project Life Cycle, Gap to Best Practice and Potential Uses

Tanne

Indrayani²

2023

Preprint

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Construction Automation and Robotics (CAR) is the anticipated technology to overcome productivity issues and work accidents, especially in developing countries. This research analyzes the implementation of CAR at Indonesian Construction SOEs from the perspective of real project implementation based on PLC, comparison with best practices, and potential uses, through the qualitative analysis of the literature as well as questionnaires and in-depth interviews. The results are compared qualitatively to obtain answers to the research objectives. This research identifies that seven Indonesian Construction SOEs have been using CAR technology, where Unmanned Aerial Systems (Drones), Virtual Reality (VR), and Prefabrication and Modularization are the most popular and mostly implemented in the construction phase. Compared to best practice, CAR implementation is still in the adoption stage. It has not reached its full potential through the development stage due to insufficient collaboration between contractors, technology companies, and universities. Although the construction market in Indonesia has not directly demanded CAR application, this technology is potentially considered to improve the completion of construction projects, especially the interaction prospect of Digital Twin (DT) and BIM. This study provides complete positional information on CARs type in the PLC in theory and practice as a benchmark for CAR research. This study fills the research gap and pioneering Indonesia’s CAR research in international journals and provides the latest and comprehensive overview of CAR implementation in Indonesia compared to best practices (USA and China).

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“…This is together with power supply, professional bodies support and project documentation with one source each. Influencing Factors Source 3D detailing [13], [15], [20], [28] Clash Detection [13], [20], [28] Collaboration/communication [15] , [19], [22], [24], [25], [27], [28] Comparability [14], [17], [18], [23], [30] Complexity [14], [17], [18] Cost of adopting AI [14], [15], [16], [18], [19], [20], [21], [22], [23], [24], [25], [26], [28], [29], [31] Costing and estimating [13], [28] Define project roles and responsibilities [13], [28] Documentation [13], [28] Facilities Management [13], [28] Fragmented industry [15] Goal Setting [13],…”

Section: Factors Influencing the Adoption Of Ai In The Construction I...mentioning

confidence: 99%

A Systematic Literature Review of Influencing Factors and Strategies of Artificial Intelligence Adoption in the Construction Industry

Tjebane

Musonda

Okoro

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The construction industry has seen an increase in Artificial Intelligence(AI) in recent years, a paradigm shift in many industries. It puts under pressure for technological advancement. Therefore, AI is under great attention in the construction industry as a new strategic paver. This paper adopts a systematic literature review (SLR) approach and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to observe and understand the influencing factors and strategies for AI adoption. The SLR for AI-related research carried out between 2010 and 2020. Data was collected from ASCE Journals, Emerald Insight, Elsevier ScienceDirect, Engineering Village, Google Scholar, ICE virtual library, IOPscience, IEEE Xplore, ScienceDirect, Scopus, SpringerLink and Taylor & Francis. The paper identifies and classifies the new developments in AI research, making its implementation and adoption a reality in the construction industry. This review has the potential for construction industry stakeholders, especially those in developing countries, to utilise the accumulated evidence from selected systematic reviews to enable the usage of AI for infrastructure development

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Critical Success Factors CSFs to Automation and Robotics in Industrialized Building System IBS

Cited by 8 publications

References 20 publications

Assessment of Barriers to Robotics Process Automation (RPA) Implementation in Safety Management of Tall Buildings

Assessment of Barriers to Robotics Process Automation (RPA) Implementation in Safety Management of Tall Buildings

Implementation of Construction Automation and Robotics (CAR) in Indonesian Construction State-Owned Enterprises: Position in Project Life Cycle, Gap to Best Practice and Potential Uses

A Systematic Literature Review of Influencing Factors and Strategies of Artificial Intelligence Adoption in the Construction Industry

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