scholarly journals Lean Based Maturity Framework Integrating Value, BIM and Big Data Analytics: Evidence from AEC Industry

2021 ◽  
Vol 13 (18) ◽  
pp. 10029
Author(s):  
Gökhan Demirdöğen ◽  
Nihan Sena Diren ◽  
Hande Aladağ ◽  
Zeynep Işık
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Negative Impact ◽  
Design Science ◽  
Big Data Analytics ◽  
Value Added ◽  
Science Research ◽  
Research Problem ◽  
Design Cycle ◽  
Aec Industry

The construction industry is considered as one of the least productive, highest energy consuming, and least digitized industries. The Lean Management (LM) philosophy became a significant way for eliminating non-value-added activities and wastes during a building’s lifecycle. However, studies have shown that philosophies are not efficient by themselves to solve the issues of the construction industry. They need to be supported with the appropriate technologies and tools. Therefore, the integrated use of Building Information Modelling (BIM) with LM or Value Engineering (VE) were proposed in the literature. Nonetheless, it was also seen that BIM can provide more insights and improvements when BIM is integrated with data analysis tools to analyze BIM data. In the literature, the synergies between these concepts are generally addressed pairwise, and there is no comprehensive framework which identifies their relationships. Therefore, this study aims to develop a maturity framework that facilitates the adoption of LM, VE, BIM, and Big Data Analytic (BDA) concepts to address long-standing productivity and digitalization issues in the Architecture, Engineering, and Construction (AEC) industry. Design Science Research (DSR) methodology and its three-cycle view (relevance, rigor, and design cycle) were applied to build the proposed maturity framework. Two interviews were performed to identify and observe research problem in relevance cycle. In the rigor cycle, a comprehensive literature review was performed to create a base for the development of the maturity framework. In addition to the developed base of the framework, lean processes were added to this cycle. In the design cycle, the developed framework was evaluated and validated by five experts through face-to-face interviews. The importance of employer’s requirements to adopt the proposed methodologies, the negative impact of change orders, the importance of pre-construction phases to facilitate value creation and waste elimination, and the usage of common data environment with BIM were identified as the prominent application and adaptation issues.

scholarly journals Design Science Research: Evaluation in the Lens of Big Data Analytics

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 27 ◽  
Author(s):  
Ahmed Elragal ◽  
Moutaz Haddara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Research ◽  
Research Evaluation ◽  
Design Science ◽  
Big Data Analytics ◽  
Science Research ◽  
Design Science Research ◽  
Evaluation Phase ◽  
New Type

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals Smile through the Mask: Emotion Measurement for Stationary Retail

2021 ◽  
Author(s):  
Michael Meyer ◽  
◽  
Susanne Robra-Bissantz ◽  
Keyword(s):  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Customer Behavior ◽  
Self Assessment ◽  
Safety Measures ◽  
Strong Change ◽  
Global Pandemic ◽  
Design Cycle ◽  
Stationary Retail

The global pandemic caused by the coronavirus disease (COVID-19) changes the lives of many people all over the world. In the context of stationary retail, a strong change of customer behavior occurs as mandatory safety measures like wearing facemasks and distance regulations have come into place. The sales personnel’s ability to understand and react to customers’ emotions is critical for service interactions and the customers’ overall satisfaction. Unfortunately, facemasks make it difficult to recognize other’s emotions and may lead to misinterpretation and confusion. To address this problem, this paper proposes the design of self-assessment interfaces that offer the customer an easy way to enter their emotions. As part of a Design Science Research (DSR) project, we designed three interfaces and evaluated them over the course of a design cycle. The results indicate that it is possible to use self-assessment technology in stationary retail to measure customer emotions.

A BIM–LCA integration technique to embodied carbon estimation applied on wall systems in Brazil

2018 ◽  
Vol 8 (5) ◽  
pp. 491-503 ◽  
Author(s):  
Julianna Crippa ◽  
Letícia Cavassin Boeing ◽  
Ana Paula Angonese Caparelli ◽  
Marienne do Rocio de Mello Maron da Costa ◽  
Sergio Scheer ◽  
...  
Keyword(s):  
Design Science ◽  
New Ventures ◽  
Science Research ◽  
Information Modeling ◽  
Content Type ◽  
Embodied Carbon ◽  
Building Information ◽  
Feasible Option ◽  
Wood Frame ◽  
Aec Industry

Purpose Aiming to simplify the extraction of embodied carbon data using a building information modeling (BIM) software, the purpose of this paper is to present a framework that integrates BIM and life cycle assessment (LCA), which are useful to the architecture, engineer and construction (AEC) industry. As a further purpose, this study also tests four different wall systems. Design/methodology/approach The study applies design science research and it presents a framework that integrates BIM and LCA. For analysis and validation, a case study features four different wall systems costs based on the Brazilian context. In the proposed framework, SimaPro8 accomplishes the LCA, while ArchiCAD 19 the modeling. Findings The first analysis covers embodied carbon and the second covers the total cost of each m² of wall. The proposed framework performs well, and it is effective in the Brazilian context. Concerning the walls, the wood frame system is the most sustainable option within this analysis and the most financially feasible option in Brazil. Originality/value The present study contributes to embodied carbon data analysis, ensuring that the best choice of elements and components is being used in the building project. This BIM–LCA integrated solution is valuable not only to the AEC industry and to professionals, but also to future researchers. This analysis is of great value to new ventures, since the society shows a great concern about reducing GHGs emissions.

scholarly journals Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

2020 ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

Abstract Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals Machine Learning Using Big Data Link Stability Based Node Observation for IoT Security

2021 ◽  
Author(s):  
R. Ganesh Babu ◽  
S. Yuvaraj ◽  
A. VedanthSrivatson ◽  
T. Ramachandran ◽  
G. Vikram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Big Data Analytics ◽  
Research Problem ◽  
Research Literature ◽  
Handheld Devices ◽  
Data Link ◽  
Iot Security ◽  
Primary Driver

IoT systems create a multi-hop organizational structure among mobile devices in required to send on data groups. The remarkable properties of gadgets frameworks cause communications to interconnect among competing handheld devices. Most physiological directing displays don’t believe secure associations all through bundle communication to organize high communicate ability and genetic blocks that also prompts increased delay as well as bundle decreasing in mastermind. Only with continued growth and transformation of IoT networks, attacks on such IoT systems are increasing at an alarming rate. Our purpose will provide researchers with a research resource on latest research patterns in IoT security. As the primary driver of with us research problem concerning IoT security as well as machine learning. This analysis of the literature among the most research literature in IoT security recognized some very key current research which will generate organizational investigations. Only with fast emergence of different IoT threats, it is essential to develop frameworks that could integrate cutting-edge big data analytics and machine learning advanced technologies. Effectiveness are critical quality variables in shaping the best methods and algorithms for detecting IoT threats in real-time or close to real time.

scholarly journals Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

2019 ◽  
Vol 12 (1) ◽  
pp. 277 ◽  
Author(s):  
Vinicius Luiz Ferraz Minatogawa ◽  
Matheus Munhoz Vieira Franco ◽  
Izabela Simon Rampasso ◽  
Rosley Anholon ◽  
Ruy Quadros ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Business Model ◽  
Small Businesses ◽  
Data Analytics ◽  
Design Science ◽  
Big Data Analytics ◽  
Business Model Innovation ◽  
Raw Material ◽  
Animal Origin

Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.

scholarly journals Deploying Geometric Dimensioning and Tolerancing in Construction

Buildings ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 62 ◽  
Author(s):  
Saeed Talebi ◽  
Lauri Koskela ◽  
Patricia Tzortzopoulos ◽  
Michail Kagioglou ◽  
Alex Krulikowski
Keyword(s):  
Construction Projects ◽  
Design Science ◽  
Methodological Approach ◽  
Science Research ◽  
Symbolic Language ◽  
Geometric Dimensioning And Tolerancing ◽  
Construction Design ◽  
Dimensioning And Tolerancing ◽  
Industry Standards ◽  
Aec Industry

No standardised approach appears to exist in the architecture, engineering, and construction (AEC) industry for the communication of tolerance information on drawings. As a result of this shortcoming, defects associated with dimensional and geometric variability occur with potentially severe consequences. In contrast, in mechanical engineering, geometric dimensioning and tolerancing (GD&T) is a symbolic language widely used to communicate both the perfect geometry and the tolerances of components and assemblies. This paper prescribes the application of GD&T in construction with the goal of developing a common language called geometric dimensioning and tolerancing in construction (GD&TIC) to facilitate the communication of tolerance information throughout design and construction. design science research is the adopted methodological approach. Evidence was collated from direct observations in two construction projects and two group interviews. A focus group meeting was conducted to evaluate whether the developed solution (GD&TIC) fulfilled its aim. The contribution of this paper to designers, to organisations involved in developing AEC industry standards, and to the scholarly community is twofold: (1) It is an attempt to develop a standardised approach (GD&TIC) for the communication of tolerance information in AEC, and (2) it identifies discrepancies between GD&TIC rules and some of the commonly used American and British standards on tolerances.

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