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1 – 10 of 243Renan Ribeiro Do Prado, Pedro Antonio Boareto, Joceir Chaves and Eduardo Alves Portela Santos
The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in…
Abstract
Purpose
The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.
Design/methodology/approach
The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.
Findings
It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.
Practical implications
Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.
Originality/value
The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.
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Mahmoud Ershadi and Fredelino Lijauco
In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and…
Abstract
Purpose
In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize factors in a framework. Thematic analysis subsequently revealed 18 selective codes under three groups of drivers, barriers, and outcomes. These three groups were explained by four key aspects including organization, stakeholders, infrastructure, and business environment that set a framework for the digitalization of construction. The study finally concluded digitalization strategies with a focus on support mechanisms, government incentives, regulations, the transition from manual labor to technicians, organizational technology culture, methodology development, and innovation processes. Such strategies provide insight into prioritizing resources towards smooth digital transformation in construction businesses.
Design/methodology/approach
A two-stage methodology is adopted by undertaking a systematic literature review followed by thematic content analysis. This work concludes with an analysis of remaining research gaps and suggestions for potential future research.
Findings
In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize variables in a framework. Thematic analysis subsequently revealed a set of variables and factors describing construction digitalization under three groups of success factors, barriers, and outcomes. A critical content analysis of the representative studies was conducted to identify five future research trends as well as associated research gaps and directions on the topic.
Practical implications
This study contributes to practice by providing directions concerning the key strategies and priorities associated with the digitalization of construction businesses.
Originality/value
This ground-breaking research brings to light a classified set of factors that are important for the digitalization of construction businesses. The elicited framework contributes to the current body of knowledge by offering a unique conceptualization of both driving and adverse aspects for the seamless digital transformation of construction.
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Elisa Gonzalez Santacruz, David Romero, Julieta Noguez and Thorsten Wuest
This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework…
Abstract
Purpose
This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”
Design/methodology/approach
The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.
Findings
This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.
Originality/value
This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.
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Gianluca Elia, Gianpaolo Ghiani, Emanuele Manni and Alessandro Margherita
This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an…
Abstract
Purpose
This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.
Design/methodology/approach
A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.
Findings
The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.
Research limitations/implications
The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.
Practical implications
The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.
Originality/value
The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.
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Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial…
Abstract
Purpose
Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.
Design/methodology/approach
The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.
Findings
Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.
Research limitations/implications
From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).
Originality/value
Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.
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Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team…
Abstract
Purpose
Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.
Design/methodology/approach
Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.
Findings
First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.
Practical implications
This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.
Originality/value
This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.
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Rida Belahouaoui and El Houssain Attak
This study aims to analyze the tax compliance behavior of family firms by integrating social and psychological norms with legitimacy determinants, focusing specifically on the…
Abstract
Purpose
This study aims to analyze the tax compliance behavior of family firms by integrating social and psychological norms with legitimacy determinants, focusing specifically on the Moroccan context.
Design/methodology/approach
Employing a qualitative research design, the study conducted semi-structured interviews with 30 chief executive officers (CEOs) of Moroccan family firms. The data were analyzed using thematic analysis to unravel the interplay between individual beliefs and societal norms.
Findings
The findings reveal a complex interplay between the personal norms of CEOs and chief financial officers (CFOs) and wider societal and cultural expectations, significantly influencing tax compliance behavior. The study identifies the multifaceted nature of tax compliance, which is shaped by personal ethics, family values and the dominant societal tax culture.
Research limitations/implications
The research is limited by its qualitative approach and focus on Moroccan family businesses, which may not be generalizable to other contexts. Future studies could use a quantitative approach and expand to other geographical settings for a more comprehensive understanding.
Practical implications
Insights from the study can assist policymakers and tax authorities in developing culturally sensitive tax compliance strategies that resonate with family business values.
Social implications
The research underscores the importance of considering sociocultural dimensions in tax compliance, fostering a more cooperative relationship between family businesses and tax authorities.
Originality/value
The study contributes a novel perspective by synthesizing social, psychological and legitimacy factors in understanding tax compliance in the unique context of family businesses.
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Alejandro Ramos-Soto, Angel Dacal-Nieto, Gonzalo Martín Alcrudo, Gabriel Mosquera and Juan José Areal
Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application…
Abstract
Purpose
Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.
Design/methodology/approach
Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.
Findings
Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.
Originality/value
This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.
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Nehemia Sugianto, Dian Tjondronegoro, Rosemary Stockdale and Elizabeth Irenne Yuwono
The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.
Abstract
Purpose
The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.
Design/methodology/approach
The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.
Findings
The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.
Originality/value
The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.
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Sihao Li, Jiali Wang and Zhao Xu
The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information…
Abstract
Purpose
The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.
Design/methodology/approach
This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.
Findings
Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.
Originality/value
This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.
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