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1 – 10 of 80R. Dhanalakshmi, Monica Benjamin, Arunkumar Sivaraman, Kiran Sood and S. S. Sreedeep
Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing…
Abstract
Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.
Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.
Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.
Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.
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R. Dhanalakshmi, Dwaraka Mai Cherukuri, Akash Ambashankar, Arunkumar Sivaraman and Kiran Sood
Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart…
Abstract
Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.
Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.
Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.
Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.
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Haitao Wu, Wenyan Zhong, Botao Zhong, Heng Li, Jiadong Guo and Imran Mehmood
Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a…
Abstract
Purpose
Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.
Design/methodology/approach
An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.
Findings
The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.
Practical implications
This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.
Originality/value
Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.
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Rinu Sathyan, Parthiban Palanisamy, Suresh G. and Navin M.
The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the…
Abstract
Purpose
The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.
Design/methodology/approach
Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.
Findings
The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.
Research limitations/implications
This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.
Originality/value
The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.
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Sudhanshu Joshi, Manu Sharma, Sunil Luthra, Jose Arturo Garza-Reyes and Ramesh Anbanandam
The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.
Abstract
Purpose
The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.
Design/methodology/approach
The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.
Findings
The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).
Research limitations/implications
This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.
Originality/value
This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.
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This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era…
Abstract
Purpose
This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.
Design/methodology/approach
A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.
Findings
This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.
Practical implications
The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.
Originality/value
This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.
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Iván Manuel De la Vega Hernández and Juan Jesús Diaz Amorin
The multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This…
Abstract
Purpose
The multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This study aims to analyse the scientific production in the Web of Science (WoS) on the relationship between smart cities and the eight urban dimensions defined by the World Economic Forum (WEF) in the period 1990 to 2021, in order to establish which countries lead the knowledge related to the search for sustainable living conditions for people and how this knowledge contributes to improving stakeholders' decision-making.
Design/methodology/approach
The methodological steps followed in the study were: (1) Identification and selection of keywords. (2) Design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using WoS terms to contrast them. (3) Data processing was performed from Journal Citation Report (JCR) journals during the year 2022.
Findings
This study identified the authors, institutions and countries that publish the most globally on the topic of Smart Cities. The acceleration in the integration of new technologies and their impact on population conglomerates and their relationship with urban dimensions were also analysed. The evidence found indicates that the USA and China are leading in this field.
Originality/value
This bibliometric study was designed to analyse a knowledge space not addressed in the scientific literature referred to the relationship between the concept of smart cities and the urban dimensions established by the WEF, the identification of new technologies that are converging to promote developments of new ways of managing urban dimensions and propose new knowledge spaces.
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The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector…
Abstract
Purpose
The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina.
Design/methodology/approach
Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test.
Findings
Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables.
Originality/value
To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.
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