Search results

Material selection, driven by wide and often conflicting objectives, is an important, sometimes difficult problem in material engineering. In this context, multi-criteria decision-making (MCDM) methodologies are effective. An approach of MCDM is needed to cater to criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase.

The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system. The intermediate pipes are used to join the components of the exhaust systems. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on tensile strength (TS), hardness (H), elongation (E), yield strength (YS) and cost (C). A hybrid methodology combining entropy-based criteria weighting, with the TOPSIS for alternative ranking, is pursued to identify the optimal design material for an engineered application in this paper. This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes. After that, the authors searched for and considered eight materials and evaluated them on the following five criteria: (1) TS, (2) YS, (3) H, (4) E and (5) C. The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes, on their performance and on the cost. In this structure, the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment. This essentially measures the quantity of information each criterion contribution, indicating the relative importance of these criteria better. Subsequently, the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative. The results show that SUS 309, SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.

The material matrix of the decision presented in Table 3 was normalized through Equation 5, as shown in Table 5, and the matrix was multiplied with weighting criteria ß_j. The obtained weighted normalized matrix V_ij is presented in Table 6. However, the ideal, worst and best value was ascertained by employing Equation 7. This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively. For the future work, it is suggested to select more alternatives and criteria. The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE).

The results provide important conclusions for material selection in this targeted application, verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels.

Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021.

The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches.

The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research.

Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

Heritage building management serves as a potent catalyst for sustainability, yet it poses a distinctive set of challenges. Achieving a harmonious balance between conserving the building's historical and cultural value and ensuring modern functionality and safety remains a primary concern. The present work proposes a socio-technical approach to the development and use of a digital twin (DT) that will integrate social data related to the use of heritage buildings with building and environmental data.

The paper presents a logical and systematic joined-up management framework to the targeted heritage buildings, according to a “Whole Building” approach. Our approach is informed by the underpinning assumption that a heritage building and even more a heritage neighborhood is a socio-technical, complex and dynamic system, the change of which depends on the dynamic interconnections of materials, competences, resources, values, space/environment, senses and time.

A heritage dynamics approach is adopted to unfold the dynamic nature of heritage and to better inform decisions that can be made in the present and future, achieving people-centered and place-based heritage management. This proposition underlines the heritage transformation as a complex systemic process that consists of nonlinear interconnections of multiple heterogeneous factors (values, senses, attitudes, spaces and resources).

This paper presents a multi-level framework of DTs that interact hierarchically to comprehensively understand, assimilate and seamlessly integrate intricate contexts, even when faced with conflicting conditions from diverse cultural heritage entities. This paper outlines the importance of the iterative system dynamics (SD) approach, which enables adaptive management and ensures the resilience of cultural heritage over time.

The transition to a circular economy (CE) is a known concern in the context of the textile industry, in which business actors attempt to facilitate circular activities such as textile recycling. However, a lack of established business relationships and networks creates uncertainty for textile circulation. In such business environments, managerial decisions regarding CE may depend not only on normative behaviour but also on heuristics that guide their choices. Since business relationships for textile circularity require interactions between business actors, this study explores how managerial heuristics are shaped in the CE transition within the textile industry and their impact on actors’ interactions within business relationships and networks.

Empirically, this qualitative study is based on interviews with managers representing companies and organisations engaged in business relationships and networks aimed at a CE transition in the textile industry, as well as on publicly available secondary data.

The findings indicate that managerial decisions promoting circularity can be influenced by, besides normative information assessment, factors predominant in (1) the business and regulatory environment, (2) managers’ experience and knowledge obtained during interactions within business networks and (3) the internal strategic approaches of business organisations. This study identifies adaptation, experience, interaction and strategy heuristics that may be utilised by managers in making decisions in the context of uncertainty, such as the industrial transition to a CE.

This study expands the knowledge of heuristics applied to managerial decision making in interacting business firms and institutional organisations aiming to facilitate textile recycling and proposes a heuristics toolbox. The study provides an insight into business actors’ interactions, as well as various factors inside and outside the organisations shaping the managerial decisions. By doing this, the study adds to the literature, highlighting the importance of contextualisation and the interrelation between the individual and business environment levels in business-to-business management.

This study aims to address challenges related to long lead time within a hazelnut company, primarily attributed to product quality issues. The purpose is to propose an integrated lean-based methodology incorporating a continuous improvement cycle, drawing on Lean Six Sigma (LSS) and Industry 4.0 applications.

The research adopts a systematic approach, commencing with a current state analysis using VSM and fishbone analysis to identify underlying problems causing long lead time. A Pareto analysis categorizes these problems, distinguishing between supplier-related issues and deficiencies in lean applications. Lean tools are initially implemented, followed by a future state VSM. Supplier-related issues are then addressed, employing root cause analyses and Industry 4.0-based countermeasures, including a proposed supplier selection model.

The study reveals that, despite initial lean implementations, lead times remain high. Addressing supplier-related issues, particularly through the proposed supplier selection model, significantly reduces the number of suppliers and contributes to lead time reduction. Industry 4.0-based countermeasures ensure traceability and strengthen supplier relationships.

This research introduces a comprehensive LSS methodology, practically demonstrating the application of various tools and providing managerial insights for practitioners and policymakers. The study contributes theoretically by addressing challenges comprehensively, practically by showcasing tool applications and managerially by offering guidance for system performance enhancement.

This study aims to present state-of-the-art research on circular economy (CE) implementation in construction and demolition waste management (CDWM) within the construction sector.

A mixed-method (scientometric and critical analysis) review strategy was adopted, involving scientometric and critical analysis to uncover the evolutionary progress within the research area, investigate key research themes in the field, and explore ten issues of CE in CDWM. Moreover, avenues for future research are provided for researchers, practitioners, decision-makers, and planners to bring innovative and new knowledge to this field.

A total of 212 articles were analyzed, and scientometric analysis was performed. The critical analysis findings reveal extensive use of surveys, interviews, case studies, or mixed-method approaches as study methodologies. Furthermore, there is limited focus on the application of modern technologies, modeling approaches, decision support systems, and monitoring and traceability tools of CE in the CDWM field. Additionally, no structured framework to implement CE in CDWM areas has been found, as existing frameworks are based on traditional linear models. Moreover, none of the studies discuss readiness factors, knowledge management systems, performance measurement systems, and life cycle assessment indicators.

The outcomes of this study can be utilized by construction and demolition sector managers, researchers, practitioners, decision-makers, and policymakers to comprehend the state-of-the-art, explore current research topics, and gain detailed insights into future research areas. Additionally, the study offers suggestions on addressing these areas effectively.

This study employs a universal approach to provide the current research progress and holistic knowledge about various important issues of CE in CDWM, offering opportunities for future research directions in the area.

Starting from the relevance of ethics to the application of artificial intelligence (AI) in the context of employee recruitment and selection (R&S), in this article, we aim to provide a comprehensive review of the literature in light of the main ethical theories (utilitarian theories, theories of justice, and theories of rights) to identify a future research agenda and practical implications.

On the basis of the best-quality and most influential journals, we conducted a systematic review of 120 articles from two databases (Web of Science and Scopus) to provide descriptive results and adopt a framework for deductive classification of the main topics.

Inspired by the three ethical theories, we identified three thematic lines of enquiry for the debate on AI in R&S: (1) the utilitarian view: the efficient optimisation of R&S through AI; (2) the justice view: the perceptions of justice and fairness related to AI techniques; and (3) the rights view: the respect for legal and human rights requirements when AI is applied.

This article provides a detailed assessment of the adoption of AI in the R&S process from the standpoint of traditional ethics theories and offers an integrative theoretical framework for future research on AI in the broader field of HRM.

This paper aims to examine how different contextual contingency factors and organizational goals influence construction clients’ decision-making when procuring contractors in the housing sector. More specifically, it investigates how clients’ choice of procurement strategies and organizational control systems is contingent upon various contextual factors and organizational goals.

It is based on an explorative interview study of clients and contractors in the Swedish housing sector underpinned by a review of organizational control literature.

The client's knowledge and resources, as well as project complexity and uncertainty, are the most important contextual contingency factors, while property management and sustainable development are the most important organizational goals that housing clients consider when designing procurement strategies.

The paper contributes to the understanding of how construction clients choose procurement strategies, by providing new insights into effects of the mentioned contextual contingency factors and organizational goals on clients’ choice of control systems through their procurement strategies.

Property owners who continuously procure housing projects with sustainability requirements and high degrees of complexity and uncertainty should develop knowledge and resources related to their client role, to enable the design and implementation of appropriate procurement strategies.

Novel aspects of the paper are the demonstration of the value of a holistic approach, considering both contextual contingency factors and organizational goals, when selecting control systems and explicit discussion of how the client's knowledge and resources influence possibilities to implement different control systems.

The purpose of this paper is to explore companies’ business risks and challenges across macro- and micro-environments, as well as how small and medium-sized enterprises (SMEs) can benefit from digital technologies, including artificial intelligence (AI), as part their risk-management (RM) strategies in the face of recent disruptive events.

We perform a literature review on risk management and business continuity (BC) in the context of SMEs, both in general and specifically in the manufacturing sector.

The critical importance of RM and BC for SMEs is highlighted. The review underscores the significant impact of recent disruptions on SMEs and reveals a range of risk factors affecting their BC. Moreover, the review recognises how SMEs, in general, and manufacturing SMEs, in particular, can benefit from using digital technologies and AI as essential components of their RM.

The review highlights transformative role of digital technologies and AI in enhancing RM. Through a systematic classification of risk factors within macro- and micro-environments, this novel approach provides a structured foundation for future research. It provides practical value by enabling SMEs to integrate dynamic capabilities and adaptive capacities through the adaption of digital technologies and AI into their RM.

This study aims to determine and investigate the main causes of construction project delays. Construction projects are more intricate and associated with significant levels of risk owing to cost overruns. These overruns frequently lead to delays, incomplete work or other related challenges. Building delays are a prevalent problem in the building sector of developing nations. These delays prolong the duration of projects and result in increased costs and conflicts among stakeholders. A conceptual model consisting of the factors causing the delays in heating, ventilation and air conditioning (HVAC) projects was developed and tested in this study.

A comprehensive data collection process was undertaken. A meticulously designed survey was distributed to a diverse cohort of 294 participants, including contractors and sub-contractors from Chennai, Tamil Nadu. The data was collected using stratified sampling, ensuring a representative sample. The data was then analysed using ordinary least squares multiple regression.

The findings of this study have significant implications for the construction industry. They indicate that factors related to sales, clients, design, procurement, finance and labour all contribute to delays in HVAC projects. Understanding these factors can help stakeholders in the industry to better manage and mitigate project delays.

This study is unique because it is a perceptual study of stakeholders. It provides valuable information for analysing and assessing project performance by identifying the primary causes of HVAC project delays. To the best of the authors’ knowledge, the study conducted on HVAC projects is the first of its kind and hence makes a pivotal contribution to the literature on construction projects. Additionally, the study will assist policymakers and consultants in taking necessary steps to minimize delays.