818 research outputs found

    The analysis of critical success factors for successful kaizen implementation during the COVID-19 pandemic: a textile industry case study

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    Purpose The primary objective of this research is to determine critical success factors (CSFs) that enable textile enterprises to effectively implement Kaizen, a Japanese concept of continuous development, particularly during disruptive situations. The study aims to provide insights into how Kaizen is specifically employed within the textile sector and to offer guidance for addressing future crises. Design/methodology/approach This study employs a structured approach to determine CSFs for successful Kaizen implementation in the textile industry. The Triple Helix Actors structure, comprising business, academia and government representatives, is utilized to uncover essential insights. Additionally, the Matriced Impacts Croises-Multiplication Applique and Classement (MICMAC) analysis and interpretative structural modeling (ISM) techniques are applied to evaluate the influence of CSFs. Findings The research identifies 17 CSFs for successful Kaizen implementation in the textile industry through a comprehensive literature review and expert input. These factors are organized into a hierarchical structure with 5 distinct levels. Additionally, the application of the MICMAC analysis reveals three clusters of CSFs: linkage, dependent and independent, highlighting their interdependencies and impact. Originality/value Major contribution of this study is understanding how Kaizen can be effectively utilized in the textile industry, especially during disruptive events. The combination of the Triple Helix Actors structure, MICMAC analysis and ISM provides a unique perspective on the essential factors driving successful Kaizen implementation. The identification of CSFs and their categorization into clusters offer valuable insights for practitioners, policymakers and academia seeking to enhance the resilience and sustainability of the textile industry

    GPT models in construction industry: Opportunities, limitations, and a use case validation

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    Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    Closing the gap: The role of distributed manufacturing systems for overcoming the barriers to manufacturing sustainability

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    The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact critical barriers to sustainability is considerably unknown. To help close this gap, this article proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the best–worst method is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that “organizational barriers” are the most severe barriers and indicate that “reduced carbon emissions” has the highest impact on “organizational” and “sociocultural barriers” whereas public approval” has the highest impact on “organizational barriers.” The results infer that “reduction of carbon emission” is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers, and thus, improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance

    Exploring the determinants of digital transformation in its different stages in Dutch SMEs: A digital dynamic capabilities perspective

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    Digital transformation (DT) has become a crucial strategic imperative for organizations seeking to thrive in the rapidly evolving business environment. While digital transformation has been extensively studied in large organizations, there remains a need for more available evidence in the context of (Dutch) SMEs and how organizations go through different DT phases. This dissertation aims to address this gap by adopting a digital dynamic capabilities perspective to explore the determinants of digital transformation in Dutch SMEs and investigate how these determinants change over the different digital transformation phases. This thesis has met these aims by integrating an extensive review of the relevant literature and implementing a qualitative study. The latter includes nine interviews with experts from different Dutch SMEs and an expert panel to validate these findings. The primary conclusions produced by this study include five internal determinants, five external determinants, three sub-capabilities, each of the sensing, seizing, transforming, and safeguarding digital dynamic capability clusters, and five desired digital transformation outcomes. ‘Digital safeguarding’ has emerged as a novel capability cluster focusing on skills required from the implementation onwards. In conclusion, this study has contributed to a deeper understanding of the differences in the digital transformation determinants and capabilities between large organizations and SMEs. Moreover, this thesis has identified that boundaries between the different digital transformation phases could be fading due to the continuity of digital transformation. Simultaneously, this research has practical relevance as these findings could support Dutch SMEs in navigating their digital transformations. Alternatively, the study could help Joanknecht, a Dutch financial advisory firm, improve its consultancy services. Looking ahead, future researchers should seek to validate and expand upon the presented findings.

    Sustainable and Resilient Supplier Selection in the Context of Circular Economy: An Ontology-Based Model

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    Purpose: Selecting the optimal supplier is a challenging managerial decision that involves several dimensions that vary over time. Despite the considerable attention devoted to this issue, knowledge is required to be updated and analyzed in this field. This paper reveals new opportunities to advance supplier selection research from a multidimensional perspective. Moreover, this study aims to formalise supplier selection knowledge to enable the appropriate selection of sustainable, resilient and circular criteria. Design/methodology/approach: This study is developed in two stages. First, a systematic literature review is conducted to select relevant papers. Descriptive and thematic analyses are employed to analyze criteria, solving approaches and case studies. Second, a criterion knowledge-based framework is developed and validated by experts to be implemented as ontology using Protégé software. Findings: (1) Evaluating the viability of suppliers need further studies to integrate other criteria and to align supplier selection objectives with research advancement; (2) Artificial intelligence tools are needed to revolutionize and optimize the traditional techniques used to solve this problem; (3) Literature lucks frameworks for specific sectors; (4) The proposed ontology provides a consistent criteria knowledge base. Practical Implications: For academics, the results of this study highlight opportunities to improve the viable supplier selection process. From a managerial perspective, the proposed ontology can assist managers in selecting the appropriate criteria. Future works can enrich the proposed ontology and integrate this knowledge base into an information system. Originality/value: This study contributes to promoting knowledge about viable supplier selection. Capitalizing the knowledge base of criteria in a computer-interpretable manner supports the digitalization of this critical decision

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Assessing supply chain innovations for building resilient food supply chains: an emerging economy perspective

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    Food waste reduction and security are the main concerns of agri-food supply chains, as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges, including resource scarcity, climate change, waste generation, etc., need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges, barriers, enablers, etc. Still, there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus, studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that ‘Business strategy innovations’ are the most significant innovations that can bring resiliency to the food supply chains, followed by ‘Technological innovations.’ The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run

    Multicriteria sorting method based on global and local search for supplier segmentation

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    The aim of this research is to develop a robust multicriteria method to classify suppliers into ordered categories and its validation in real contexts. The proposed technique is based on a property of net flows of the PROMETHEE method and uses global and local search concepts, which are common in the optimisation field. The results obtained are compared to those from the most cited sorting algorithm, and an empirical validation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does not require additional information from decision-makers as other sorting algorithms do for assigning incomparable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is also contributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate into decision support systems for automated decisions in the supply chain management. Finally, this practical approach is also useful to classify customers and any type of alternatives or actions into ordered categories, which have an increasing number of real applications
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