| 1. |
- Fathi, Masood, et al.
(författare)
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Balancing assembly lines with industrial and collaborative robots : Current trends and future research directions
- 2024
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 193
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Forskningsöversikt (refereegranskat)abstract
- Assembly-line balancing is a significant issue in production systems. Employing industrial robots as the main production resource was a milestone in developing assembly lines, and emerging Industry 4.0 led industries to build collaborative assembly lines by combining robots and human operator skills. Recently, the majority of research on assembly line balancing has contributed to addressing aspects of utilizing robots in assembly lines and how they can increase line performance. Various models and methods are developed, considering different objectives and performance indicators. Despite the increasing number of studies in this area, a thorough literature review is lacking in identifying gaps, shedding light on research directions, and facilitating future development. This study systematically reviews assembly-line balancing studies targeted at assembly lines with industrial and collaborative robots. Studies are classified based on their objectives and reviewed for their solution method, line layout, and other essential specifications. A descriptive analysis is provided to assist researchers and practitioners in linking different properties of assembly lines to the objectives and applied methodologies. The results show that most studies developed models and solution methods that focused on simultaneously optimizing more than one objective. The review reveals that minimizing the cycle time is the most popular objective, and meta-heuristic algorithms are the dominant solution approaches. It is also observed that balancing assembly lines with collaborative robots has received more attention in the last five years with the emergence of Industry 4.0. The review also highlights gaps in the related literature and provides promising insights for future research.
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| 2. |
- Foroughi, Behzad, et al.
(författare)
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Determinants of continuance intention to use food delivery apps : findings from PLS and fsQCA
- 2024
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Ingår i: International Journal of Contemporary Hospitality Management. - : Emerald Group Publishing Limited. - 0959-6119 .- 1757-1049. ; 36:4, s. 1235-1261
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Tidskriftsartikel (refereegranskat)abstract
- PurposeCustomers increasingly use food delivery applications (FDAs) to place orders. Despite the popularity of FDAs, limited research has investigated the drivers of the continuance intention to use FDAs. This study aims to uncover the drivers of the continuance intention to use FDAs by integrating the “technology continuance theory” (TCT) with perceived task-technology fit, perceived value and perceived food safety.Design/methodology/approachData were collected from 398 individuals in Thailand and evaluated using “partial least squares” (PLS) and “fuzzy-set qualitative comparative analysis” (fsQCA).FindingsThe PLS results supported the significance of all direct relationships, except the effects of perceived ease of use on attitude and perceived usefulness on continuance intention. Accordingly, perceived food safety positively moderated the impact of perceived ease of use on attitudes. The fsQCA uncovered seven solutions with various combinations of factors that predicted high continuance intention.Practical implicationsThis study enables food delivery apps to develop effective strategies for retaining users and sustaining financial performance.Originality/valueThis research contributes to the literature by investigating the factors underlying the continuous use of FDAs with a new PLS-fsQCA technique and applying TCT in a new technological context, FDAs and enriching it by adding three variables: perceived task-technology fit, perceived value and perceived food safety.
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| 3. |
- Foroughi, Behzad, et al.
(författare)
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Determinants of followers' purchase intentions toward brands endorsed by social media influencers : Findings from PLS and fsQCA
- 2024
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Ingår i: Journal of Consumer Behaviour. - : John Wiley & Sons. - 1472-0817 .- 1479-1838. ; 23:2, s. 888-914
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Tidskriftsartikel (refereegranskat)abstract
- Given the rise of marketing through social media influencers (SMIs), this study aimed to investigate the influences of source credibility and content validity factors on followers' attitudes and intention to purchase brands endorsed by SMIs through brand engagement and brand expected value. The study assessed the moderating effect of product-influencer congruency. Data were collected from 429 individuals in Iran and were assessed using the "partial least squares" (PLS) and "fuzzy-set qualitative comparative analysis" (fsQCA) approaches. PLS results affirmed the significance of all direct relationships except the role of entertainment value on brand engagement and informativeness value on brand expected value. Product-influencer congruence positively moderated the effects of trustworthiness and expertise on brand expected value. fsQCA identified five solutions with various combinations of elements that lead to high purchase intention. The results of performing fsQCA revealed that three factors, namely attractiveness, entertainment value, and attitude, are necessary to achieve high purchase intention. fsQCA results challenged the PLS findings, indicating the importance of using an asymmetric approach in conjunction with PLS. Our study provides valuable insight for marketing influencers and practitioners on the importance of source credibility and content value factors in enhancing the effectiveness of SMI marketing activities and persuading customers to purchase endorsed brands.
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| 4. |
- Ghobakhloo, Morteza, et al.
(författare)
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Beyond Industry 4.0 : a systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability
- 2024
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Ingår i: Asia-Pacific Journal of Business Administration. - : Emerald Group Publishing Limited. - 1757-4323 .- 1757-4331.
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Forskningsöversikt (refereegranskat)abstract
- Purpose: The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda. Design/methodology/approach: The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives. Findings: The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies. Practical implications: Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications. Originality/value: This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.
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| 5. |
- Ghobakhloo, Morteza, et al.
(författare)
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Blockchain technology as an enabler for sustainable business ecosystems : A comprehensive roadmap for socioenvironmental and economic sustainability
- 2024
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Ingår i: Business strategy and development. - : John Wiley & Sons. - 2572-3170. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Blockchain technology is a core technology expected to play a highly instrumental role in competing with socioenvironmental challenges. The literature hypothesizes various blockchain functions for building a sustainable business ecosystem. This study unifies these diverse perspectives into an interpretive strategy roadmap that provides a holistic overview of how blockchain should be leveraged to deliver sustainability functions optimally. The study first identified the sustainability functions of blockchain through a content-centric literature review. The study applied interpretive structural modeling (ISM) and drew on experts' opinions to model how and in which order blockchain delivers these sustainability functions. The study further drew on the ISM output and interpretive logic-knowledge base to develop the promised roadmap. Results revealed that blockchain promotes a decentralized decision system that facilitates automation and real-time information sharing (RIS) across supply chains. Blockchain introduces traceability and transparency into supply chain operations. These conditions offer monitoring of business operations and the development of trust across value-chain stakeholders. These driver functions lead to value chain optimization and circularity integration into business and supply chain operations. When these necessary functional conditions are met, businesses can further draw on blockchain to promote economic and environmental aspects of sustainability through more complex functions enabling resource efficiency, cost reduction, pollution prevention, and higher profit margins. The order in which businesses can leverage these functions would define blockchain sustainability performance. Each function is uniquely valuable to sustainability, and none of them can be overlooked.
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| 6. |
- Ghobakhloo, Morteza, et al.
(författare)
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Generative artificial intelligence in manufacturing : opportunities for actualizing Industry 5.0 sustainability goals
- 2024
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Ingår i: Journal of Manufacturing Technology Management. - : Emerald Group Publishing Limited. - 1741-038X .- 1758-7786. ; 35:9, s. 94-121
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach. Design/methodology/approach: The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0. Findings: Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations. Practical implications: While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits. Originality/value: This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.
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| 7. |
- Mahmoodi, Ehsan, et al.
(författare)
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A framework for throughput bottleneck analysis using cloud-based cyber-physical systems in Industry 4.0 and smart manufacturing
- 2024
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 232, s. 3121-3130
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Tidskriftsartikel (refereegranskat)abstract
- The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement.
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| 8. |
- Mahmoodi, Ehsan, et al.
(författare)
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Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 72, s. 287-307
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Tidskriftsartikel (refereegranskat)abstract
- Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.
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| 9. |
- Senali, Madugoda Gunaratnege, et al.
(författare)
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Determinants of trust and purchase intention in social commerce : Perceived price fairness and trust disposition as moderators
- 2024
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Ingår i: Electronic Commerce Research and Applications. - : Elsevier. - 1567-4223 .- 1873-7846. ; 64
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Tidskriftsartikel (refereegranskat)abstract
- The study investigates the determinants of trust in sellers and products and purchase intention in the social commerce (s-commerce) context by considering the moderating effects of trust disposition and perceived price fairness. The data were collected from 416 individuals who have followed at least one seller on Instagram and analysed using the Partial Least Squares (PLS) approach. The findings revealed that review quantity, review quality, perceived symmetric product information, and responsiveness positively influence trust in seller. The direct influence of review quality on trust in products was confirmed. Trust disposition negatively moderates the impacts of review quality on trust in sellers and responsiveness on trust in products. Furthermore, perceived price fairness positively moderates the influence of trust in sellers and products on purchase intention. The findings extend the literature on s-commerce in several ways. The findings enable s-commerce sellers to formulate effective marketing strategies and boost purchase intention.
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| 10. |
- Tirkolaee, Erfan Babaee, et al.
(författare)
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Integrated design of a sustainable waste management system with co-modal transportation network : A robust bi-level decision support system
- 2024
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Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 449
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Tidskriftsartikel (refereegranskat)abstract
- Efficient waste management practices play a critical role in addressing the acute challenges of environmental protection, public health and resource conservation. A well-designed system guarantees that waste is efficiently collected, treated and disposed while minimizing negative impacts on ecosystems and human well-being. This work presents a robust bi-level decision support system to establish a sustainable waste management system using a co -modal transportation network to treat municipal solid waste timely and efficiently. Consequently, two integrated multi-objective mathematical models are developed to formulate the problem. Configuring the municipal solid waste network in the first level of the suggested decision support system, the transportation network is designed in the second level taking into account non-identical modes. The objectives are to minimize total cost and total emission in both levels, while maximization of total job creation is also addressed in the first level. Robust optimization method and weighted goal programming method are then utilized to accommodate the developed decision support system against uncertainty and multi-objectiveness, respectively. To validate the efficiency of these methods, they are assessed against possibilistic linear programming technique and Lp-metric approach with the help of simple additive weighting (SAW) method, respectively. Eventually, several numerical examples are generated based on the benchmarks given in the literature, which are then tackled using CPLEX solve to appraise the applicability and complexity of the developed methodology. The findings reveal the efficacy of the decision support system in terms of finding solutions in less than 448 s on average. Finally, sensitivity analyses are performed to draw out useful practical implications and decision aids.
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