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1 – 10 of 22Satyendra Kr Sharma, Rajkumar Sharma and Anil Jindal
Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This…
Abstract
Purpose
Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.
Design/methodology/approach
Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.
Findings
It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.
Originality/value
This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.
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Julio Henrique Costa Nobrega, Tiago F.A.C. Sigahi, Izabela Simon Rampasso, Vinicius Luiz Ferraz Minatogawa, Gustavo Hermínio Salati Marcondes de Moraes, Lucas Veiga Ávila and Rosley Anholon
This paper aims to analyze the main challenges and critical success factors (CSFs) in managing multi-sided platforms (MSP) in Brazil, as well as to understand the differences…
Abstract
Purpose
This paper aims to analyze the main challenges and critical success factors (CSFs) in managing multi-sided platforms (MSP) in Brazil, as well as to understand the differences between this management model and traditional companies.
Design/methodology/approach
Semi-structured interviews were conducted with experienced professionals in the field, focusing on challenges, CSFs and difficulties in managing MSP businesses. The data were analyzed using a mixed-method approach, involving content analysis for qualitative data and grey relational analysis and sensitivity analysis for quantitative data.
Findings
The experts identified eight CSFs, seven key differences between traditional businesses and MSPs, and five technology-related challenges in managing MSPs. They assessed the main difficulties reported in the literature and ranked them, with the most critical challenges being competition with companies adopting MSP models in the same sector (product/service niche) and the necessity for ongoing process adjustments to accommodate scalability.
Originality/value
This study enhances understanding of CSF, disparities between traditional and MSPs and technology-related challenges in this management model. The results can assist managers in emerging nations in enhancing the performance of MSP operations and can be a resource for researchers studying various contexts and creating company guidelines.
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Soumyananda Dinda and Poulomi Khasnobis
This paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era.
Abstract
Purpose
This paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era.
Design/methodology/approach
Crime and socio-economic data are taken from National Crime Record Bureau and the Reserve Bank of India, respectively. Twenty major Indian states are selected for the study purpose for the period of 1994–2019. Fixed effect panel data technique is used for analysis purpose.
Findings
Property crime rate declines with economic growth, while it increases with financial development. Findings of fiscal policy instruments are different. Own tax is positively associated with property crime in India, while non-tax fiscal instruments such as fine, penalty, and so on, are inversely related to it. Property crime rate is inversely related to institutional factors like charge sheet and conviction rate.
Research limitations/implications
Further research is needed for other crimes in India. State-level data are used here for analysis purpose; however, spatial or cluster analysis techniques might provide more insights for combating crimes in India.
Practical implications
This study suggests that economic growth and fiscal instrument along with institutional development are essential to control property crime in India.
Social implications
Government should take steps to improve the law-and-order system to control property crime across states.
Originality/value
Impact of non-tax fiscal instrument reduces property crime while that of own tax is increases it in India. These findings are unique and added certain insight in the study. Institutional roles are captured its performances like charge sheet and convict rate, which are significantly reduce property crime in Indian states. Least square dummy variable model is applied to capture individual state effects.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0063
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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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Daniel de Abreu Pereira Uhr, Mikael Jhordan Lacerda Cordeiro and Júlia Gallego Ziero Uhr
This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income…
Abstract
Purpose
This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.
Design/methodology/approach
Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.
Findings
The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.
Originality/value
This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.
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Ville Jylhä, Noora Hirvonen and Jutta Haider
This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.
Abstract
Purpose
This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.
Design/methodology/approach
Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.
Findings
The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.
Originality/value
This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.
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Saranya P., Praveen Nagarajan and A.P. Shashikala
This study aims to predict the fracture properties of geopolymer concrete, which is necessary for studying failure behaviour of concrete.
Abstract
Purpose
This study aims to predict the fracture properties of geopolymer concrete, which is necessary for studying failure behaviour of concrete.
Design/methodology/approach
Geopolymers are new alternative binders for cement in which polymerization gives strength to concrete rather than through hydration. Geopolymer concrete was developed from industrial byproducts such as GGBS and dolomite. Present study estimates the fracture energy of GGBS geopolymer concrete using three point bending test (RILEM TC50-FMC) with different percentages of dolomite and compare with cement concrete having same strength.
Findings
The fracture properties such as peak load, critical stress intensity factor, fracture energy and characteristic length are found to be higher for GGBS-dolomite geopolymer concrete, when their proportion becomes 70:30.
Originality/value
To the best of the authors’ knowledge, this is an original experimental work.
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Hubannur Seremet and Nazim Babacan
This paper aims to examine the static compression characteristics of cell topologies in body-centered cubic with vertical struts (BCCZ) and face-centered cubic with vertical…
Abstract
Purpose
This paper aims to examine the static compression characteristics of cell topologies in body-centered cubic with vertical struts (BCCZ) and face-centered cubic with vertical struts (FCCZ) along with novel BCCZZ and FCCZZ lattice structures.
Design/methodology/approach
The newly developed structures were obtained by adding extra interior vertical struts into the BCCZ and FCCZ configurations. The samples, composed of the AlSi10Mg alloy, were fabricated using the selective laser melting (SLM) additive manufacturing technique. The specific compressive strength and failure behavior of the manufactured lattice structures were investigated, and comparative analysis among them was done.
Findings
The results revealed that the specific strength of BCCZZ and FCCZZ samples with 0.5 mm strut diameter exhibited approximately a 23% and 18% increase, respectively, compared with the BCCZ and FCCZ samples with identical strut diameters. Moreover, finite element analysis was carried out to simulate the compressive response of the lattice structures, which could be used to predict their strength and collapse mode. The findings showed that while the local buckling of lattice cells is the major failure mode, the samples subsequently collapsed along a diagonal shear band.
Originality/value
An original and systematic investigation was conducted to explore the compression properties of newly fabricated lattice structures using SLM. The results revealed that the novel FCCZZ and BCCZZ structures were found to possess significant potential for load-bearing applications.
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Miaoxian Guo, Shouheng Wei, Chentong Han, Wanliang Xia, Chao Luo and Zhijian Lin
Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical…
Abstract
Purpose
Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.
Design/methodology/approach
This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.
Findings
The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.
Originality/value
A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.
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Asif Ur Rehman, Pedro Navarrete-Segado, Metin U. Salamci, Christine Frances, Mallorie Tourbin and David Grossin
The consolidation process and morphology evolution in ceramics-based additive manufacturing (AM) are still not well-understood. As a way to better understand the ceramic selective…
Abstract
Purpose
The consolidation process and morphology evolution in ceramics-based additive manufacturing (AM) are still not well-understood. As a way to better understand the ceramic selective laser sintering (SLS), a dynamic three-dimensional computational model was developed to forecast thermal behavior of hydroxyapatite (HA) bioceramic.
Design/methodology/approach
AM has revolutionized automotive, biomedical and aerospace industries, among many others. AM provides design and geometric freedom, rapid product customization and manufacturing flexibility through its layer-by-layer technique. However, a very limited number of materials are printable because of rapid melting and solidification hysteresis. Melting-solidification dynamics in powder bed fusion are usually correlated with welding, often ignoring the intrinsic properties of the laser irradiation; unsurprisingly, the printable materials are mostly the well-known weldable materials.
Findings
The consolidation mechanism of HA was identified during its processing in a ceramic SLS device, then the effect of the laser energy density was studied to see how it affects the processing window. Premature sintering and sintering regimes were revealed and elaborated in detail. The full consolidation beyond sintering was also revealed along with its interaction to baseplate.
Originality/value
These findings provide important insight into the consolidation mechanism of HA ceramics, which will be the cornerstone for extending the range of materials in laser powder bed fusion of ceramics.
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