Shweta V. Matey, Dadarao N. Raut, Rajesh B. Pansare and Ravi Kant
Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve…
Abstract
Purpose
Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.
Design/methodology/approach
Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.
Findings
According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.
Practical implications
The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.
Originality/value
This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.
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Swayam Sampurna Panigrahi, Rajesh Katiyar and Debasish Mishra
The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to…
Abstract
Purpose
The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to address wasteful or non-value-adding activities in the supply chain is crucial. This article determines key factors of lean supply chain management (LSCM) for continuous improvement in the manufacturing sector.
Design/methodology/approach
The methodology comprises three steps. The first step identifies critical factors of LSCM in manufacturing from prior research and a series of expert consultations. Critical factors are identified and validated that industries can leverage to attain their lean goals. The second step uses the decision-making and trial evaluation laboratory (DEMATEL) method to determine the causal relationship among the factors. DEMATEL analysis categorizes factors into cause and effect, which will assist industry personnel in decision-making. The third step involves further data analysis to visualize the importance of the most critical factors. It develops a machine learning (ML) model in the form of a decision tree that helps in assessing the factors into cause or effect groups via a threshold value of expert ratings.
Findings
IT tools, JIT manufacturing and material handling and logistics form the most critical factors for LSCM implementation.
Originality/value
The analysis from DEMATEL and ML together will be beneficial for manufacturing practitioners to improve the supply chain performance based on the identified factors and their criticality towards LSCM implementation.
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From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply…
Abstract
Purpose
From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.
Design/methodology/approach
This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.
Findings
The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.
Research limitations/implications
There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.
Originality/value
This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.
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Ali B. Mahmoud, V. Kumar, Alexander Berman, Samer Elhajjar and Leonora Fuxman
This study aims to explore blockchain potential for digital marketing (BlkChn-Mk-KAP) by developing and validating a measurement model for assessing the constructs of knowledge…
Abstract
Purpose
This study aims to explore blockchain potential for digital marketing (BlkChn-Mk-KAP) by developing and validating a measurement model for assessing the constructs of knowledge, attitude and practice (KAP) related to blockchain technology in digital marketing.
Design/methodology/approach
A four-study process was used. The first study reviewed the literature to develop a pool of possible measurement items. Using exploratory factor analysis and reliability assessments, Study 2 (n = 162) investigated the dimensionality of the items developed in Study 1. The factorial structure from Study 2 was validated in Study 3 (n = 204), and the measurement model invariance was assessed using covariance-based structural equation modelling (CB-SEM). Finally, in Study 4 (n = 203), the predictive validity of the BlkChn-Mk-KAP was tested using a CB-SEM approach, testing its constructs correlations with the perceived usefulness of blockchain for digital marketing.
Findings
The findings indicate that the BlkChn-Mk-KAP measurement model comprises three-dimensional multi-item scales: knowledge, attitude and practice.
Research limitations/implications
This study introduces a promising BlkChn-Mk-KAP model to examine blockchain’s role in digital marketing. The authors acknowledge the sampling limitation in this research. To enhance the generalisability of the findings, future research should expand to different groups, including generation, gender and age. In addition, further exploration of the explicit links between blockchain knowledge, attitudes and subsequent digital marketing performance is warranted.
Practical implications
Educating employees about blockchain technology’s unique features can shape favourable attitudes and stimulate the utilisation of blockchain-enabled technologies in digital marketing practice. BlkChn-Mk-KAP can offer a reliable and valid instrument to benchmark marketers’ KAP of blockchain-powered digital marketing as they implement blockchain technology to gain a competitive advantage.
Social implications
This study helps to adopt sustainable practices ensuring the wellbeing of the key stakeholders.
Originality/value
This research introduces the first validated conceptualisation and measurement model, BlkChn-Mk-KAP, to evaluate blockchain KAPs among digital marketing professionals.
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Rohit Raj, Arpit Singh, Vimal Kumar and Pratima Verma
Recent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in…
Abstract
Purpose
Recent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in driving organizations to achieve efficiency and transparency in supply chains. However, there exist some insurmountable challenges associated with the adoption of BT in organizational supply chains (SC). This paper attempts to categorically identify and systematize the most influential challenges in the implementation of BT in SC.
Design/methodology/approach
This study resorts to an extensive literature review and consultations with experts in the field of supply chain management (SCM), information technology and academia to identify, categorize and prioritize the major challenges using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Combined Compromise Solution method (CoCoSo).
Findings
The top three classes of challenges revealed in this study are privacy challenges (PC), infrastructure challenges (IC) and transparency challenges (TC). Maintaining a balance between data openness and secrecy and rectification of incorrect/erroneous input are the top two challenges in the PC category, integration of BT with sustainable practices and ensuring legitimacy are the top two challenges in the IC category, and proper and correct information sharing in organizations was the top most challenge in the TC category.
Originality/value
Future scholars and industry professionals will be guided by the importance of the challenges identified in this study to develop an economical and logical approach for integrating BT to increase the efficiency and outcome of supply chains across several industrial sectors.
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Monojit Das, V.N.A. Naikan and Subhash Chandra Panja
The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear…
Abstract
Purpose
The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.
Design/methodology/approach
This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.
Findings
Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.
Originality/value
This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.
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Dheeraj Lal Soni, Venkata Swamy Naidu Neigapula and Jagadish Jagadish
This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin…
Abstract
Purpose
This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface.
Design/methodology/approach
The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects.
Findings
The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface.
Research limitations/implications
Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces.
Practical implications
The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools.
Social implications
Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration.
Originality/value
Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/
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This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.
Abstract
Purpose
This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.
Design/methodology/approach
This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.
Findings
The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.
Practical implications
This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.
Originality/value
This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.