Industrial Decision Research Articles

A critical investigation into identifying key focus areas for the implementation of blockchain applications in the mining industry

Digital information and data are crucial drivers of progress in various industries, including mining, where data-driven decision making optimizes resource extraction, enhances safety, and ensures sustainability. The adoption of digital technologies like artificial intelligence (AI) and the Internet of Things has amplified the importance of digital information. However, the integrity and security of this information are paramount, leading to the exploration of blockchain technology as a potential solution for secure digital value exchange in mining. This research examines blockchain's capabilities, drawing insights from its applications in sectors like banking, government, healthcare, and entertainment, and evaluates their relevance to mining's core value chain processes. The study identifies blockchain's distributed ledger technology, cryptographic security, and decentralization as unique advantages that can revolutionize mining by enhancing transaction speed, reducing costs, and improving supply chain transparency and compliance with sustainability standards. Blockchain's transparent and auditable records can enhance business transparency, fostering trust among stakeholders, including investors and regulatory bodies. The technology's consensus mechanisms and smart contracts further promote trust in collaborative ventures. This research provides a foundational understanding for decision-makers in the mining industry to evaluate blockchain's feasibility and potential return on investment, guiding strategic planning and resource allocation for blockchain applications. By leveraging blockchain, mining companies can optimize operations, improve sustainability practices, and establish a robust foundation for future growth, positioning blockchain as a transformative technology in the sector.

Analysis of Operational Strategies for POP MART Based on SWOT Analysis

With the development of POP MART, many people are curious to know how their industry developed. The analysis of POP MART in this paper has certain references and learning significance for the market. This paper employed the SWOT analysis method to evaluate the strengths and weaknesses of POP MART, while also assessing the market to determine the company's profitability. The analysis aimed to understand POP MART's competitive position and financial performance comprehensively. Based on recent research, the rapid development of POP MART can be attributed to its ability to effectively cater to the evolving shopping preferences of the younger demographic and adapt to the changing socio-economic landscape. This adaptability has allowed POP MART to successfully align itself with the dynamic demands of the current era, thereby driving its accelerated growth within the retail sector. This multidimensional exploration of both frameworks offers valuable insights for stakeholders and decision-makers in the retail industry.

Impact of Generative AI on Enterprise Performance in China

This study examines the impact of generative artificial intelligence (GAI) on Chinese enterprises. 320 participants completed electronic surveys, revealing a positive relationship between GAI and corporate performance. Managerial relationships were found to play a crucial role, mediating the influence of GAI on performance. Additionally, the speed of technological change in the industry was identified as a moderator, highlighting the dynamic nature of GAI's impact on managerial relationships and firm performance. These findings underscore the strategic importance of GAI in cultivating managerial relationships and improving enterprise performance, deepening our understanding of the integration of advanced AI technologies and management practices in the Chinese market. Practical implications are offered for decision-makers in technology-intensive industries, providing valuable insights for leveraging GAI to gain a competitive edge and achieve sustainable growth in a rapidly evolving technological landscape.

On CO2 efficiency and trade-offs between sustainability, safety, economy and resilience in infrastructure developments

The escalating crisis of climate change underscores the pressing need to reduce global CO2e emissions substantially. At the same time, a major increase is expected in global demand for infrastructure systems. In meeting this challenge, we here provide and illustrate a novel modeling framework that affords decision support in pursuit of sustainable developments of infrastructure systems. Based on this framework, for the case of roadway traffic infrastructure systems, it is shown that different strategies for the development of roadway traffic infrastructure systems, yielding approximately the same economic benefits to society, can differ in their embedded carbon footprint by up to a factor of three. This knowledge has the potential to contribute significantly to sustainable developments in general and mitigation of climate change in particular. Our models and results may readily assist decision-makers in the construction industry, at national and supra-national scales, in identifying optimal strategies for infrastructure developments. Furthermore, our work may straightforwardly support regulatory bodies and code writers in defining requirements for material and structural performances for the next generation of standards and codes.

Water Absorption Behavior of Date Palm Fruit Branches Fiber (DPF) Composites: Experimental and Statistical Analyses

ABSTRACT The environmental effects on the response of Date Palm Fiber (DPF) composites are explored through an in-depth analysis of water absorption behavior. This study comprehensively investigates the influence of fiber content, immersion time, and water type on mass gain. Rigorous experimentation and advanced statistical analyses quantify the percent contribution of each factor, emphasizing the dominant role of fiber content at 96.61%. While immersion time and water type contribute relatively smaller percentages (2.11% and 1.28%, respectively), their insights are invaluable for tailored composite design. The study extends to interaction effects, showcasing the combined influence of factors. Regression models, progressing from linear and reciprocal formulations to comprehensive global models, are developed. Meticulous examination of prediction accuracy, using diverse statistical metrics, highlights the superior performance of the global reciprocal model across different water types. This work provides essential insights for optimizing DPF composite design, fabrication, and application, empowering engineers and researchers to make informed decisions in industries demanding tailored water absorption behavior.

Analyzing sustainability in bread production: a life cycle assessment approach to energy, exergy and environmental footprint.

Bread production is a pivotal component of global nutrition. However, its extensive production imposes significant strain on resources and energy, resulting in substantial environmental consequences. This study focuses on a multidimensional assessment of the environmental sustainability of the bread life cycle as a case study in Iran. By integrating four life cycle assessment (LCA) methods, this research demonstrates a comprehensive analysis of environmental effects, energy consumption, and exergy demand in bread production. It also identifies the hotspot stages and inputs within the bread production chain. Eventually, it proposes strategies for mitigating the environmental impacts in line with sustainable development goals. Data collection involved questionnaires by face-to-face interviews. The LCA evaluation was conducted using SimaPro software. Sustainability analysis was assessed using four different methods: CML, ReCiPe, cumulative energy demand (CED), and cumulative exergy demand (CExD) method, from cradle to bakery gate. The CML method results indicate that the highest environmental impacts are associated with marine aquatic ecotoxicity (157.04 to 193.36 kg 1,4-DB eq), fossil fuel depletion (11.05 to 12.73 MJ), eutrophication (4.20 × 10-3 to 4.70 × 10-3 kg PO4-3eq), acidification (8.09 × 10-3 to 9.16 × 10-3 kg SO2 eq), and global warming (0.61 to 0.69 kg CO2 eq). The ReCiPe method highlights wheat production stages and gas consumption as the most significant contributors to damage in terms of human health, ecosystems, and resource consumption indicators. The CED method reveals that fossil energy accounts for over 97% of the energy consumed during the bread life cycle. Energy consumption per kilogram of bread ranges from 12.07 to 13.93 MJ. The CExD method for producing 1 kg of traditional bread falls between 32.25 and 35.88 MJ. More than 60% of this value is attributed to renewable resources of water used in irrigation during the wheat farming stage, while over 35% is linked to non-renewable fossil resources, primarily due to the consumption of natural gas in bakery operations. To assess the potential decrease in environmental emissions, a sensitivity analysis was performed, considering the effects of substituting natural gas with biogas and grid electricity with photovoltaic electricity in the bakery. Then, three improved scenarios were developed, each demonstrating effective reductions in environmental impacts, with the most remarkable decreases observed in marine aquatic ecotoxicity (55%) and fossil fuel depletion (44%). Overall, the findings demonstrate that Sangak bread production exhibits a more environmentally friendly profile than other types of bread. These results can guide decision-makers in the bread production industry towards implementing sustainable practices that prioritize resource efficiency and environmental conservation. Also, stakeholders can develop strategies to reduce the environmental impacts and work towards a more sustainable future.

A novel framework for integrating environmental costs and carbon pricing in open-pit mine plans: Towards sustainable and green mining

As the worldwide need for mineral resources continues to grow, there is an increasing emphasis on mitigation of the environmental consequences of the mining sector, necessitating a shift towards sustainable and green mining practices. This paradigm requires the meticulous integration of environmental considerations throughout all stages of planning and operation in open-pit mining. Therefore, this research introduces an innovative integrated framework for long-term mine planning, strategically combining economic and environmental considerations in open-pit mining operations. The methodology integrates carbon pricing into the decision-making process, addressing potential financial risks and introducing two distinct scenarios: one focused on minimizing environmental costs and the other on maximizing adjusted Net Present Value (NPV). The application of this integrated framework to an open-pit iron mine in the Middle East region yielded insightful results. The integrated framework's impact on block sequencing, greenhouse gas emissions, and financial outcomes was assessed, highlighting the divergent strategies employed in the two scenarios. The minimization scenario demonstrated it was feasible to attain 90.54% of the global warming impact and 90.48% of environmental costs while achieving 96.25% of the adjusted NPV compared to the maximization scenario. Sensitivity analyses further underscored the adaptability of the framework to varying carbon pricing scenarios. This research validates a sustainable approach to mine planning, emphasizing a balance between economic returns and ecological responsibility, and provides valuable insights for decision-makers in the mining industry.

Petrological Characteristics of Lateritic Bauxite Deposits of India and Their Significance in Mineral Processing

India is endowed with 3896 million tons of bauxite resources and ranking it fifth globally. The bauxite comprises various minerals such as gibbsite, boehmite, diaspore, hematite, goethite, kaolinite, quartz, anatase and rutile. India possesses abundant lateritic bauxite resources originating from parent rocks like Khondalite, Deccan trap basalt, Granite Gneiss, Sandstone, etc., each exhibiting its specific characteristics. The geotechnological evaluation of India's lateritic bauxite deposits, conducted by Jawaharlal Nehru Aluminium Research Development and Design Centre (JNARDDC), revealed the presence of two types of deposits: high-level and low-level (coastal). In this study, bauxite and laterite samples were collected from different regions in India and subjected to comprehensive technological evaluation. Mineral processing techniques are optimized based on the mineralogical and petrological characteristics of the bauxite deposits. The mineral liberation, association, and alteration information obtained through petrological analysis guide the selection and optimization of various methods. The understanding of the mineralogy and petrological characteristics also assists in predicting the behaviour of bauxite during the processing stages, facilitating process control and minimizing losses. This paper aims to highlight the petrological, chemico-mineralogical characteristics of Indian lateritic bauxite deposits and emphasize the significance of petrology and mineralogy in their evaluation and mineral processing. Microscopic examination plays a vital role in mineral characterization and the beneficiation process by enabling mineral identification, texture analysis, grain size, mineral liberation, association, alteration, and process optimization. Such comprehensive analysis contributes to maximizing the economic value of mineral resources, aiding informed decision-making in exploration, mining, and mineral processing industries. Keywords: Bauxite, Lateritic Deposits, Petrological Characteristics, Mineralogy, Mineral Processing

A Comprehensive Study on the Influence of Corporate Governance Factors on Financial Performance of Listed Banking Companies in Bangladesh

This comprehensive study investigates the influence of organizational management elements on the economic outcomes of publicly traded banking enterprises in Bangladesh. The research begins with an introduction highlighting the significance of this study Within the framework of the Bangladeshi Financial industry. The review of literature delves into existing research on Effective management of organizations and their economic achievements, providing a foundation for this study. It identifies gaps and emphasizes the need for specific analysis in the Bangladeshi banking industry. An abbreviated summary of corporate oversight principles and practices sets the stage for understanding the study’s framework. Subsequently, the analysis of the financial industry in Bangladesh offers essential context for the study. The examination and results portion showcases factual information and outcomes, demonstrating the correlation between corporate governance elements and economic efficiency in banks listed in Bangladesh. Here we use five years data of five banks and then we use SPSS as a statistical tool for data analysis. The conclusion synthesizes these findings, highlighting key takeaways and their implications for the sector. Finally, the study concludes by discussing policy implications. It outlines recommendations for regulatory bodies and banking institutions to enhance the financial performance of banks in Bangladesh by implementing effective corporate governance strategies and practices. This study intends to provide valuable perspectives for stakeholders and decision-makers in the banking industry.

Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany

Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.

Job Seeker's Perceived Employability and Hiring Manager's Decision-Making in the Hospitality Industry

The relationship between job applicants 'perceived employability' and employers' actual hiring decisions is an important issue for both job applicants and hiring organizations, as well as higher education. Actual 'employability' is a set of skills and qualifications suitable for organizational job roles when hiring managers make decisions. However, most research heavily focuses on job applicants: the relationship among perceived employability, proactive career behavior, and career motivation. Little research has involved actual 'employability' from the perspective of hiring managers. The study examines both perceived employability and the employability of hospitality industry hiring managers. The hospitality industry's turnover rate is much higher than the overall industry average. We investigate the relationship between hospitality job applicants' career motivation and perceived employability mediated through proactive career behavior.

Demolition of the European ships fleet: A scenario analysis

This paper conducts a detailed exploration of projected trends in the demolition and recycling of the European ship fleet, in light of the dismantling capacity in Europe. Utilizing a robust predictive model based on the Weibull distribution, the study synthesizes historical data and current operational metrics to forecast trends up to the year 2050, such as ship typologies, their average age, and recycling frequency. By estimating the rate and volume of ships reaching their end-of-life (EOL), the study aims to delineate the required capacity for ship recycling in Europe over the coming decades. The analysis reveals the key factors influencing the demolition trajectory and provide essential insights for future planning and policy formulation in the field of ship recycling. The study also underscore the importance of developing adaptive strategies in ship dismantling catering to the anticipated shifts in fleet composition and evolving environmental regulations, offering a foundation for informed decision-making and policy development in the maritime industry.

The intention of adopting blockchain technology in agri-food supply chains: evidence from an Indian economy

Purpose This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices. Design/methodology/approach The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework. Findings The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity. Practical implications This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC. Originality/value This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

Towards Sustainable Internet Service Provision: Analyzing Consumer Preferences through a Hybrid TOPSIS–SEM–Neural Network Framework

In our increasingly digital world, grasping consumer preferences for internet service providers (ISPs) is paramount. This study was conducted in Occidental Mindoro, Luzon, the Philippines, and surveyed 280 respondents across ten municipalities with 81 in-person and online questionnaires. The research focused on twelve latent variables: internet speed, assurance, tangibility, responsiveness, reliability, empathy, data privacy, service quality, value-added services, price, customer satisfaction, and customer loyalty. Methods such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Structural Equation Modeling (SEM), and an Artificial Neural Network (ANN) were integrated for the analysis. The study utilized TOPSIS to rank ISPs based on Service Quality (SERVQUAL) parameters, followed by SEM to delve into variable connections affecting preferences, and through as ANN, consumer behavior and loyalty were predicted. The SEM–ANN results revealed that assurance, responsiveness, empathy, and data privacy directly impacted service quality. Moreover, a significant correlation was found between customer satisfaction and service quality, influencing further customer loyalty alongside pricing. By integrating these methodologies, the study provides a comprehensive understanding of ISP preferences and emphasizes service quality as the most significant factor in industry decision-making.

Consumer Behavior in the World of Cryptocurrencies

Abstract Cryptocurrencies are a new form of digital assets that have gained increasing popularity in recent years. Investors have a dual objective of maximizing profits while minimizing risks. In today's world, there is an increase in the demand for cryptocurrencies, with focus on the emotional aspects as well as on the underlying technical analysis. This abstract provides a synthesis of recent research and insights into the behavior of consumers engaging with cryptocurrencies. Key determinants such as trust, perceived usefulness, and ease of use play pivotal roles in driving consumer adoption of cryptocurrencies. Furthermore, behavioral uncertainty and risk perception emerge as critical considerations impacting investment decisions within this dynamic ecosystem. The abstract also highlights the significant influence of digital platforms and social media on shaping consumer attitudes and behaviors towards cryptocurrencies, underscoring the importance of online discourse and information dissemination in this context. As the cryptocurrency market develops and grows, understanding consumer behavior becomes increasingly paramount for stakeholders, policymakers, and researchers alike. By unraveling the complexities of consumer preferences, motivations, and perceptions, this abstract offers valuable perspectives to inform strategic decision-making and foster sustainable growth in the cryptocurrency industry. This research was based on scientific articles and carefully selected and studied important data from trusted sources like academic journals, financial databases, and websites focusing on cryptocurrency information.

Principal-feature-guided degradation trend prediction algorithm based on gear fault dynamics model

The long-term remaining useful life (RUL) prediction of gears is one of the key technologies for gear failure prediction and health management, which has significant practical significance for maintenance decision-making in the traditional mechanical industry. However, due to the lack of sufficient failure data, most RUL prediction methods face significant challenges when predicting gear degradation trends under different fault conditions. The purpose of this article is to solve the dependency of predictive models on data. The paper proposes a principal-feature-guided degradation trend prediction algorithm based on the gear fault dynamics model. Based on the degradation mechanisms under different failure modes, this paper establishes a high-fidelity faulty gears dynamic model. The accuracy of the dynamic model for two failure modes has reached 90% and 88%. The model can provide adequate failure data for RUL faulty gear prediction. Furthermore, a principal-feature-guided Transformer-TCN-ATT (TTA) algorithm was proposed to achieve trend prediction of faulty gears. The prediction algorithm introduces a principal feature reinforcement to reduce the impact of high-frequency fluctuations on feature extraction. Compared with the various typical methods, the proposed algorithm improves prediction accuracy by 76% and 77% for two failure modes.

Tourism Demand Forecast and Future Market Trend Research

This study aims to reveal the trends and changes in the development of the tourism market by forecasting tourism demand and future market trends, providing important references for decision-makers in the tourism industry. We adopted data analysis methods to construct a tourism demand forecasting model and analyzed the impact of different factors on tourism demand. The main findings include the trend of diversification and dynamism in tourism demand due to changes in economic, social, and environmental factors. The implications of this study for future market trends include guiding the tourism industry in formulating corresponding strategies to enhance market competitiveness. In summary, this study provides important references for the future development of the tourism market and has significant theoretical and practical implications.

Sustainable supply chain decision-making in the automotive industry: A data-driven approach

The auto parts manufacturing sector faces multifaceted challenges ranging from production planning to sustainability imperatives, necessitating innovative solutions. This study presents an integrated data-driven approach tailored to address these challenges. Leveraging advanced AI techniques, including Convolutional and Recurrent Neural Networks optimized with the Moth-flame Optimization Algorithm (MFO), we accurately predict demand quantities for automotive components. Through empirical validation with Iranian auto parts manufacturers, our model achieves an impressive accuracy rate of over 90 %. Subsequently, Data Envelopment Analysis (DEA) evaluates suppliers not only based on demand quantities but also on their social, economic, and environmental impacts, with a resulting average efficiency score of 0.75. The Best-Worst Method (BWM) further refines supplier selection, leading to the identification of top-performing suppliers with an average score of 0.8. This comprehensive approach enables auto parts manufacturers to optimize production planning processes while aligning with sustainable development goals. The successful application of our model underscores the transformative potential of integrating business analytics and AI in the automotive industry towards sustainability.

Does employee engagement mediate the nexus of job resource and employee turnover intentions?

PurposeEngaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.Design/methodology/approachData were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.FindingsData supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.Research limitations/implicationsThe generalizability of the findings will be constrained due to the research’s pharmaceutical industry focus and cross-sectional data.Practical implicationsThe study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.Originality/valueBy empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.

Towards circular fashion: Management strategies promoting circular behaviour along the value chain

The fashion industry is a significant driver of cultural, societal, and economic growth worldwide, but its traditional linear approach has led to negative environmental and social impacts. This study addresses the need to transition towards sustainable and circular practices by exploring the relationship between management strategies and behaviours across the fashion value chain. Through 10 semi-structured interviews with top industry decision-makers, thematic analysis revealed eight core themes, including collaborations, marketing strategies, product-related characteristics, education and awareness, business models, integration of technology, regulations and legislation, and strategic planning. The study provides targeted recommendations for stakeholders to promote a more sustainable and circular behaviour in the fashion industry. For instance, it emphasises strategic partnerships with suppliers, waste management companies, and consumers to minimise waste and promote circularity across the value chain. It urges the adoption of sustainable materials and eco-friendly designs for environmentally conscious consumers and encourages visual storytelling for transparency. Additionally, the study highlights the need for circular business models, recommending on-demand and made-to-measure manufacturing to align production with demand and reduce overproduction. Offering end-of-life strategies such as take-back programs, recycling initiatives, and repair services extends product lifecycles and creates sustainable value. Integrating advanced technologies such as digital IDs improves supply chain transparency and accountability. These actionable recommendations guided the development of a comprehensive framework for the practical application of sustainable and circular practices across the value chain, offering industry practitioners, researchers and policymakers effective strategies for driving impactful change within the fashion industry.