ENHANCING FASHION FORECASTING ACCURACY THROUGH CONSUMER DATA ANALYTICS: INSIGHTS FROM CURRENT LITERATURE

The importance of data has been gradually acknowledged by fashion professionals to improve sales and margins because fashion brands and retailers need to develop, manufacture, and sell styles that resonate with consumers. Lately, advancements in data analytics, machine learning, and computing power, the value of utilizing artificial intelligence (AI)-based software or applications has been well acknowledged by fashion brans and retailers who want to apply a data-driven decision-making approach to develop more efficient fashion design, merchandising, and marketing strategies. In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are currently available in the fashion industry are also introduced. Finally, a need for developing courses or programs focusing on fashion- specific data analytics in higher education is addressed as more and more fashion brands and retailers are trying to hire fashion data analysts. Collaboration with technology partners who are providing AI-powered data analytics services to fashion brands and retailers is needed to educate fashion students with practical knowledge and skills

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

This chapter presents a case study of Boubyan Bank, a leading financial institution in Kuwait, to showcase its strategic vision for integrating Financial Technology (FinTech). It was first established as a conventional Islamic bank but changed substantially after it adopted FinTech solutions to improve its service delivery and operational efficiency. This chapter follows the bank’s evolution from its traditional banking roots to a technologically advanced institution, with a focus on implementing key technologies such as blockchain, Artificial Intelligence and advanced data analytics. These technologies have enabled the bank to improve as regards transaction security, customer service and the ability to offer personalised financial products. The impact of these technological integrations is explored, and there is discussion of how improvements in operational efficiency and customer engagement have helped the bank to more effectively serve a broader customer base. Future prospects for Boubyan Bank within the broader context of ongoing and emerging trends in the FinTech sector are considered, and the bank’s plans to maintain its competitive advantage by leveraging new technologies are discussed. This case study illustrates Boubyan Bank’s successful integration of FinTech, an achievement that provides valuable insights and strategic lessons for other financial institutions who are considering similar technological advancements. This chapter aims to set a benchmark in digital excellence, underlining Boubyan Bank’s pioneering role in transforming the financial landscape in Kuwait and beyond.

Urbanization's rapid progress has led to many big cities, which have modernized people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges can seem nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data implies rich knowledge about a city and can help tackle these challenges when used correctly. Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. Urban computing also helps us understand the nature of urban phenomena and even predict the future of cities.

The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).

The Industrial Revolution 4.0 era has changed the landscape of the world of manufacturing and technology with the integration of digital technology, automation and sophisticated data communications. The fashion industry in Indonesia is also not immune from the major impact of these changes, especially in marketing decision making. Big data analysis is crucial in optimizing marketing strategies by understanding consumer preferences, market trends and competition. However, the use of big data also brings challenges, especially regarding the security and privacy of consumer data. This research adopts a qualitative method with a literature review approach to investigate the use of big data in marketing decision making in the Indonesian fashion industry. The research results show that big data plays an important role in customer segmentation, consumer behavior analysis, trend prediction, and product innovation. The benefits companies gain from implementing big data include deep understanding of consumer behavior, fast decision making, operational efficiency, better risk management, product and service innovation, improved customer experience, and dynamic price adjustments. However, the challenges faced include data security risks, personal identification risks, and compliance with global privacy regulations. Assessment of the readiness of Indonesian fashion industry companies to adopt big data involves evaluating technology infrastructure, data analysis skills, awareness of big data concepts, data security and privacy, understanding customer behavior, technology adoption, ability to process real-time data, budget availability, readiness for innovation, and acceptance of organizational culture. By considering these aspects, companies can evaluate the extent to which they are ready to integrate big data in marketing decision making to achieve success in the Industrial Revolution 4.0 era.

This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.

Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application:The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems.

The rapid growth of global air traffic has made optimizing passenger flow in airports a critical challenge for the aviation industry. Efficient management of airport infrastructure is essential for minimizing wait times, enhancing passenger comfort, and improving operational efficiency. This paper explores mathematical-statistical modeling techniques and technological solutions, including the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, for optimizing passenger mobility in the landside area of airports. A key aspect of this optimization is the development of an algorithm for dynamic passenger traffic management, designed to analyze real-time data and facilitate proactive decision-making. The proposed algorithm, implemented in MATLAB, employs a Poisson distribution model to centralize, sort, and filter passenger data. By analyzing distinct time intervals, the system identifies congestion hotspots and enables dynamic resource allocation, reducing processing delays and preventing overcrowding. Real-world applications of similar methodologies, such as those implemented at Schiphol Airport and Hong Kong International Airport, have demonstrated significant improvements in operational efficiency, including reduced wait times and optimized baggage handling processes. The paper further examines global trends in airport digitalization, multimodal transportation integration, and the increasing role of AI-driven predictive analytics. Additionally, statistical methods such as ARIMA, SARIMA, and machine learning-based neural networks are evaluated for their effectiveness in forecasting passenger flow and optimizing airport resources. The findings highlight the economic and operational benefits of intelligent passenger flow management, including reduced operational costs, improved airport revenue from commercial areas, and enhanced passenger satisfaction. By integrating real-time monitoring with predictive modeling, airports can maximize infrastructure utilization and provide a seamless travel experience. This research contributes to the development of future-ready airports by proposing scalable solutions for managing increasing passenger volumes through advanced analytics and automation.

The transition from haute couture to prêt-à-porter in the 1960s created a great revolution in terms of both production and consumption for the fashion industry. One of the most relevant aspects of this process was the emergent demand for predicting future trends, in order to acquire a higher level of competitiveness compared to rivals. This time is considered the birth of a structured activity known as fashion forecasting, managed by the bureaux de style and embodied in trendbooks. In recent times this activity is often referred to as 'coolhunting,' a striking term that implies an idea of research as intuitive 'hunt' for incipient signals in fashion and reflected in consumers' lifestyles. This chapter considers coolhunting, a developing professional activity, as an emblematic place of symbolic mechanism, which is crucial to explaining not only the fashion system but also many cultural processes of production and consumption of material goods rich in immaterial contents. While coolhunting is historically rooted in fashion forecasting, it is also characterised by a relevant set of novelties, above all the shifting of research from a monolithic interest in fashion fads to socio-cultural trends that involve the whole symbolic imagination of the customer. A second important point is how the fashion forecasting model has extended to many branches of cultural production that are increasingly involved in a trend-oriented logic. This chapter critically examines coolhunting. It asks: what impact does such a practice have on the geography of trends? And how does it contribute to the social construction of authenticity and taste barriers between the mainstream and alternative niches? Coolhunters emerge as 'messengers of distinction,' the matching point between the consumers' need of distinction and the producers' attempt at creating distinctive goods.

This chapter provides a brief overview of the growing role played by big data, data analytics, and artificial intelligence (AI) in the accounting profession. The term 'Big Data' along with other trending topics such as 'Data Analytics' and 'Artificial Intelligence (AI)' have become buzzwords in the accounting profession in recent years. The skills of accounting professionals have evolved as technology has made rapid advances from using pencil and paper to typewriters and calculators, and eventually spreadsheets and accounting software. Data analytics in accounting is a relatively new skill set that is growing significantly in all areas of the accounting profession. Accounting professionals who can discern patterns and trends in big data and translate them into compelling strategic narratives will find themselves at the center of the twenty-first-century business world. Therefore, accounting professionals can capitalize on numerous opportunities in this rapidly evolving, disruptive, but ultimately advantageous environment by embracing big data, data analytics, and artificial intelligence (AI) to stay ahead of the competition.

This chapter explores how cutting-edge technologies, such as big data analytics, digital twins, and blockchain, are revolutionizing the green finance industry. It examines how these technologies' convergence improves sustainable financial practices efficiency, transparency, and decision-making while providing a fresh viewpoint on their complementary effects. The chapter describes the distinct functions of digital twins in asset optimization and risk management, blockchain in guaranteeing security and trust, and sophisticated data analytics in facilitating well-informed financial decisions.

The primary aim of this study is to explore how Artificial Intelligence can enhance the effectiveness of program performance evaluations. By leveraging data-driven techniques, the research aims to identify methods that facilitate more accurate assessments of program outcomes using LLM models, thereby enhancing decision-making processes. The study adopts a mixed-methods design, combining qualitative and quantitative approaches to assess the impact of Artificial Intelligence on program performance evaluation. The research was conducted over twelve months, enabling a detailed analysis of both the immediate and long-term impacts of Artificial Intelligence interventions on program management. The methodology employed in this study is structured around a comprehensive approach to data collection and analysis, ensuring robust insights into program performance evaluations. Qualitative research was conducted to identify relevant metrics for assessment. The qualitative component encompasses in-depth interviews with key stakeholders, providing insights into the contextual factors that influence analytics deployment. Concurrently, the quantitative analysis employs statistical methodologies to evaluate performance metrics both before and after the implementation of AI-driven methodologies. The study's findings highlight the critical role of Artificial Intelligence in enhancing program performance evaluation. Detailed data analysis revealed that employing Artificial Intelligence facilitates the extraction of real-time insights, which significantly assist in strategic decision-making. Programs that integrated advanced Artificial Intelligence and data analytics tools showed improved capability in identifying trends, directly impacting their effectiveness and adaptability. The study concludes that Artificial Intelligence and data analytics enhance program performance evaluations. By providing dynamic, real-time insights and risk assessments, the utilization of Artificial Intelligence and data analytics significantly improves decision-making processes and aids in strategic planning. It emphasizes the importance of robust data quality and governance practices that ensure the accuracy and reliability of evaluations.

Small and medium‐sized enterprises (SMEs) often face unique challenges in integrating advanced digital technologies due to limited resources and organizational constraints. While much attention has been given to large firms' adoption of big data analytics (BDA) and artificial intelligence (AI), less is known about how SMEs can leverage these tools effectively. This study addresses the research question of how SMEs, especially SaaS B2B, can transform marketing processes through advanced data analytics, large language models (LLMs), and generative AI. Drawing on a qualitative case study of a SaaS company, this research proposes a framework for integrating web mining and LLMs into a semantic analysis process to improve client acquisition by better targeting potential clients, i.e., leads. It optimizes lead qualification and facilitates the replication of successful cases by automating data collection, generating tailored marketing content, and creating customized commercial proposals. Based on the business analytics success model (BASM) framework, our study investigates the organizational changes necessary to embed innovative data‐driven business processes into existing workflows through process mapping. Key stakeholders evaluate the proposal via interviews, which discuss the barriers to aligning and opportunities associated with adopting AI‐driven solutions and emphasize the importance of technological capabilities with business goals. The findings reveal that for SMEs, adopting advanced analytics is contingent upon overcoming resource constraints through targeted organizational adjustments, such as incorporating dominant logic identification into strategic planning. This research contributes theoretically by customizing the BASM for SaaS SMEs and offering a roadmap for implementing AI‐driven marketing solutions, demonstrating how SMEs can enhance operational efficiency and achieve competitive advantages, even in resource‐constrained environments.

Education institutions utilize scalable computing to handle ever-increasing volumes and varieties of data effectively. The study shows how scalable computing makes it easier to manage resources and helps change learning environments in real-time, leading to better and more efficient education. This research utilizes scalable computational tools to investigate how business education embeds machine learning and big data analysis within its innovation strategy and practices. Their performance on an educational level determines one’s ability to contribute to economic and social development. Traditional business education faces issues such as cultivating experiences, accurately measuring student achievement and adapting quickly to new market expectations. Hence, this study proposes the statistical learning analysis for policy data analysis (SLA-PDA), which uses machine learning techniques, big data analytics, and scalable computing to analyze educational data, find trends, predict, and develop personalized learning strategies. The framework enhances decision-making capacities in educational practice by making decisions based on insights from data. Simulation: An exhaustive examination of the outcomes confirms the validity of the proposed methodology and indicates that vocational education programs can be made more standardized and effective overall. Considerations highlight how the SLA-PDA can be used in various educational settings, such as curriculum design, student performance evaluation, and resource allocation. Findings from this study indicate that employee training should be enhanced using advanced data analytics and machine learning. The proposed SLA-PDA methodology achieves a 96.3% accuracy in analyzing student performance, 96.8% in improving student progress assessment, 97.52% in resource allocation, 98.15% in integration for decision-making, and 98.16% in scalable computing within the virtualized school context.