Current Industry Practice Research Articles

The Role of Artificial Intelligence (AI) in Improving Inventory Management and Demand Forecasting in the E-Commerce Sector: Research on Bangladesh Perspective

The rapid growth of e-commerce in Bangladesh has created significant opportunities for businesses to expand their reach and serve a larger customer base. However, challenges such as inefficient inventory management, inaccurate demand forecasting, and logistics inefficiencies continue to hinder the sector's growth and profitability. Artificial intelligence (AI) offers an innovative solution to address these challenges by enabling data-driven decision-making, real-time monitoring, and predictive analytics. This study investigates the role of AI in improving inventory management and demand forecasting in Bangladesh's ecommerce environment. The study examines how AI technologies such as machine learning, predictive analytics, and automation can optimize inventory levels through more accurate demand forecasting, reduce operational costs, and improve customer satisfaction. By analyzing current practices in Bangladesh's e-commerce industry, the study identifies key gaps in AI adoption and highlights barriers such as lack of infrastructure, high implementation costs and limited technical expertise. Furthermore, the paper examines success stories of AI adoption in global e-commerce markets and assesses their applicability to Bangladesh. The results indicate that AI-powered inventory and forecasting systems can significantly improve the efficiency and competitiveness of e-commerce enterprises in Bangladesh. However, collaboration among stakeholders, investments in AI infrastructure and capacity building are essential to achieve these benefits. The study concludes with practical recommendations for integrating AI solutions in Bangladesh's e-commerce ecosystem and highlights the need for policy support and technological advancements to foster sustainable growth.

Resource Sharing on the Internet: A Comprehensive Survey on ISP Peering

Peering has rapidly emerged as a critical element of the Internet architecture in ensuring low latency, bandwidth efficient and high Quality-of-Service operation for Internet Service Providers (ISPs). In this paper, we delve into the multifaceted domain of peering, providing a comprehensive overview of its fundamental elements. We investigate the various peering models and policies available to ISPs, as well as key players of the ecosystem. We also explore security risks associated with peering and available safeguards to mitigate them. Our goal is to review current industry practices and research literature to shed valuable insights into the complexities of ISP peering.

Prediction of pipe-jacking forces using a physics-constrained neural network

Purpose A common design driver for pipe-jacking projects is the jacking force required to advance the tunnel boring machine and pipe string. Empirical methods are popular in industry but are well known to lack accuracy, while there is a strong desire to supplement such approaches with robust data-driven techniques, typically small construction datasets present significant challenges. Design/methodology/approach To address this challenge, this paper develops a physics-constrained neural network predictive model for pipe-jacking forces. Information used as input into the model includes principal design information and soil type. Findings The physics constrained model was found to predict jacking force to a higher accuracy than current industry practice and better discern meaningful patterns in data than a purely data-driven artificial neural network. The results reveal promising performance for this initial dataset such that there is motivation, as a longer-term objective, to train the present approach on a more comprehensive drive database for more reliable and cost effective solutions for new projects. Originality/value Novel contributions include (a) a bespoke framework to constrain a neural network using a pipe-jacking mechanistic model which includes stoppage-induced friction increases, (b) built-in model uncertainty for greater confidence in model outputs, (c) new historical drive data for model training and (d) one-hot encoding of soil type as a model input. The model is calibrated and validated against 14 tunnel drives across four different sites with four distinctive ground types.

Research on Artificial Intelligence Applications in Global Supply Chains

This research examines the transformative role of artificial intelligence (AI) in global supply chain management, analysing its applications, benefits, and challenges across various supply chain functions. Through a comprehensive analysis of current implementations and industry practices, the study reveals how AI technologies are revolutionizing supply chain operations through enhanced demand forecasting, inventory optimization, logistics management, and supplier risk assessment. The research identifies key success factors in AI implementation while addressing critical challenges including data privacy concerns, technical integration difficulties, cost barriers, and ethical considerations. The findings demonstrate that successful AI integration in supply chains requires a balanced approach combining technological innovation with careful consideration of organizational readiness, data security, and ethical implications. The study concludes by proposing strategic recommendations for organizations seeking to leverage AI in their supply chain operations and highlighting future development directions as AI technology continues to evolve. But the insights from industry leaders like Amazon and Alibaba may not fully apply to SMEs, which face unique challenges such as limited resources and technological readiness, highlighting the need for future research on scalable, cost-effective AI solutions and collaborative strategies to support their adoption.

Influencer marketing unlocked: Understanding the value chains driving the creator economy

As influencer marketing evolves into a dominant force in the marketing landscape, it necessitates a deeper theoretical exploration to understand its strategic implementations and impacts. This article examines the dynamics of influencer marketing within the growing creator economy, emphasizing the interactions among firms, influencers, followers, and digital platforms. We introduce a novel, equity-driven framework that analyzes how influencers contribute to customer equity, how influencers manage and leverage the value from their followers, and how platforms maximize the value from their users. We detail the complex relationships and value exchanges within the influencer marketing ecosystem, highlighting the challenges of measuring the return on investment and influencers’ strategic use of content to maintain authenticity and influence. By synthesizing diverse academic literature and current industry practices, this manuscript provides a comprehensive overview of the mechanisms of value creation and exchange in influencer marketing, offers strategic implications for marketers aiming to optimize their influencer engagements, and outlines future work in the form of the eleven “INFLUENCERS” research directions.

Current Industry Practices on Closed System Drug-Transfer Devices for Parenteral Drug Products.

USP<800> and NIOSH provide guidance on the use of Closed System Drug-Transfer Devices for hazardous drug in the healthcare setting. However, CSTDs are used by clinical sites irrespective of drug hazard status. In this paper, previous publications that describe evaluation and CSTD risk assessment strategies are presented. In addition, a pharmaceutical industry survey was performed to describe the current strategies for evaluation of CSTDs and how these devices are managed for clinical studies. Finally, recommendations are proposed to mitigate risks associated with CSTDs. These recommendations incorporate: testing considerations, risk assessments, and communication.

π-Stacked organic heterojunction enabled efficient hydrogen peroxide photoproduction.

Hydrogen peroxide (H2O2) finds wide application in disinfection, chemical production, and bleaching owing to its sustainability and ease of storage compared to hydrogen. However, the current industrial engineering practices for producing photocatalysts used in H2O2 generation face challenges such as low yields with organic catalysts and high costs associated with inorganic catalysts. In light of these challenges, we investigate a viable method utilizing a simple condensation reflux technique to synthesize pure organic heterojunction photocatalysts featuring a π-π stacked structure derived from graphitized carbon nitride (melem) and meso-tetrakis (4-carboxyphenyl) porphyrin, enabling efficient H2O2 production without the necessity of sacrificial agents. The synthesized catalyst demonstrates an impressive H2O2 production rate of 106mmol/h.g.L under seawater conditions and 77.67mmol/h.g.L under pure water conditions in ambient air. Coupling to a solar evaporator, an H2O2 concentration reaches 0.88wt%. Transient absorption spectroscopy revealed ultrafast charge separation which enabled efficient photocatalysis.

Cultivated meat microbiological safety considerations and practices.

Cultivated meat, produced using cell culture technology, is an alternative to conventional meat production that avoids the risks from enteric pathogens associated with animal slaughter and processing. Cultivated meat therefore has significant theoretical microbiological safety advantages, though limited information is available to validate this. This review discusses sources and vectors of microbial contamination throughout cultivated meat production, introduces industry survey data to evaluate current industry practices for monitoring and mitigating these hazards, and highlights future research needs. Industry survey respondents reported an average microbiological contamination batch failure rate of 11.2%. The most common vectors were related to personnel, equipment, and the production environment, while the most commonly reported type of microbiological contaminant was bacteria. These will likely remain prominent vectors and source organisms in commercial-scale production but can be addressed by a modified combination of existing commercial food and biopharmaceutical production safety systems such as Hazard Analysis and Critical Control Points (HACCP), Good Manufacturing Practices (GMP), and Good Cell Culture Practice (GCCP). As the sector matures and embeds these and other safety management systems, microbiological contamination issues should be surmountable. Data are also included to investigate whether the limited microbiome of cultivated products poses a novel food safety risk. However, further studies are needed to assess the growth potential of microorganisms in different cultivated meat products, taking into account factors such as their composition, pH, water activity, and background microflora.

Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms

Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhance fermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.

Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon.

Wild Atlantic salmon migrate to sea following completion of a developmental process known as parr-smolt transformation (PST), which establishes a seawater (SW) tolerant phenotype. Effective imitation of this aspect of anadromous life history is a crucial aspect of commercial salmon production, with current industry practice being marred by significant losses during transition from the freshwater (FW) to SW phase of production. The natural photoperiodic control of PST can be mimicked by exposing farmed juvenile fish to a reduced duration photoperiod for at least 6 weeks before increasing the photoperiod in the last 1-2 months before SW transfer. While it is known that variations in this general protocol affect subsequent SW performance, there is no uniformly accepted industry standard; moreover, reliable prediction of SW performance from fish attributes in the FW phase remains a major challenge. Here we describe an experiment in which we took gill biopsies 1 week prior to SW transfer from 3000 individually tagged fish raised on three different photoperiod regimes during the FW phase. Biopsies were subjected to RNA profiling by Illumina sequencing, while individual fish growth and survival was monitored over 300 days in a SW cage environment, run as a common garden experiment. Using a random forest machine learning algorithm, we developed gene expression-based predictive models for initial survival and stunted growth in SW. Stunted growth phenotypes could not be predicted based on gill transcriptomes, but survival the first 40 days in SW could be predicted with moderate accuracy. While several previously identified marker genes contribute to this model, a surprisingly low weighting is ascribed to sodium potassium ATPase subunit genes, contradicting advocacy for their use as SW readiness markers. However, genes with photoperiod-history sensitive regulation were highly enriched among the genes with highest importance in the prediction model. This work opens new avenues for understanding and exploiting developmental changes in gill physiology during smolt development.

Impact of Message Framing on Consumer Decision Making in FMCG Industry

Framing effect refers to the cognitive bias in which decisions are made depending upon how information is being presented. It is crucial for a marketer to acknowledge how communication translates into behavior, and how the presentation of information can affect purchase decision making. This study focused on the fast-moving consumer goods (FMCG) industry, an industry with huge potential and considerable risks. The study aimed to observe and analyze the effectiveness of message framing on affecting consumers purchase decision making. This study utilizes a survey as the primary data collection tool with a total of 385 respondents. Logistic regression is used to determine the effect of types of frames, content of messages, and focus of frames on purchase decision making. Results indicated that message framing is proven to be effective in affecting Indonesian purchase decision making in the FMCG Industry. Types of frames do not significantly affect the effectiveness of framing on Indonesian consumers in the FMCG industry. However, the content of message and focus of frames do contribute to consumers' purchase decision making. It is recommended that marketers should understand other relevant factors of message framing including current industry practice, personal connection, and combination of frame types, content of messages, and focus of frames.

Unveiling the Contemporary Research Direction and Current Business Management Strategies for Port Decarbonization Through a Systematic Review

Port decarbonization is becoming an increasingly critical focus in the maritime industry. It facilitates prioritizing compliance with the latest regulatory updates introduced by the International Maritime Organization, the European Union, and national governments. It is also seen as a key element to tackling climate change challenges. Hence, it is essential to comprehend recent developments in port decarbonization research to address sustainability requirements of the industry. This article reveals the recent research directions and current industrial practices in response to port decarbonization requirements by employing a holistic view of the literature. The methodology of this research uses a detailed qualitative approach framework to conduct an in-depth analysis of contemporary research publications. In this study, 75 recent publications in the literature are examined in-depth using systematic review, thematic analysis, and content analysis. The findings of this study suggest a significant emphasis on innovative technologies and collaborative strategies for achieving decarbonization goals. Another important contribution of this study is the identification of key barriers and facilitators in the implementation of decarbonization practices at ports. This study also encompasses future research directions by identifying existing themes, contents, and research gaps in the literature. It is anticipated that the outcomes of this article will shed light on the main research motivations and policymaking requirements in the field of port decarbonization.

The Two Powers: How Pascal and Python Shaped Programming Education

The choice of programming language for education is an intensely debated topic. On the one hand, the programming language is supposed to be relevant in that its organisation, structures, and paradigms adhere to current standards and best practices in industry and academia. On the other hand, the programming language is expected to be simple, that is, easy to use and, of course, easy to learn. This increasingly means that we need an introduction to concepts shaped by a long history without all the historic baggage. The flip side of these requirements of an educational programming language is its power to shape thought patterns, mental models, and best practices of the students, and thus of their future. Moreover, the aspect of simplicity may have the power to decide whether programming will become democraticed or remain the realm of a few highly paid specialists. From the many programming languages that have been used in programming education, few stand out for being as popular and widely used as Pascal and Python. Decades apart, both have been designed with simplicity and relevance in mind, and yet it seems they could not differ more in so many design choices, most notably typing. In this article we therefore consider the similarities and differences between Pascal and Python with emphasis on an educational perspective. We identify core features that might explain their suitability for education, compare their strengths and weaknesses, and discuss to what extent the premises behind their design still apply to the future of programming education.

Real-Time Data Analytics in Customer Experience Management: A Framework for Digital Transformation and Business Intelligence

Real-time data analytics has emerged as a transformative force in customer experience management, enabling organizations to process and respond to customer interactions instantaneously. This article presents a comprehensive framework for implementing real-time analytics in customer-centric business operations, examining both theoretical foundations and practical applications across multiple industries. Through systematic analysis of implementation strategies, technical architectures, and organizational challenges, this article illuminates the critical role of real-time analytics in enabling personalized customer experiences, dynamic pricing optimization, and predictive service delivery. The article synthesizes current industry practices while identifying key success factors and potential barriers in real-time analytics adoption. Findings indicate that organizations leveraging real-time analytics capabilities demonstrate enhanced customer satisfaction, improved operational efficiency, and strengthened competitive positioning. This article contributes to both theoretical understanding and practical implementation of real-time analytics in customer experience management, providing actionable insights for practitioners while identifying promising directions for future research. The article concludes with a structured framework for organizations seeking to implement or enhance their real-time analytics capabilities in pursuit of superior customer experience.

Shear Failure Assessment of a G+3 RC Framed Building via Nonlinear Static Pushover Analysis using SAP2000

Accurately modelling shear behaviour in reinforced concrete (RC) structures is essential for seismic evaluation, as shear failure can lead to catastrophic outcomes. Current industry practices often emphasize nonlinear flexural analysis while underestimating the critical role of shear behaviour. This study aims to develop a nonlinear force-deformation model specifically for shear in RC sections to bridge the gap in existing literature. Standards like IS-456:2000 and ACI-318:2008 provide ultimate strength estimates but may inadequately consider the contribution of concrete under seismic loads. Insights from models by Priestley et al. (1996) and Park and Pauley (1975) are integrated to improve the calculation of shear hinge properties. A comparative nonlinear static (pushover) analysis of an RC framed building, with and without shear hinges, demonstrates the impact of shear modelling. Results show that neglecting shear hinges overestimates base shear and roof displacement capacity, masking non-ductile failure modes. Incorporating shear hinges provides a more realistic assessment of structural strength and ductility, emphasizing their necessity in seismic analysis and design. Keywords: Shear behaviour, Reinforced concrete (RC) structures, Seismic evaluation, Shear failure, Nonlinear analysis, Force-deformation model, Shear hinge.

Navigating Fish Immunity: Focus on Mucosal Immunity and the Evolving Landscape of Mucosal Vaccines

The growing role of aquaculture in global food security has underscored the need for advanced immunological insights to protect fish health and boost productivity. As aquaculture’s importance rises, understanding fish immunity is crucial for developing effective vaccination strategies. Fish possess a specialized immune system with unique mucosal structures that enable resilience in aquatic environments. This review examines critical advances in fish mucosal immunity, particularly focusing on mucosal vaccines that target infection at primary entry points, such as the gills, skin, and gastrointestinal tract. Mucosal vaccination has demonstrated a compelling capacity to stimulate localized and systemic immune responses, offering enhanced protection against waterborne pathogens. Additionally, this review addresses knowledge gaps from previous research on the global aquaculture vaccines market by offering a regional perspective on industry developments, recent trends, and innovative vaccine formulations. In doing so, it highlights the role of mucosal vaccines in overcoming the specific challenges of fish farming and supporting sustainable aquaculture. This synthesis of current methodologies, industry practices, and future directions contributes to a deeper understanding of fish immunology, ultimately informing strategies to achieve optimal disease management and bolster global aquaculture resilience.

The New Frontier of Ad Analytics: Privacy-Centric Approaches to Campaign Measurement and Optimization

This article explores the transformative shift in digital advertising measurement techniques in response to growing privacy concerns and regulatory pressures. It examines the transition from traditional third-party cookie-based tracking to advanced data modeling approaches that prioritize user privacy. The article provides a comprehensive overview of privacy-preserving ad measurement techniques, including aggregated and anonymized data analysis, probabilistic attribution models, and differential privacy. It delves into Big Tech's advancements in conversion modeling, highlighting the role of machine learning in developing more accurate and privacy-compliant attribution methods. The implications for advertisers and marketers are discussed, addressing the challenges of adapting to this new paradigm and strategies for optimizing campaigns using modeled data. Ethical considerations and the importance of building consumer trust are emphasized, underscoring the need for transparency and responsible data stewardship. By synthesizing current research and industry practices, this article offers insights into the future of ad measurement in a privacy-centric digital ecosystem, demonstrating how the industry can balance effective campaign analytics with robust privacy protection to create a more sustainable and trustworthy advertising landscape.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

Best Practices for Designing Scalable REST APIs in Cloud Environments

This research paper explores the best practices for developing scalable Representational State Transfer (REST) APIs in cloud environments. As the demand for robust and high-performance APIs continues to grow, developers face numerous challenges in designing and implementing scalable solutions. This study examines various aspects of API development, including architectural principles, cloud-native technologies, performance optimization techniques, and security considerations. By synthesizing current research and industry practices, this paper provides a comprehensive guide for practitioners and researchers in the field of API development for cloud environments.

Advanced earth grid monitoring for HV network safety compliance

ABSTRACT Electrical energy is a critical component for human comfort, but mismanagement of this energy can lead to human injury and even fatalities. High voltage safety compliance hinges on the presence of an adequate earthing system within the infrastructure. Various factors influence the effectiveness of the earthing system, including soil resistivity in the surrounding area and the availability of auxiliary paths for fault currents. Weather conditions, changes in the surrounding environment, and alterations within the high-voltage network significantly impact these elements. Any modifications to the auxiliary path conditions can have severe consequences on the network’s safety compliance. The current industry practice lacks continuous monitoring of the earth grid and fault current paths, with no provision for frequent current injection tests on the entire earth grid system. To close the existing gap, this paper proposes a novel approach through the introduction of an Earthing IoT Monitoring system. This system enables network owners to monitor safety compliance across the entire network, identify fault current return paths, and pinpoint any necessary preventive maintenance measures. The paper also delves into the crucial roles played by soil resistivity and split factors in the earthing system domain. A case study is included to support the innovative nature of the proposed system.