4F.004 Predictive Intelligence to Prevent Workplace Injury

In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales forecasting in online marketplace platforms is also explored, through the examination of various predictive models using real-world case studies. By exploring various methodologies and tools, the study illustrates how predictive analytics can be integrated into e-businesses' operations to drive growth, and enhance decision-making, highlighting the transformative potential of analytics in making data-driven decisions, ultimately fostering sustainable growth and competitive advantage. Through the analysis of historical sales data, consumer behaviour patterns, and market trends, predictive analytics provides actionable insights that are crucial for strategic planning and operational efficiency. The paper also addresses challenges and best practices for implementing predictive analytics into the business process of e-businesses. ‍

The last-mile delivery problem is one of the most complex and resource-intensive aspects of modern logistics, especially within the growing e-commerce sector. As online shopping continues to expand, companies are under immense pressure to deliver goods more quickly, efficiently, and at lower costs, all while meeting the demands of increasingly time-sensitive customers. This has created a need for innovative solutions that can tackle challenges related to dynamic traffic patterns, fluctuating customer preferences, and operational constraints such as vehicle capacities and delivery windows. In response to these challenges, this paper explores the application of predictive analytics as a tool for optimizing last-mile delivery routes in real-time.The study begins by identifying the core challenges inherent in last-mile logistics, particularly in the U.S. e-commerce landscape, where the cost of last-mile delivery can represent up to 53% of total shipping costs. With traffic congestion, unpredictable customer availability, and delivery time constraints posing significant hurdles, conventional static route planning models often fall short. In this paper, predictive analytics is proposed as a solution to these challenges, utilizing real-time data to inform more efficient routing decisions. By processing vast amounts of real-time traffic data, customer preferences, and delivery constraints, predictive models can offer a more flexible and responsive approach to last-mile delivery.The research then presents a comprehensive literature review of existing route optimization methods, such as the traditional Vehicle Routing Problem (VRP) and its extensions, including VRP with Time Windows (VRPTW), Dynamic VRP (DVRP), and Capacitated VRP (CVRP). While these models have proven useful, their limitations are exposed when faced with real-time operational complexities in the e-commerce sector. Therefore, this study introduces an advanced dynamic routing model that integrates machine learning algorithms—such as decision trees and neural networks—with traditional VRP frameworks. These machine learning models, trained on historical data, are capable of predicting future traffic patterns, customer behavior, and delivery time windows.A case study is conducted using data from U.S.-based e-commerce companies to demonstrate the practical application of predictive analytics in optimizing last-mile delivery. The case study outlines how predictive models are used to dynamically adjust delivery routes based on real-time conditions, leading to significant improvements in efficiency, cost savings, and customer satisfaction. Key performance indicators such as delivery times, fuel consumption, and vehicle utilization are examined before and after the implementation of the predictive models, with the results showing a reduction in delivery time by 20% and fuel costs by 15%, alongside improved on-time delivery rates.The paper concludes by presenting the proposed dynamic route optimization model as a solution that combines the flexibility and responsiveness of predictive analytics with the robust framework of traditional VRP models. Through the integration of machine learning, real-time data processing, and dynamic routing, the model is shown to significantly improve last-mile delivery efficiency. This study's findings highlight the potential for predictive analytics to revolutionize the logistics industry, particularly in the high-demand e-commerce sector, where quick and reliable delivery is paramount. The research suggests that as e-commerce continues to grow, predictive analytics will play an increasingly critical role in ensuring that last-mile delivery is both cost-effective and responsive to the evolving needs of consumers.

The recent COVID-19 outbreak has caused a high threat to the medical sciences across the world. About 39,596,858 confirmed cases and 1,107,374 deaths were witnessed as of 18 October 2020 by the World Health Organization. Although several strategies have been adopted for drug discovery, a promising drug/vaccine has not been developed. Conventional drug discovery is a time-consuming and expensive process and therefore adopting computational predictions would contribute to quicker and cost-effective drug discovery. Computational intelligence has played a significant part during the pandemic by designing applications for tracking of the community spread of the infection, processing and analyzing chest images and also developing computational tools and software for predictive analysis that contribute to the drug discovery channel. In this chapter, we provide a brief overview of the applications of computational intelligence and predictive analytics in screening potential lead molecules against COVID-19 with an emphasis on the drug repurposing. Bioinformatic tools and software are associated with the molecular targets, especially protein structure prediction, energy minimization of the predicted structures, binding site predictions, different ligand databases, virtual screening based on drug likeliness, pharmacokinetic and toxicity predictions, molecular docking and interaction modeling analysis along with various computational biology tools and software. This chapter also highlights the predictions of the binding potential of the few antibiotics that can be repurposed against the severe acute respiratory syndrome-coronavirus-2 targets by several computational biology tools and resources. Therefore, this chapter certainly enlightens with various approaches to computational intelligence and predictive analytics that would aid in fastening the process of drug discovery.

Predictive Analytics in SAP for Demand-Driven Inventory Optimization is a crucial strategy for enhancing inventory management in dynamic supply chain environments. By integrating advanced analytics and machine learning capabilities, SAP enables businesses to forecast demand patterns, optimize stock levels, and reduce operational costs. This approach empowers organizations to predict fluctuations in demand with greater accuracy, ensuring that inventory levels are adjusted in real-time to meet customer needs while minimizing excess stock. The application of predictive analytics helps to streamline replenishment processes, reduce stockouts, and improve overall customer satisfaction. This paper explores the role of SAP's predictive analytics tools in optimizing inventory through demand-driven strategies, providing businesses with a competitive edge in an increasingly complex global marketplace.

Energy efficiency plays a crucial role in the field of industrial automation, particularly in relation to conveyor belt systems that operate continuously in various sectors, including mining, logistics, and manufacturing. Smart Industrial Components (SIC) are required to process, manage, and produce a substantial amount of different types of IIOT (Industrial Internet of Things) data. This historical data is the main source for analysing the current and future conditions of the components. In this paper, a predictive analytics algorithm is proposed to analyse the trend of real-time and future power consumption. The proposed algorithm seamlessly combines generative AI with IIoT data to enhance predictive modelling capabilities. This study highlights key challenges, methodologies, and future directions in leveraging AI-driven analytics for industrial energy management, as well as investigates the application of predictive data analytics for conveyor belt systems focusing on two key aspects: (1) historical data analysis to monitor the current condition of the conveyor belt system and (2) the use of generative AI for future prediction essential for analysing future trends of power consumption running at the specific condition.

Abstract: Predictive analysis has become an efficient tool and procedures now a day for many industries, organization, firms and service sectors. In sales, predictive analytics refers to the software and/or procedures that evaluate past and present sales data in order to forecast sales results and enhance efficiency. A vast amount of first-party and third-party customer/prospect data is available these days to sellers and marketers. Indeed, sellers and marketers frequently feel overpowered by the sheer amount of data when deciding how to leverage it to get desired outcomes. That issue is resolved by predictive analytics in sales, which uses artificial intelligence systems to assist sales representatives in understanding customer and sales pipeline information. Advances in data analytics are being used by an ever-increasing number of organizations to identify patterns and trends, increase productivity, and make more informed business decisions. In this paper, we will discuss and analyze the role and impact of the predictive analytics in sales.

The safety and health of the workplace is a decisive factor in making an organization function effectively. An emphatic worldwide endeavor is under way, applying a management framework, in order to implement cost-effective occupational health and safety (OHS) and assist governments and employer and employe representatives to better target their priorities in prevention of workplace injuries and diseases (Frick, Jensen, Quinlan, & Wilthagen, 2000). Revolving around the families of management standards, such as ISO 9001:2000 and ISO 14001:1996, 2004 the systems approach has drawn attention among the standards organizations, accreditation bodies, and the national regulatory agencies in formalizing, implementing, and evaluating OHS management systems (OHSMS). Several OHSMS frameworks have been proposed, aiming at building a documented approach to sustainable change and OHS improvement in the enterprises (International Occupational Hygiene Association [IOHA], 1998). Therefore, by conforming to an OHSMS, the organization can make visible its business objectives of quality management (Kara, 1996; Nag, 2002). This contribution elucidates the key elements of OHSMS and the issues surrounding the development of the OHSMS models, outlines the key documentation and implementation requirement, and certification of the management system, and briefly examines the global acceptance and challenges of the OHS programs and systems in the context of diverse national initiatives.

This research focuses on analyzing and improving maintenance process by exploring novel applications of process mining and predictive analytics. We analyze the software maintenance process by applying process mining on software repositories, and address the identified inefficiencies using predictive analytics. To drive our research, we engage with practitioners from large, global IT companies and emphasize on the practical usability of the proposed solution approaches, which are evaluated by conducting a series of case studies on open source and commercial projects.

INTRODUCTION. The increasing complexity of construction projects and growing competitive pressure in the industry have created a critical need to enhance planning methods and resour ce allocation in construction management. RESEARCH HYPOTHESIS. The introduction of integrated digital technologies, such as digital twins, artificial intelligence, the Internet of Things, and advanced planning methods, will significantly increase the efficiency of construction process planning, reduce costs and project execution time, while reducing risks and improving forecasting accuracy. THE AIM The purpose of this study was to develop a methodology for improving the efficiency of construction process planning through the integration of digital technologies and algorithmic optimisation methods. METHODS. The study was based on an analysis of the implementation of digital planning methods in seven leading international construction companies in 2023–2024, using data from technical documentation for deployed systems and financial statements. FINDINGS. The adoption of integrated digital platforms reduced planning time by 45–52% and minimised equipment downtime by 38–40%. The use of graph databases optimised the sequencing of construction operations, while predictive analytics improved forecasting accuracy for work deadlines to 92%. The integration of artificial intelligence (AI) and Internet of Things (IoT) technologies lowered logistics costs by 25–33% and reduced modelling (BIM) technologies, combined with collaborative planning systems, increased productivity by 25% and reduced schedule deviations by 32%. Furthermore, the development of a digital twin system enabled process modelling at the planning stage, facilitating early detection of potential issues and reducing error correction costs by 28%. The application of predictive analytics allowed a shift from reactive to proactive resource management through risk forecasting and automatic plan adjustments. CONCLUSION. The proposed phased methodology for digital solution implementation, incorporating a technology audit and personnel development programmes, can be applied by construction companies of various sizes to optimise planning processes and enhance operational efficiency. KEYWORDS: digital twins; intelligent systems; graph databases; manufacturing processes; collaborative planning.

The assessment of future students' employability by the Institute of Higher Learning in collaboration with career centres is one of the most crucial steps in the educational industry for establishing an active and ascendable plan. Predictive analysis for this project is done using machine learning. This study investigates the Employability Signals of Undergraduates in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria. The findings demonstrate that higher education was where the most accurate predictor of undergraduate students' employability was initially examined. The study's conclusions can be used to develop a roadmap that will make it simpler to use predictive analytics. The findings of this study may also facilitate the creation and application of predictive analytics, one of the possible approaches for analysing the education data gathered during the pre-covid period for this study. Systematic literature reviews should be trustworthy, repeatable, and valid when used in scientific investigations. As a result, the inquiry will reach a conclusion based on the evaluations found on pertinent and customized dates.

Safety is a major concern in the oil and gas industry due to accidents with the potential for catastrophic consequences. One of these was the Piper Alpha drilling platform disaster in 1988, which resulted in the loss of the Piper Alpha platform and the subsequently 167 lives; the worst disaster in the oil and gas industry. To foster a proactive approach in the prevention of workplace injuries, organisations have turned towards key predictors of safety, such as leadership. However, simply practising leadership behaviours is not enough to improve safety, especially in safety-critical organisations. An inability to move beyond the formal control inherent in traditional bureaucratic mindsets limits the applicability of mainstream leadership theories for safety-critical organisations. To address this challenge, this research study adopts a different paradigm for leadership, one which frames leadership as a complex interactive dynamic from which adaptive outcomes emerge as complexity science. Hence, the aim of this research study was to investigate the direct relationship between leadership behaviours and safety outcomes, and the mediating effect of complexity science principles. To accomplish this, a conceptual model was developed using theoretical foundation built from the literature review. The model comprises three main components: 1) leadership behaviours, 2) five complexity science principles (connectivity, interdependence, feedback, exploration-of-the-space-of-possibilities and co-evolution) and 3) three safety outcomes (safety participation, safety compliance and safety climate). These components were linked by the proposed hypotheses that were established based on assumed theoretical relationships. To assess and validate the conceptual model and test the hypotheses, a series of quantitative approaches were employed. A multivariate statistical approach was utilised for data collection through a questionnaire survey; 173 project managers participated in the survey. These individuals are responsible for emergency commands, as well as the day-to-day management of safety. Principal component analysis/Exploratory factor analysis was performed to identify the factor structures representing each underlying construct. Confirmatory factor analysis was conducted to validate the results obtained from the exploratory factor analysis, followed by multiple regression analysis. Next, structural equation modelling was applied to test the structural models for hypotheses testing. The regression analyses demonstrated that all three identified leadership behaviours — 1) empowering & engaging, 2) modelling & reinforcing, and 3) promoting & enabling — directly influenced the level of safety participation and safety compliance. Additionally, all five principles of complexity science mediated the relationship between empowering & engaging leadership behaviour, and safety participation and safety compliance. As for the hypothesised relationship between modelling & reinforcing leadership behaviour and safety outcomes, apart from the co-evolution principle, all other complexity science principles arbitrated the relationship with safety participation and safety compliance. Furthermore, the relationships between promoting & enabling leadership behaviour, and safety participation and safety compliance were mediated by all five principles of complexity science. Finally, structural equation modelling was used to refine and confirm the findings of the regression analysis, verifying both empowering & engaging, and promoting & enabling leadership behaviours have direct and indirect influences on safety participation and safety compliance levels as a result of all five complexity science principles. And established that modelling & reinforcing leadership behaviour has direct and indirect effects on safety participation and safety compliance levels under the influence of only four complexity science principles — namely, connectivity, interdependence, feedback and exploration-of-the-space-of-possibilities. On the basis of the research results, this study provides insight into the understanding of safety leadership by the empirical evidence that displayed the relationships between the three leadership behaviour constructs and their contribution to the effect of complexity science principles and safety outcomes within the oil and gas industry. From these results, the study provides a list of recommendations for research on the effect of leadership behaviours on complexity science principles and safety outcomes.

The growing reliance on data-driven decision-making across sectors has positioned predictive analytics as a core application of data science. Despite significant advancements in artificial intelligence and machine learning, existing predictive analytics studies often suffer from fragmented analytical pipelines and insufficient validation, limiting model reliability and real-world applicability. This study proposes and validates a structured data science framework for predictive analytics that integrates data preparation, feature engineering, model development, and validation within a unified methodological architecture. Adopting a quantitative, modeldriven research design, the study develops multiple predictive models and evaluates their performance through systematic validation procedures. The findings demonstrate that predictive models developed within the proposed framework exhibit improved accuracy, robustness, and generalizability compared to models developed using ad hoc approaches. The results further highlight the importance of continuous validation and comparative model assessment in mitigating overfitting and enhancing predictive stability. By emphasizing methodological coherence and validation rigor, the framework supports transparent and reliable decision-support systems applicable across domains such as education, healthcare, business analytics, logistics, and sustainability. The study contributes to data science literature by offering an empirically grounded framework that bridges theoretical principles with practical implementation, providing a replicable foundation for advancing predictive analytics research and application

This survey paper explores the application of machine learning (ML) techniques in the domain of workplace injuries, focusing on three key areas: risk prediction, return to work (RTW) strategies, and demographic analysis. Through an extensive review of literature from January 2015 to July 2024, this paper examines the latest advancements in ML-driven approaches to workplace safety and identifies important research gaps. This paper highlights how classical ML techniques, such as ensemble models and decision trees, have become essential tools for identifying workplace injury risks, enabling more accurate interventions. It emphasizes the importance of leveraging ML in personalized RTW programs, which use data-driven insights to improve recovery outcomes and reduce economic demands. In the context of demographic analysis, this paper explores how ML algorithms can uncover disparities in injury rates across various age groups, industries, and occupations, underscoring the need for targeted safety measures. Moreover, research gaps are identified, particularly regarding the emerging potential of advanced ML techniques, such as deep learning and large language models (LLMs), for analyzing structured and unstructured safety data, methods that have not yet been widely applied in workplace injury research. As such, future research should apply recent advances in ML, integrating these approaches with comprehensive and accessible datasets to enhance the prediction and prevention of workplace injuries, provide more detailed analytics and insights, and improve safety protocols across all industries. This comprehensive survey is an invaluable resource for researchers and practitioners leveraging ML to address complex challenges in workplace safety.

Artificial intelligence (AI) is revolutionizing occupational health and safety (OHS) by addressing workplace hazards and enhancing employee well-being. This review explores the broader context of increasing automation and digitalization, focusing on the role of human-AI interaction in improving workplace health, safety, and productivity while considering associated challenges. A narrative review methodology was employed, involving a comprehensive literature search in PubMed, Embase, and Scopus for studies published within the last 25 years. After screening for relevance and eligibility, a total of 52 articles were included in the final analysis. These publications examined various AI applications in OHS, such as wearable technologies, predictive analytics, and ergonomic tools, with a focus on their contributions and limitations. Key findings demonstrate that AI enhances hazard detection, enables real-time monitoring, and improves training through immersive simulations, significantly contributing to safer and more efficient workplaces. However, challenges such as data privacy concerns, algorithmic biases, and reduced worker autonomy were identified as significant barriers to broader AI adoption in OHS. AI holds great promise in transforming OHS practices, but its integration requires ethical frameworks and human-centric collaboration models to ensure transparency, equity, and worker empowerment. Addressing these challenges will allow workplaces to harness the full potential of AI in creating safer, healthier, and more sustainable environments.

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience