Predictive Analytics in SAP for Demand-Driven Inventory Optimization

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.

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.

Preventing workplace injury is critical, however to effectively target preventative activities we need an understanding of the future risk of a workplace. Innovative methods from predictive analytics offer an opportunity...

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.

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

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.

Demand forecasting in e-commerce procurement systems is central to operational efficiency, customer satisfaction, and cost reduction. Accurate forecasting enables organizations to anticipate fluctuations in consumer demand, align procurement strategies, optimize inventory levels, and minimize supply chain risks. Traditional forecasting methods, while valuable, often fail to capture the complexities of modern e-commerce, where purchasing patterns are dynamic, influenced by seasonality, promotions, social media trends, and external shocks. Predictive analytics has emerged as a transformative approach, leveraging machine learning, statistical modelling, and big data analytics to enhance forecasting accuracy. By exploiting diverse data sources including transaction records, clickstream behaviour, demographic information, and external market signals predictive models enable organizations to move from reactive to proactive procurement management. This study conducts a comprehensive literature-based review of predictive analytics approaches in e-commerce demand forecasting up to 2020. It examines methodological developments, challenges, and applications across procurement systems, highlighting the integration of regression models, time-series forecasting, machine learning, and deep learning algorithms. The review identifies trends toward hybrid and ensemble models, the growing role of real-time analytics, and the challenges of scalability, data quality, and interpretability. The findings underscore that predictive analytics holds substantial potential in improving demand forecasting, but its successful adoption requires careful alignment with organizational goals, robust data governance, and cross-functional collaboration. The study provides a structured synthesis of existing scholarship and offers insights into the implications for e-commerce procurement systems seeking to enhance forecasting accuracy in volatile, data-rich environments.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

In the last years Machine Learning (and the Artificial Intelligence), is experiencing a new rush thanks to the growth of volume and kind of data, the presence of tools / software with higher computational power and cheaper data storage size (e.g. cloud). In Credit Risk Management, the PD (Probability of Default) estimation has attracted lots of research interests in the past literature and recent studies have shown that advanced Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods tied to simplified Machine Learning techniques. The study empirically investigates the results of applying different advanced machine learning techniques in estimation and calibration of Probability of Default. The study has been done on big data sample with more than 800,000 Retail customers of a panel European Banks under ECB Supervision, with 10 years of historical information (2006 - 2016) and 300 variables to be analyzed for each customer. The study shows that neural network produces a higher population riskiness ranking accuracy, with 71% of Accuracy Ratio. However, the authors’ idea is that classification tree is more interpretable from an economic and credit point of view. In terms of model calibration, cluster analysis produces rating classes more stable and with a predicted risk probability aligned with the observed default rate.

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably.

This chapter examines the increasing use of predictive analytics technologies by police. Such tools analyze data to identify patterns and make predictions about crime. Champions of predictive policing see it as valuable for preventing crime, but it also raises a number of ethical concerns. Since these technologies rely on historical data—often infected by racial bias—it can reproduce bias in its predictions and recommendations. An even more basic worry about using predictive analytics for preemptive policing is that this approach makes harmful assumptions about individuals and fails to recognize them as ethical agents. Preemptive policing aims to reduce overall crime, but does so by treating suspects and at-risk individuals in an instrumental fashion. Predictive analytics tools perhaps, then, best belong in the hands of those with more direct obligations to at-risk individuals—like social workers.