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Journal articles on the topic 'Predictive business decisions'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Puryskina, Valentina A. "Predictive analytics as a modern business development tool." Accounting and control 9 (2025): 37–51. https://doi.org/10.36871/u.i.k.2025.09.01.005.

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Modern businesses operate in a dynamically changing external environment, the complexity of which increases exponentially. This is due to the rapid growth of data volumes, the high rate of technological change, including digitalization of all spheres of society, and increased competition. In these circumstances, the ability of organizations to make informed management decisions becomes a key success factor. One of the most promising business development tools is predictive analytics, which allows predicting future events and trends based on data analysis. The use of predictive analytics opens

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Sachin, Kumar, Dube Devanshu, Prasad K. Krishna, and S. Aithal P. "Emerging Concept of Tech-Business-Analytics an Intersection of IoT & Data Analytics and its Applications on Predictive Business Decisions." International Journal of Applied Engineering and Management Letters (IJAEML) 4, no. 2 (2020): 200–210. https://doi.org/10.5281/zenodo.4151640.

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This study examines the emerging fields of data analytics and decision prediction using data collected across different systems using Internet of Things technology. The Internet of Things (IoT) is a collection of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transmit data across a network without needing human-to-human or human-to-computer interaction. A specified aim of predicting the future, along with the explanation of the problem using another high-tech system and model should be us

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Wang, Weihan. "Artificial Intelligence in Strategic Business Decisions: Enhancing Market Competitiveness." Advances in Economics, Management and Political Sciences 117, no. 1 (2024): 87–93. http://dx.doi.org/10.54254/2754-1169/117/20241987.

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Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, red

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Sneha, R. "Predictive Analytics: Paradigm Shift in Decision Making Process." Shanlax International Journal of Management, 6, S2 (2019): 66–70. https://doi.org/10.5281/zenodo.7106583.

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Data is the one of the important input in modern era to take decisions at the right time. Due to increase in the number of transactions, there is a storage of huge voluminous data. These data sets have to be handled carefully in order to get best out of it. Decision makers has to process these data in order to make decisions for their benefits. Analyzing these voluminous data, there is a requirement for the Big Data Analytics. These huge data consist of High Volume, Veracity, Variety, Velocity and value. The same has to be handled carefully and process judiciously in order gain best out of it.

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Edith Ebele Agu, Njideka Rita Chiekezie, Angela Omozele Abhulimen, and Anwuli Nkemchor Obiki-Osafiele. "Building sustainable business models with predictive analytics: Case studies from various industries." International Journal of Advanced Economics 6, no. 8 (2024): 394–406. http://dx.doi.org/10.51594/ijae.v6i8.1436.

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Predictive analytics has emerged as a powerful tool for businesses across various industries to build sustainable business models. This review provides insights into the significance of predictive analytics in fostering sustainability and showcases case studies from different sectors where predictive analytics has been effectively employed. Predictive analytics enables businesses to anticipate future trends, identify potential risks, and make data-driven decisions, thereby enhancing operational efficiency, improving customer experiences, and driving growth. By leveraging historical data and ad

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Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, and Andrew Ifesinachi Daraojimba. "Business analytics and decision science: A review of techniques in strategic business decision making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1761–69. http://dx.doi.org/10.30574/wjarr.2024.21.2.0247.

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Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation

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Chidera, Victoria Ibeh, Franca Asuzu Onyeka, Olorunsogo Temidayo, Adijat Elufioye Oluwafunmi, Leonard Nduubuisi Ndubuisi, and Ifesinachi Daraojimba Andrew. "Business analytics and decision science: A review of techniques in strategic business decision making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1761–69. https://doi.org/10.5281/zenodo.14041802.

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Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation

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Banwat, Bhakti. "The Role of Predictive Analytics in Strategic Business Decision-Making." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04302.

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Abstract In today’s rapidly evolving business environment, making accurate and timely strategic decisions is essential for sustainable success. Predictive analytics, which leverages historical and real-time data to forecast future outcomes, has become a critical tool for businesses to gain competitive advantage. This research paper explores the role of predictive analytics in strategic business decision-making, examining its adoption across industries, benefits, challenges, and impact on organizational performance. Through a structured questionnaire distributed to business professionals, this

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Lebefromm, Uwe. "Predictive analytics as a tool of controlling in decision making process in the marina industry." Pomorstvo 35, no. 1 (2021): 100–108. http://dx.doi.org/10.31217/p.35.1.11.

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This paper is dealing with predictive modeling based on predictive analytics using computer application system and the usage of the prediction results for decision-making processes. Usually the prediction is based on the experience of decision makers, but the aim of this study is to explain and proof higher predictive efficiency when using predictive analytics based on machine learning as well as more accurate future-oriented business decisions. The marina industry in Croatia is used for this research because of its complexity and necessity to predict future events that influence company succe

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HANUSHCHAK-YEFIMENKO, Liudmyla, and Sanja GONGETA. "PREDICTIVE ANALYTICS AND MARKETING MANAGEMENT: IMPLEMENTING BUSINESS STRATEGIES THROUGH INTELLIGENT MODELS." Management 41, no. 1 (2025): 80–94. https://doi.org/10.30857/2415-3206.2025.1.5.

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INTRODUCTION. In today's digital economy and highly competitive market conditions, companies are faced with the need to quickly adapt to changes in consumer behavior, market trends, and technological trends. In these conditions, marketing management ceases to be an exclusively intuitive art and is transformed into a systematic activity based on data analysis, customer behavior prediction, and customer interaction management based on objective patterns. One of the key tools that allows for such a transformation is predictive analytics, which provides companies with the ability to proactively ma

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Stefanovic, Nenad. "Collaborative predictive business intelligence model for spare parts inventory replenishment." Computer Science and Information Systems 12, no. 3 (2015): 911–30. http://dx.doi.org/10.2298/csis141101034s.

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In today?s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. I

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BAKAM, Genevieve, Khumbulani MPOFU, and Charles MBOHWA. "A REFERENCE MODEL FOR BUSINESS ANALYTICS-BASED DECISION-MAKING PROCESSES IN RAIL TRANSPORT MANUFACTURING COMPANIES IN SOUTH AFRICA." Business Excellence and Management 15, no. 1 (2025): 97–113. https://doi.org/10.24818/beman/2025.15.1-07.

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In addition to digital transformation, businesses have reviewed their business strategies and decision-making techniques to develop a competitive advantage in the transport manufacturing sector. It happens that innovative business approaches face some limitations compromising business survival in the long term. This study investigates the importance of adopting a reference model for business analytics-based decision-making processes in rail transport manufacturing companies. This study follows a qualitative research design using secondary data published in various annual reports to define the

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Adebiaye, Richmond, Mohammed Alshami, and Theophilus Owusu. "MACHINE LEARNING MODELS FOR EXTRAPOLATIVE ANALYTICS AS A PANACEA FOR BUSINESS INTELLIGENCE DECISIONS." International Journal of Engineering Technologies and Management Research 10, no. 6 (2023): 13–32. http://dx.doi.org/10.29121/ijetmr.v10.i6.2023.1333.

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The application of business intelligence (BI) in data analytics helps organizations access critical information in finance, marketing, healthcare, retail, and other critical infrastructures. However, there is a dearth of strategies to effectively leverage BI to empower businesses to refine useful data, understand newer industry trends, and improve competitive intelligence strategy for effective decision-making. This study implemented predictive data analytics to determine how the subjective decision-making process of used dealerships conducts their sales of vehicles and other business variable

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Saleh, Haifa Hadi, Azzam Khalid Chyad, Maha Barakat, and Ghazwan Salim Naamo. "Enhancing Business Operations Efficiency thorough Predictive Analytics." Journal of Ecohumanism 3, no. 5 (2024): 700–714. http://dx.doi.org/10.62754/joe.v3i5.3932.

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Background: In today's competitive world, organizations constantly seek innovative ways to improve operational efficiency and maintain a competitive advantage. Introducing big data and advanced analytics techniques has created new opportunities for optimizing corporate processes. Objective: The article aims to investigate the potential of predictive analytics in improving business operations efficiency, emphasizing cost savings, process optimization, and better decision-making. Methods: We used a mixed-methods research design, integrating quantitative analysis of operational data from 30 organ

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Ocaña, Luis Llerena Oca�, Dionisio Ponce Ruiz, and Betty Valle Fiallos. "Optimizing Retail Business Strategies with Advanced Analytics and Improved Business Intelligence Techniques." Journal of Intelligent Systems and Internet of Things 11, no. 1 (2024): 75–83. http://dx.doi.org/10.54216/jisiot.110108.

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The retail landscape thrives on the synthesis of advanced analytics and business intelligence techniques, pivotal in navigating the complexities of consumer behavior and market dynamics. This study addresses the imperative to optimize retail strategies by leveraging historical sales data from 45 diverse stores with multifaceted departments. The challenge of predicting retail sales prices guided our methodology, employing convolutional neural network architectures and Root Mean Square Error (RMSE) as the principal error metric. Through iterative computations and feature extractions, our model a

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Gupta Lakkimsetty, N. V. Rama Sai Chalapathi. "Role of AI in Business Analytics: Predictive Insights for Future Trends." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 1–10. https://doi.org/10.47760/ijcsmc.2025.v14i03.001.

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In the current corporate environment, combining Artificial Intelligence (AI) using Cloud corporate Intelligence (CBI) is a revolutionary way to improve data visualisation and predictive analytics. This article examines how cloud-based solutions and AI technologies work together, emphasising how both have an effect on decision-making. The ability to make decisions is significantly enhanced by AI-driven systems, which provide precise real-time insights and predictive analytics. In this article, we look at the many benefits of integrating AI with BI, including improved operational efficiency, per

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Dr. Vijai Tiwari. "Role of Data Analytics in Business Decision Making." Knowledgeable Research: A Multidisciplinary Journal 3, no. 01 (2024): 18–27. http://dx.doi.org/10.57067/0zr57x43.

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Data analytics plays a pivotal role in modern business decision-making by enabling organizations to convert raw data into valuable insights. Through techniques such as data mining, predictive analytics, and machine learning, businesses can identify patterns, forecast trends, and make evidence-based decisions. This research explores the impact of data analytics on enhancing operational efficiency, improving customer engagement, and gaining competitive advantages. The study also highlights the importance of data-driven strategies in reducing risks and optimizing resources. Ultimately, it undersc

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Jangam, Dhanshri Satish, and Arati R. Deshpande. "Business Analytics Using Predictive Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 595–609. http://dx.doi.org/10.17762/ijritcc.v11i8s.7242.

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In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and acc

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Crick, James M., and Dave Crick. "Angel investors’ predictive and control funding criteria." Journal of Research in Marketing and Entrepreneurship 20, no. 1 (2018): 34–56. http://dx.doi.org/10.1108/jrme-11-2016-0043.

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PurposeThis study aims to investigate the question involving what factors affect angel investors’ decision-making in funding new start-ups with specific reference to their evolving business models. Without funding and access to networks and experience, certain entrepreneurs will not get their business model through the start-up phase.Design/methodology/approachData arise from 20 semi-structured interviews with angel investors in New Zealand plus supplementary interviews with business incubator managers and textual data.FindingsThe findings suggest a degree of causation-based decision-making, i

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Researcher. "PREDICTIVE FORECASTING IN ECONOMICS: THE IMPERFECT CRYSTAL BALL." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 30–37. https://doi.org/10.5281/zenodo.13683991.

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Economic forecasting, a cornerstone of decision-making in policy, business, and finance, faces persistent challenges in achieving consistent accuracy. This article explores the multifaceted nature of these challenges, including the complexity and dynamic evolution of economic systems, the impact of unforeseen shocks, the unpredictability of human behavior, and limitations in data quality and availability. Despite these hurdles, the article argues for the continued importance of economic forecasting in guiding crucial decisions across various sectors. It examines how forecasts inform fiscal and

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Srinivasa, Reddy Vuyyuru. "Unlocking Future Consumer Insights: Using Predictive Analytics and AI to Shape Proactive Retail Strategies." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 110–15. https://doi.org/10.5281/zenodo.15560633.

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Companies within the retail sector experience fundamental transformations through the combination of predictive analytics alongside artificial intelligence (AI) for developing new strategic directions. The research delves into technology-enabled methods for retailers to acquire consumer data that produces business strategies for inventory enhancements, together with personalized customer service and performance optimization. The current AI and predictive analytics trends serve as research subjects to understand operational retail opportunities for developing proactive decision-making approache

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Zhang, Chunyang, and Wenjing Han. "Ensembles of decision trees and gradient-based learning for employee turnover rate prediction." PeerJ Computer Science 10 (October 9, 2024): e2387. http://dx.doi.org/10.7717/peerj-cs.2387.

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Employee turnover has a negative impact on business profitability. To tackle this issue, we can utilize computational advancements to forecast attrition and minimize expenses. We employed an HR Analytics dataset to investigate the feasibility of using these predictive models in decision support systems. We developed an ensemble of gradient-based decision trees that accurately predicted employee turnover and performed better than other sophisticated techniques. This approach demonstrates exceptional performance in handling structured and imbalanced data, effectively capturing intricate patterns

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Chauhan, Shailesh. "Leveraging Data for Enhancing Predictive Analytics in Enterprise Decision-Making." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 1561–76. https://doi.org/10.22214/ijraset.2025.67118.

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Predictive analytics is critical in a data-driven business environment, which enables any organization to make proactive and well-informed decisions. The paper depicts a discussion on how enterprises can leverage predictive analytics in obtaining value-driven insights for enhancing decision-making processes. It provides a deep understanding of the different types of data, data engineering, and methodologies that enrich predictive modeling. Real-world applications and case studies from retail, health, and finance lead the role of predictive analytics in optimized operations to measurable busine

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Talekar, P. R. "Predictive Analytics for Market Trends." International Journal of Advance and Applied Research 5, no. 10 (2024): 64–66. https://doi.org/10.5281/zenodo.11299015.

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Predictive analytics as their name implies, tries to predict the things that happen in the future. It supports most of the enterprise activities, but we are focused on marketing. Rather than just describing who, where and when, it helps in predicting the future using many algorithms of regression equations. It not only describes what’s happening but also predicts what will happen in the future, Predictive analytics helps the marketers make better decisions in today’s generation by providing them with insights into what is likely to happen in future and helps in recognizing the patt

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S, ABHILASH. "The Power of Big Data: Enhancing Business Strategy with Real-Time Analytics." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02855.

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In today’s fast-paced business environment, organizations face the challenge of making timely and informed decisions to maintain a competitive edge. The Power of Big Data: Enhancing Business Strategy with Real-Time Analytics explores how big data analytics (BDA) transforms raw data into actionable insights, enabling real-time decision- making and strategic planning. This paper delves into the technologies, methodologies, and tools that facilitate the collection, processing, and analysis of large datasets from diverse sources such as social media, IoT devices, and transactional systems. It high

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Pathoori, Mahesh Reddy. "The Evolution of Workforce Analytics: From Historical Reporting to Predictive Decision-Making." European Journal of Computer Science and Information Technology 13, no. 46 (2025): 68–83. https://doi.org/10.37745/ejcsit.2013/vol13n466883.

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Workforce analytics has undergone a transformational evolution from basic historical reporting to sophisticated predictive decision-making capabilities that fundamentally reshape how organizations understand and leverage their human capital. This progression represents more than a technological advancement—it signifies a paradigm shift in strategic human resource management. Organizations now harness integrated data ecosystems, machine learning algorithms, and predictive models to anticipate workforce needs, optimize talent deployment, and align human capital investments with business objectiv

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Narayana, T., Sohail Shaik, and S. Kaur. "Predictive Analytics – The Cognitive Analysis." Oriental journal of computer science and technology 10, no. 1 (2017): 187–93. http://dx.doi.org/10.13005/ojcst/10.01.25.

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Predictive analytics plays an important role in the decision-making process and intuitive business decisions, by overthrowing the traditional instinct process. Predictive analytics utilizes data-mining techniques in order to predict the future outcomes with a high level of certainty. This advanced branch of data engineering is composed of various analytical and statistical methods which are used to develop models that predict the future occurrences. This paper examines the concepts of predictive analytics and various mining methods to achieve the prior. In conclusion, paper discusses process a

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Adekunle, Bolaji Iyanu, Ezinne C. Chukwuma-Eke, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 791–99. https://doi.org/10.54660/.ijmrge.2021.2.1.791-799.

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Predictive modeling has emerged as a powerful tool for optimizing business operations by leveraging machine learning techniques to reduce inefficiencies. This study explores the application of predictive analytics in identifying and mitigating common operational inefficiencies such as delays, resource misallocation, and excessive costs. By utilizing historical data, real-time analytics, and machine learning algorithms, businesses can make data-driven decisions that enhance efficiency and productivity. This examines key machine learning methodologies, including supervised and unsupervised learn

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Oluwaseun, Badmus, Anas Rajput Shahab, Babatope Arogundade John, and Williams Mosope. "AI-driven business analytics and decision making." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 616–33. https://doi.org/10.5281/zenodo.15010634.

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The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time

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Aleksandrova, Yanka, and Mariya Armyanova. "Analyzing the Impact of the Threshold on Machine Learning Models for Credit Risk Prediction Using Business Intelligence." Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series 12, no. 2 (2023): 79–88. http://dx.doi.org/10.56065/ijusv-ess/2023.12.2.79.

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Making decisions based on predictions generated from machine learning models requires users to have a clear understanding of the mechanisms and logic behind every prediction. From one side, business users must be convinced in the ability of the models to generate correct predictions. Predictive power, expressed by the different performance measures, is not sufficient for building trust and acceptance of machine learning models. Business users need additional techniques and tools for model interpretation and evaluation of the effects from decisions based on machine learning predictions. In this

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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Integrating business intelligence and predictive analytics in banking: A framework for optimizing financial decision-making." Finance & Accounting Research Journal 6, no. 8 (2024): 1517–30. http://dx.doi.org/10.51594/farj.v6i8.1505.

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Integrating Business Intelligence (BI) and predictive analytics in banking has become a pivotal strategy for optimizing financial decision-making. This review paper explores this integration's theoretical foundations, current trends, and challenges while proposing a comprehensive framework to enhance decision-making processes. The proposed framework emphasizes the importance of a unified data architecture, advanced data integration techniques, and the synergistic use of BI tools and predictive models. By examining the implications for banking institutions, the paper highlights how this integra

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Shree, Chand Chhimpa. "Predictive Analytics in Financial Forecasting: Methods, Applications, and Challenges." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.10673796.

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Predictive analytics plays a crucial role in financial forecasting, offering organizations the ability to anticipate future trends, mitigate risks, and make data-driven decisions. This paper provides an in-depth exploration of predictive analytics in financial forecasting, covering methods, applications, challenges, and emerging trends. Through case studies and empirical examples, we illustrate the practical applications and tangible benefits of predictive analytics across various industries, including retail, banking, and telecommunications. We discuss key methodologies such as regression ana

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Ayuningtyas, Astika, Sabil Mokodenseho, Adit Mohammad Aziz, Dwi Nugraheny, and Nurcahyani Dewi Retnowati. "Big Data Analysis and Its Utilization for Business Decision-Making." West Science Information System and Technology 1, no. 01 (2023): 10–18. http://dx.doi.org/10.58812/wsist.v1i01.177.

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In today's data-driven business landscape, the analysis of big data has become a pivotal tool for making informed decisions across industries. This research method paper explores the methodologies employed in big data analysis for business decision-making and conducts a bibliometric analysis using VOSviewer to map the scholarly landscape of this field. The systematic literature review identifies key methodologies, including descriptive, predictive, and prescriptive analytics, text analysis, and network analysis. Real-world case studies demonstrate their practical applications in diverse sector

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Jalil, Muhammad Saqib, Esrat Zahan Snigdha, Mohammad Tonmoy Jubaear Mehedy, et al. "AI-Powered Predictive Analytics in Healthcare Business: Enhancing Operational Efficiency and Patient Outcomes." American Journal of Medical Sciences and Pharmaceutical Research 07, no. 03 (2025): 93–114. https://doi.org/10.37547/tajmspr/volume07issue03-13.

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The implementation of AI-powered predictive analytics within healthcare business operations is transforming medical practices through improved operational performance and better clinical results. The research examines how algorithms from machine learning combined with deep learning methods and real-time data processing systems enable better decisions in clinical settings and resource management along with advanced patient care methods. The research employs both practical applications and scientific study of empirical evidence to evaluate the ability of predictive AI models in healthcare to dec

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Eido, Warveen Merza, and Subhi R. M. Zeebaree. "Smarter Marketing with AI: How Cloud Technology is Changing Business." Asian Journal of Research in Computer Science 18, no. 4 (2025): 331–59. https://doi.org/10.9734/ajrcos/2025/v18i4623.

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The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing cap

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Balakrishna Teja Pillutla. "Reshaping workforce agility through intelligent cloud HR: An empirical study on predictive analytics and employee lifecycle management using SAP HCM platforms." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 1156–66. https://doi.org/10.30574/wjaets.2024.13.1.0520.

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The research examines the transformation of workforce agility that occurs with predictive analytics integration on SAP HCM platforms within cloud-based HR systems. The research integrates case studies with empirical analysis to understand how predictive models create transformative benefits for managing employee lifecycles, acquiring talents, and planning workforces. The data indicates that SAP HCM predictive analytics integration enables better decision-making and better employee retention and develops an agile workforce. The research gives business organizations and human resource profession

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Omoniyi Babatunde Johnson, Yodit Wondaferew Weldegeorgise, Emmanuel Cadet, Olajide Soji Osundare, and Harrison Oke Ekpobimi. "Developing advanced predictive modeling techniques for optimizing business operations and reducing costs." Computer Science & IT Research Journal 5, no. 12 (2024): 2627–44. https://doi.org/10.51594/csitrj.v5i12.1757.

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In today's competitive business landscape, organizations are increasingly turning to predictive modeling techniques to enhance operational efficiency and reduce costs. By leveraging data analytics, machine learning, and statistical methods, predictive models enable businesses to anticipate market trends, optimize resource allocation, and make data-driven decisions. This review explores the development of advanced predictive modeling techniques to optimize various business processes, from inventory management and supply chain optimization to customer relationship management and financial foreca

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Sk, Sajida Sultana, G. Joseph Anand Kumar, V. Leela Venkata Mani Sai, N. Bala Sai, and E. Sai Naga Lakshmi. "Predicting restaurant ratings using regression analysis approach." ITM Web of Conferences 74 (2025): 03003. https://doi.org/10.1051/itmconf/20257403003.

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Restaurant Builder solves the challenge of building restaurants in a highly competitive market by providing a framework for accurately predicting restaurant prices, a key tool for attracting customers and measuring success. This study identifies and identifies key factors that influence evaluation, allowing restaurant owners to make informed decisions, reduce risk, and save time before starting a business. The study uses seven regression models to compare performance indicators and identify the most reliable predictive models, ultimately providing valuable resources to support informed decisio

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Muhammad Ali, Nayeem ul Hassan Ansari, Bilal Ahmed Chishty, Chin-Hong Puah, and Muhammad Ashfaq. "Investor behaviour and investment decisions: Evidence from Pakistan Stock Exchange." Asian Academy of Management Journal 28, no. 2 (2023): 1–28. http://dx.doi.org/10.21315/aamj2023.28.2.1.

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This research aims to understand the influence of behavioural factors on investment decisions in the Pakistan Stock Exchange (PSX). This study gathered primary data using a survey-based questionnaire from 318 individual investors. The issue being investigated in this study is how behavioural elements, such as sentiment, overconfidence, over- and under-reaction, and perceived market efficiency, affect investment choices made on the PSX, with a particular emphasis on the limited predictive power of herd behaviour. The sample data were analysed using partial least square-structural equation model

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Adekunle, Bolaji Iyanu, Ezinne C. Chukwuma-Eke, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "Predictive Analytics for Demand Forecasting: Enhancing Business Resource Allocation Through Time Series Models." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 32–42. https://doi.org/10.54660/.ijfmr.2021.2.1.32-42.

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Predictive analytics has become a crucial tool in modern business decision-making, particularly in demand forecasting. By leveraging historical data and statistical modeling techniques, businesses can enhance resource allocation, optimize inventory management, and improve overall operational efficiency. Among various predictive analytics methods, time series models are widely employed due to their ability to capture trends, seasonality, and patterns in demand fluctuations. This explores the application of time series models, including Moving Average (MA), Autoregressive (AR), Autoregressive In

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Rimon, SM Tamim Hossain. "Leveraging Artificial Intelligence in Business Analytics for Informed Strategic Decision-Making: Enhancing Operational Efficiency, Market Insights, and Competitive Advantage." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 600–624. https://doi.org/10.60087/jaigs.v6i1.278.

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Abstract In recent years, Artificial Intelligence (AI) has emerged as a transformative force in business analytics, enabling organizations to make more informed, data-driven strategic decisions. This paper explores the integration of AI in business analytics and its impact on enhancing operational efficiency, gaining market insights, and securing a competitive advantage. AI technologies like machine learning and natural language processing have revolutionized how businesses collect, analyze, and leverage data to optimize decision-making processes. By automating routine tasks and providing pred

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Oluwaseun Badmus, Shahab Anas Rajput, John Babatope Arogundade, and Mosope Williams. "AI-driven business analytics and decision making." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 616–33. http://dx.doi.org/10.30574/wjarr.2024.24.1.3093.

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The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time

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Sajid, Shruthi, Jeewanie Jayasinghe Arachchige, Faiza Allah Bukhsh, Abhishta Abhishta, and Faizan Ahmed. "Building Trust in Predictive Analytics: A Review of ML Explainability and Interpretability." International Journal of Computing Sciences Research 9 (January 1, 2025): 3364–91. https://doi.org/10.25147/ijcsr.2017.001.1.223.

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Purpose–The purpose of the manuscript is to explorethe previous literature to reveal the trust and interpretability of predictive analytical models that use ML /AI techniques. Method–The methodology applied for the studyis the guidelines of Kitchenham et al. (2007). Results–The results reveal that past research explicitly discussed the usage of predictive analytics.However, ML models are considered black boxes and sufferfrom transparency.The study proposed a typical process to ensure that predictions made by AI/ML models can be interpreted and trusted. Conclusion–The literature review conducte

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Carlos Eduardo Rodriguez. "Optimizing business aviation operations through predictive maintenance: A data-driven approach to aircraft lifecycle management." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 3162–72. https://doi.org/10.30574/wjarr.2024.23.1.2146.

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Predictive analytics transforms aircraft lifecycle management by integrating predictive maintenance systems into business aviation. Predictive maintenance analyzes current data alongside machine learning algorithms with IoT sensors to anticipate equipment faults, which helps organizations reduce their expenses and increase their operation reliability. This investigation uses predictive data models to evaluate how predictive maintenance methods minimize unplanned breaks, maximize operational efficiency, and minimize total maintenance expenses. The studied outcomes demonstrate how reducing unexp

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Zhang, Xiaotian. "How Business Intelligence Will Impact Financial Investments in the Capital Market." Advances in Economics, Management and Political Sciences 93, no. 1 (2024): 147–53. http://dx.doi.org/10.54254/2754-1169/93/20241078.

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In today's dynamic data-driven financial environment, the role of Business Intelligence (BI) in capital markets investment decisions cannot be overstated. The core of Business Intelligence (BI) lies in its extraordinary ability to transform raw data into valuable insights. It enables investors to confidently navigate the intricate world of the stock market. With the help of BI tools and platforms, investors now have a range of analytical capabilities that were once considered unimaginable. The data-driven decision-making approach of business intelligence has been a game changer for investors a

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Polinati, Anjani kumar, Sangeeta Singh, Satyasri Akula, et al. "Revolutionizing Information Management: AI-Driven Decision Support Systems for Dynamic Business Environments." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 322–35. https://doi.org/10.52783/jisem.v10i35s.6010.

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The need of more sophisticated decision making tools in modern businesses is largely influenced by the pace and intricacy of new business markets. The use of Artificial Intelligence (AI) is transforming Decision Support Systems (DSS), inforamtion technology, and maagement strategies. This paper seeks to analyze how AI technology is changing the business decision making processes with and emphasis on its use in DSS.   The application of AI, machine learning (ML), natural language processing (NLP), and predictive analysis have radically transformed the efficiency and effectiveness of decisi

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Oloruntosin Tolulope Joel and Vincent Ugochukwu Oguanobi. "Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification." International Journal of Frontiers in Engineering and Technology Research 6, no. 2 (2024): 071–81. http://dx.doi.org/10.53294/ijfetr.2024.6.2.0035.

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In today's hyper-competitive business landscape, leveraging data-driven strategies is paramount for sustainable growth and profitability. This review presents an overview of the imperative role of predictive analytics in facilitating business expansion by enhancing profitability and identifying opportunities. Predictive analytics harnesses historical data and advanced modeling techniques to forecast future trends, enabling businesses to make informed decisions with precision. By understanding predictive analytics, businesses can effectively identify expansion opportunities by analyzing market

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Houtmeyers, Kobe C., Arne Jaspers, and Pedro Figueiredo. "Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics." International Journal of Sports Physiology and Performance 16, no. 11 (2021): 1719–23. http://dx.doi.org/10.1123/ijspp.2020-0958.

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Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in bu

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Sakib, Nazmus, and Mushfika Rahman Rhidita. "Marketing Decision Making through Predictive Modeling: A 6S Architectural Layout Approach of Market Mining." MANTHAN: Journal of Commerce and Management 9, no. 2 (2022): 1–15. http://dx.doi.org/10.17492/jpi.manthan.v9i2.922201.

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The six(6) “S” concepts, a blend of data science and market penetration, include storing knowledge, segregating datamarts, synthesis penetration, synchronizing business processes, and scaling forecast. This study employs marketing data and company profiles in the input layer which will function to internal layers and be embedded in the neural network grid learning models. A strategy for identifying business intelligence is presented that will involve to improve characteristics using markets’ data mining. The suggested hidden 6S layers statistically define the business analysis structure, which

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Ugbebor, Friday O., David A. Adeteye, and John O. Ugbebor. "PREDICTIVE ANALYTICS MODELS FOR SMES TO FORECAST MARKET TRENDS, CUSTOMER BEHAVIOR, AND POTENTIAL BUSINESS RISKS." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 3 (2024): 355–81. https://doi.org/10.60087/jklst.v3.n3.p355-381.

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Abstract Introduction: Small and Medium-sized Enterprises (SMEs) face numerous challenges in today's rapidly evolving business landscape. Predictive analytics models offer a promising solution for SMEs to gain insights into market trends, customer behavior, and potential risks. These models apply analytical techniques for predicting future performances to help SMEs make the right decisions and survive successfully in their industries. Predictive analytics has received quite a lot of consideration from big organizations but is not much common among SMEs because of certain challenges that hinder

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