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Purpose: The purpose of this publication is to present the applications of usage of business analytics in continuous improvement. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: This paper explores the pivotal role of business analytics in driving continuous improvement within Industry 4.0 environments. It examines how the integration of advanced analytics tools, such as predictive modeling and real-time data visualization, transforms operational efficiency, quality management, and strategic decision-making. By leveraging vast datasets generated by interconnected systems, organizations can identify inefficiencies, anticipate potential issues, and enhance customer experiences. The paper highlights both the advantages, including improved decision-making, increased efficiency, and data-driven innovation, as well as the challenges, such as data quality concerns and integration difficulties. Ultimately, it underscores the significance of business analytics in fostering a culture of ongoing refinement and adaptability, crucial for sustaining competitive advantage and achieving longterm success in the evolving industrial landscape. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of continuous improvement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in cybersecurity analytics. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics into cybersecurity practices within Industry 4.0 signifies a pivotal advancement in safeguarding organizational assets against the evolving cyber threat landscape. As industrial systems grow more complex and interconnected, traditional security methods focused on perimeter defenses are increasingly inadequate. Modern cybersecurity strategies must therefore incorporate advanced analytics to manage and mitigate risks effectively. Business analytics enhances cybersecurity through sophisticated machine learning algorithms, predictive capabilities for anticipating future threats, and improved incident response via real-time monitoring and automated alerts. These innovations foster a proactive and efficient security approach, enabling swift detection, response, and informed decision-making based on thorough risk assessments. Despite these advantages, challenges such as data overload, false positives, integration hurdles, and the need for specialized expertise persist. Additionally, concerns about data privacy, costs, and analytical complexity must be managed. Embracing business analytics while addressing these challenges will enable organizations to fortify their security posture, optimize resource use, and adapt to the demands of Industry 4.0, thereby shaping the future of cybersecurity in a rapidly evolving digital landscape. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of cybersecurity analytics. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in human resource analytics. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: This paper explores the transformative potential of business analytics within human resource (HR) analytics, particularly in the context of Industry 4.0. It highlights how the integration of advanced analytics tools and methodologies enables organizations to gain deep insights into workforce behaviors, trends, and patterns, ultimately facilitating more informed decision-making and strategic workforce management. Through applications such as talent acquisition and retention, performance management, workforce planning, and employee wellbeing, business analytics empowers HR professionals to optimize HR processes, enhance employee satisfaction, and drive organizational success. However, challenges such as data quality issues, privacy concerns, and skills gaps among HR professionals underscore the need for a strategic approach and investment in technology and talent to fully realize the benefits of business analytics in HR analytics. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of human resource analytics [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in demand forecasting. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The rise of Industry 4.0 has revolutionized the business landscape by creating a data-rich environment fueled by interconnected devices and digital systems throughout the supply chain. In this era, business analytics emerges as a crucial tool, leveraging the abundance of data to uncover intricate patterns and trends in consumer behavior, market dynamics, and product demand. By analyzing historical data and integrating external factors, business analytics enables more accurate demand forecasts, essential for effective inventory management, production planning, and overall business success. Advanced analytics techniques such as machine learning and predictive modeling thrive in Industry 4.0 environments, enabling businesses to process large datasets and predict future demand with precision. Moreover, business analytics facilitates the integration of demand forecasting with other supply chain components, optimizing resource allocation and enhancing efficiency. Industry 4.0 also fosters greater customization and personalization of products and services through market segmentation analysis and tailored forecasting, driving competitive advantage. This publication has explored the applications of business analytics in demand forecasting, emphasizing its importance, aspects, software applications, advantages, and challenges within the Industry 4.0 context. By addressing these aspects, organizations can harness the power of business analytics to optimize operations, foster growth, and stay competitive in today's dynamic business environment. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in global supply chain coordination. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics in global supply chain coordination within the framework of Industry 4.0 is a transformative strategy that propels organizations toward enhanced efficiency, adaptability, and competitiveness. This approach is particularly crucial as industries undergo significant technological transformations. Business analytics strategically deployed in the modern supply chain serves as a vital tool, addressing the complexities inherent in Industry 4.0. The diverse applications of business analytics, including demand forecasting, supply chain visibility, predictive maintenance, collaboration, and risk management, highlight its multifaceted benefits. These applications empower organizations to anticipate market demands, optimize inventory, ensure reliable production flows, foster collaboration, and proactively address risks, thereby bolstering overall supply chain resilience and responsiveness. The use of sophisticated software tools like SAP Integrated Business Planning, Oracle SCM Cloud, and IBM Watson Supply Chain underscores the pivotal role of technology in overcoming the intricate challenges of managing global supply chain processes. Despite the numerous advantages, challenges such as data quality, integration issues, implementation costs, and skill shortages necessitate careful consideration and strategic planning. Organizations must address these challenges, along with security and privacy concerns, resistance to change, and the complexity of analytics tools, through investments in training and change management strategies to fully unlock the potential of business analytics. The presented tables further illustrate the advantages and challenges, emphasizing the positive impact of business analytics on efficiency, risk management, and strategic alignment with organizational goals. In conclusion, the judicious use of business analytics offers substantial opportunities for optimizing global supply chain coordination in Industry 4.0, requiring organizations to navigate both advantages and challenges proactively to position themselves at the forefront of innovation and competitiveness in the dynamic landscape of modern industry. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of global supply chain coordination. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in risk mitigation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The incorporation of business analytics into risk mitigation strategies amid the landscape of Industry 4.0 signifies a pivotal paradigm shift in response to the multifaceted challenges posed by technological disruptions, supply chain vulnerabilities, regulatory changes, and cybersecurity threats. As industries undergo unprecedented digital transformations, traditional risk management approaches are rendered obsolete, prompting the integration of more data-driven and analytical methodologies. Business analytics plays a crucial role in this transformative shift, leveraging vast volumes of data generated in Industry 4.0 environments. Through advanced analytics techniques, organizations can proactively anticipate and respond to potential risks in real-time, with predictive analytics enabling the forecast of disruptions and the implementation of preemptive measures. Beyond immediate operational concerns, the applications of business analytics extend to supply chain optimization, cybersecurity monitoring, and regulatory compliance. The symbiotic relationship between business analytics and risk mitigation emerges as a cornerstone for sustainable and resilient business practices in the evolving landscape of Industry 4.0, emphasizing the necessity of addressing associated challenges and leveraging a diverse array of software applications for comprehensive risk management. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of risk mitigation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in smart manufacturing. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics in smart manufacturing within the framework of Industry 4.0 marks a significant stride in industrial processes, offering manifold advantages alongside notable challenges. Throughout this study, we delve into the expansive realm of business analytics applications, encompassing predictive maintenance, quality control, supply chain optimization, and real-time decision-making. Leveraging business analytics yields palpable benefits in smart manufacturing, exemplified by proactive equipment maintenance, stringent quality standards adherence, and streamlined supply chain operations. Additionally, analytics-driven enhancements in production optimization, energy management, demand forecasting, and asset performance management contribute to heightened productivity, cost reduction, and sustainability improvement. Challenges including data integration complexities, implementation intricacies, security concerns, scalability limitations, model interpretability issues, and skill gaps necessitate concerted efforts for effective resolution. Collaboration among stakeholders—manufacturers, software developers, policymakers, and educational institutions—is imperative. Joint initiatives aimed at bolstering data integration capabilities, providing specialized training, fortifying cybersecurity measures, and fostering a culture of continuous improvement are crucial for successful business analytics deployment. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in customer behaviour analysis. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with customer behavior analysis in Industry 4.0 environments offers businesses a transformative opportunity to gain profound insights into customer preferences, trends, and behaviors. Through the utilization of state-of-the-art technologies and data-driven methodologies, organizations can attain unprecedented levels of precision and detail in understanding customer behavior. Real-time data collection and analysis facilitate agile responses to evolving market dynamics, enabling personalized customer experiences across various channels. Additionally, advanced analytics tools such as predictive modeling and sentiment analysis empower businesses to forecast future trends, address churn, and enhance customer satisfaction. However, businesses may encounter challenges like data quality issues, privacy concerns, and resource limitations. Overcoming these obstacles necessitates a comprehensive approach, involving investments in data governance, talent acquisition, and technology infrastructure. By surmounting these challenges, businesses can harness the full potential of business analytics to drive strategic decisions, refine marketing strategies, and elevate overall business performance within Industry 4.0 environments. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in environmental sustainability. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics into environmental sustainability practices within the evolving landscape of Industry 4.0 presents both transformative opportunities and significant challenges. This convergence of digital technologies, data analytics, and environmental consciousness compels businesses to adopt sustainable practices. Business analytics acts as a pivotal tool, enabling enterprises to monitor, analyze, and optimize their environmental performance in the interconnected world of Industry 4.0. Advantages of leveraging business analytics in this context include real-time monitoring and predictive analytics, empowering proactive decision-making for resource optimization, reduced energy consumption, and minimized emissions. Smart resource allocation, improved energy management, and enhanced supply chain sustainability contribute to a comprehensive approach to environmental sustainability. The integration of environmental, social, and governance (ESG) criteria ensures transparency and a holistic approach to sustainability in the Industry 4.0 era. However, these advantages come with challenges such as data quality, integration complexities, cybersecurity concerns, and high initial implementation costs. Talent shortages, regulatory compliance complexities, and ethical considerations further complicate the landscape. Balancing short-term gains with long-term goals, addressing resistance to change, and ensuring stakeholder engagement are critical considerations. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in predictive maintenance. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics into predictive maintenance within the context of Industry 4.0 signifies a revolutionary change in how organizations manage their industrial assets. The Industry 4.0 landscape, characterized by advanced technologies and interconnected systems, has elevated predictive maintenance as a cornerstone of operational strategies. This shift from reactive to proactive maintenance, powered by real-time data analytics, machine learning, and IoT integration, not only ensures equipment longevity but also offers a multitude of advantages. The applications of business analytics in predictive maintenance, detailed in Table 1, illustrate the comprehensive approach organizations take in collecting, integrating, and analyzing data to anticipate and prevent equipment failures. The significance of this strategic imperative is further underscored by the diverse range of business analytics software highlighted in Table 2, tailored to specific industry needs and emphasizing adaptability and scalability in Industry 4.0 conditions. The benefits outlined in Table 3 highlight the substantial positive impact of business analytics on predictive maintenance, including increased operational efficiency, cost savings, extended equipment lifespan, and improved safety. The alignment of predictive maintenance with IoT and Industry 4.0 principles ensures a smooth integration into the broader manufacturing ecosystem. However, challenges discussed in Table 4 reveal the complexities organizations face in maintaining the quality, scalability, and responsiveness of predictive maintenance systems. Addressing these challenges requires continuous investment in infrastructure, skilled resources, and resolving issues related to data quality and latency. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in maintenance resources allocation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with condition monitoring technologies heralds a transformative shift in maintenance resource allocation. Through the utilization of real-time data from sensors and monitoring devices, organizations can craft dynamic and adaptive maintenance strategies. These strategies, grounded in the current equipment condition, strike a nuanced balance between preventive and corrective actions. Business analytics enriches decision-making by factoring in critical elements such as equipment importance, operational impact, resource availability, and budget constraints. This integration instigates a paradigm shift, fostering proactive, adaptive, and efficient resource allocation, resulting in heightened asset reliability, diminished downtime, and amplified operational performance. The evolving synergy between business analytics and maintenance practices is becoming integral to the future of asset management and industrial operations. The diverse applications of business analytics in maintenance, as outlined in Table 1, underscore its versatility, encompassing predictive maintenance, condition monitoring, asset performance management, work order prioritization, resource optimization, cost-benefit analysis, inventory management, and performance metrics monitoring. Concurrently, the adoption of advanced software solutions in Industry 4.0 conditions, exemplified by IBM Maximo Asset Management, SAP Intelligent Asset Management, and Fiix CMMS, reflects a commitment to efficiency and innovation in maintenance resource allocation. Despite the substantial advantages, addressing challenges outlined in Table 4, including data quality, integration complexities, implementation costs, and skill development, is crucial. These challenges underscore the necessity for a strategic and holistic implementation approach that considers technology, personnel training, and organizational readiness. In essence, the evolution of maintenance resource allocation through business analytics signifies a data-driven revolution poised to optimize current operations and position organizations for sustained success amid rapid technological advancements and the transformations of Industry 4.0. Originality/value: Detailed analysis of business analytics in the case of maintenance resource allocation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in work safety and health. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: In the era of Industry 4.0, characterized by automation and smart technologies, the preservation of workforce health and well-being takes center stage. This paper explores the transformative integration of business analytics into work safety and health practices, signaling a paradigm shift in monitoring, managing, and elevating occupational safety standards. The publication underscores the proactive role of business analytics, especially through wearable device-driven physiological monitoring, in safeguarding worker health without compromising productivity. By aligning seamlessly with regulatory compliance, business analytics becomes a linchpin in mitigating legal and financial risks associated with workplace incidents. The synergy between work safety, health, and business analytics presents a transformative approach, empowering organizations to proactively address safety concerns and cultivate safer work environments. While delving into the software applications, the paper highlights the versatility of analytics in predicting incidents, monitoring compliance, and optimizing workloads. Challenges such as data quality, privacy concerns, and integration issues are acknowledged, yet the multitude of advantages showcased, including incident prevention, data-driven decision-making, and cost savings, affirm the transformative potential of business analytics in shaping a safer, more efficient, and healthier future workplace. Originality/value: Detailed analysis of business analytics in work and safety area. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in cost reduction. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The application of business analytics in cost reduction is paramount in today's fiercely competitive business landscape. This article highlights the pivotal role of business analytics in optimizing financial performance by identifying inefficiencies and areas for cost reduction. By harnessing the power of data, business analytics enables data-driven decision-making, differentiates between cost drivers, streamlines processes, and optimizes inventory and pricing strategies. Various methodologies and techniques, along with a diverse range of software solutions, support organizations in their cost reduction endeavors. Leveraging analytics not only provides a competitive edge but also enhances customer satisfaction, mitigates financial risks, and optimizes supplier relationships. This multifaceted approach empowers organizations to achieve cost reduction goals and secure long-term financial success. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in process optimization. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: In the era of Industry 4.0, the intersection of data-driven technologies and manufacturing processes underscores the indispensable role of business analytics in process optimization. This transformative landscape redefines business operations, highlighting data analytics as the cornerstone of operational excellence. Business analytics offers a multitude of advantages, including data-driven decision-making, efficiency enhancements, predictive capabilities, and cost reduction, ultimately ensuring a competitive edge. Nevertheless, it also presents a set of challenges, such as data quality, integration, and resistance to change. Overcoming these obstacles is essential to fully unlock the potential of business analytics in Industry 4.0. In doing so, organizations can navigate this dynamic landscape and secure their position in the ever-evolving industrial ecosystem. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in inventory management. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Industry 4.0 has ushered in a transformative era of technological innovation, particularly in inventory management. Business Analytics has emerged as a crucial tool for optimizing inventory control and supply chain management in this context. This integration of digital technologies, the Internet of Things, artificial intelligence, and big data has made inventory management more complex yet highly efficient. Business Analytics, driven by machine learning and predictive analytics, empowers organizations to make data-driven decisions, improve demand forecasting, and enhance supplier relationships, resulting in cost reduction, increased customer satisfaction, and sustainability efforts. Industry 4.0 has also introduced various software solutions that streamline inventory management, albeit with challenges. Overcoming these challenges, businesses leverage the advantages to adapt to a dynamic market, reduce costs, and enhance sustainability, solidifying the critical role of Business Analytics in inventory management in an evolving landscape. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in personalized customer experience. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The applications of business analytics in personalized customer experiences span data collection, segmentation, predictive analytics, and multi-channel personalization, delivering greater customer satisfaction, revenue, and competitive advantage. Achieving this transformation requires the use of various software solutions, including CRM systems, marketing automation tools, analytics platforms, predictive analytics software, and recommendation engines. Harnessing the power of data and analytics is the key to gaining a competitive edge in this customer-centric era. Businesses that tailor their offerings to each customer's unique preferences and needs can not only meet but exceed customer expectations, ensuring growth and long-term success. Ultimately, personalized customer experiences, driven by business analytics, are not merely a response to customer demands; they are the path to unlocking the full potential of customer relationships and establishing a strong position in the ever-evolving marketplace. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the usage of quality management methods in Quality 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The applications of business analytics in personalized customer experiences span data collection, segmentation, predictive analytics, and multi-channel personalization, delivering greater customer satisfaction, revenue, and competitive advantage. Achieving this transformation requires the use of various software solutions, including CRM systems, marketing automation tools, analytics platforms, predictive analytics software, and recommendation engines. Harnessing the power of data and analytics is the key to gaining a competitive edge in this customer-centric era. Businesses that tailor their offerings to each customer's unique preferences and needs can not only meet but exceed customer expectations, ensuring growth and long-term success. Ultimately, personalized customer experiences, driven by business analytics, are not merely a response to customer demands; they are the path to unlocking the full potential of customer relationships and establishing a strong position in the ever-evolving marketplace. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in energy management. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The advantages of using business analytics in energy management are substantial, including cost reduction, enhanced energy efficiency, and sustainability alignment. This approach also provides real-time monitoring and control capabilities, predictive maintenance, and compliance with regulatory requirements. However, it comes with challenges such as data integration complexity, data quality, and security issues, as well as the need to address high implementation costs and talent gaps. Business analytics is a game-changer in energy management within Industry 4.0, offering numerous benefits but also requiring organizations to overcome significant challenges. Embracing these complexities will enable organizations to thrive in this dynamic landscape while contributing to a sustainable and efficient future. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in energy management. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The advantages of using business analytics in energy management are substantial, including cost reduction, enhanced energy efficiency, and sustainability alignment. This approach also provides real-time monitoring and control capabilities, predictive maintenance, and compliance with regulatory requirements. However, it comes with challenges such as data integration complexity, data quality, and security issues, as well as the need to address high implementation costs and talent gaps. Business analytics is a game-changer in energy management within Industry 4.0, offering numerous benefits but also requiring organizations to overcome significant challenges. Embracing these complexities will enable organizations to thrive in this dynamic landscape while contributing to a sustainable and efficient future. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the potential usage of business analytics in Industry 4.0. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Business Analytics and Industry 4.0 represent a dynamic synergy that has the potential to revolutionize industries, drive sustainability, and elevate businesses to new heights. To fully realize these possibilities, organizations must invest in data strategies, talent development, and an organizational culture that embraces data-driven decision-making. The journey ahead is both challenging and promising, and those who navigate it adeptly will shape the future of industry and business in profound ways. Originality/value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in Industry 4.0. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in Industry 4.0. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Specifically, the paper discussed how business analytics is employed in predictive maintenance, supply chain optimization, and quality control. Predictive maintenance allows organizations to proactively address equipment failures, thereby reducing downtime and maintenance costs. Supply chain optimization optimizes resource allocation, minimizes costs, and improves customer service through data-driven decision-making. Quality control relies on data analytics to monitor, assess, and enhance product and service quality, ultimately leading to cost reduction and customer satisfaction. It is evident that business analytics is not merely a tool but a strategic imperative for organizations in the era of Industry 4.0. It empowers them to continuously improve their operations, mitigate risks, and stay ahead in a rapidly evolving business landscape. As technology and data analytics capabilities continue to advance, businesses that effectively leverage these tools will be better positioned to thrive in the dynamic and competitive world of Industry 4.0. Originality/value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in Industry 4.0. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the business analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The paper explores the main concepts of business analytics, including descriptive, real-time, diagnostic, predictive, and prescriptive analytics. Each stage of development builds upon the previous one, addressing specific needs in data analysis and decision-making. The paper also presents a detailed comparison of the five types of business analytics, showcasing their unique characteristics, techniques, and applications. Understanding these differences helps organizations select the appropriate analytics type to suit their requirements and drive success. As technology and data processing capabilities advance, business analytics continues to evolve. Embracing the power of data and analytics grants organizations a competitive advantage, unlocking opportunities and driving innovation. Integrating analytics into decision-making processes is essential for thriving in a data-driven world, ensuring sustained growth and success in an ever-changing marketplace. Originality/value: Detailed analysis of all subjects related to the problems connected with the prospective analytics. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the prospective analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Prescriptive analytics aims to assist businesses in making informed decisions that optimize desired outcomes or minimize undesired ones. It goes beyond predicting future outcomes and provides recommendations on the best actions to achieve desired goals while considering potential risks and uncertainties. Prescriptive analytics finds applications in various domains such as supply chain management, financial planning, healthcare, marketing, and operations management. It empowers businesses to make data-driven decisions, optimize resource allocation, enhance efficiency, and gain a competitive advantage. Considered the highest level of analytics, prescriptive analytics combines historical data, real-time information, optimization techniques, and decision models to generate actionable recommendations. Originality/value: Detailed analysis of all subjects related to the problems connected with the prospective analytics. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the diagnostic analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The paper discusses the concept of diagnostic analytics, which is a powerful tool for organizations to understand the underlying factors and reasons behind specific outcomes or events. By analyzing historical data and applying statistical techniques, organizations can identify root causes, patterns, and correlations that explain past events. This understanding enables informed decision-making, performance improvement, risk mitigation, enhanced customer insights, process optimization, resource allocation, and continuous improvement. Nevertheless, there are several challenges associated with diagnostic analytics. Firstly, the analysis process can be time-consuming due to the need for thorough examination and interpretation of data. Additionally, real-time insights may be limited as diagnostic analytics primarily focuses on historical data. Issues related to data quality and availability may also arise, impacting the accuracy and reliability of the analysis. Furthermore, diagnostic analytics lacks predictive capabilities, making it more challenging to anticipate future outcomes. The complexity of analysis, data privacy and security concerns, risks of bias and misinterpretation, and difficulties in identifying causal relationships further add to the challenges organizations face. Originality/value: Detailed analysis of all subjects related to the problems connected with the diagnostic analytics. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the predictive analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Predictive analytics is a powerful tool that leverages historical data and statistical models to forecast future outcomes and behaviors. It enables organizations to gain valuable insights, make informed decisions, and drive business growth. By analyzing patterns, correlations, and trends in data, predictive analytics can uncover hidden relationships and provide a deeper understanding of business processes, customer behavior, market trends, and other important factors. The benefits of predictive analytics are numerous. It enables organizations to forecast and predict future events, leading to proactive decision-making and the ability to anticipate trends and outcomes. It enhances decision-making processes, improves resource allocation, and provides enhanced customer insights. Predictive analytics also helps in risk mitigation, fraud detection, optimization of operations and pricing, product development, and marketing effectiveness. By leveraging these benefits, organizations can gain a competitive advantage and achieve sustainable success. Originality/value: Detailed analysis of all subjects related to the problems connected with the predictive analytics. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the descriptive analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The paper discusses the concept of descriptive analytics, which involves collecting, cleaning, and summarizing historical data from various sources to provide a clear and concise summary that can aid in decision-making. The paper explains the importance of descriptive analytics as the foundation for other types of data analytics, and highlights the steps involved in its implementation, including data collection, cleaning and preparation, exploration and visualization, analysis, interpretation, and reporting. The paper also mentions the advantages of descriptive analytics, such as identifying trends and patterns, optimizing processes, improving decision-making, and simplifying communication, while cautioning businesses about the potential pitfalls and challenges of this approach, such as limited predictive power, incomplete data, data privacy concerns, biased results, and overreliance on historical data. The paper emphasizes the importance of understanding these issues to ensure that the insights generated are relevant, accurate, and useful. Originality/value: Detailed analysis of all subjects related to the problems connected with the descriptive analytics. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the real-time analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The paper focus on the advantages and disadvantages of real-time analytics. The ability to process and analyze data in real-time allows organizations to quickly identify trends and patterns, optimize their operations, and allocate resources more efficiently. Additionally, real-time analytics helps businesses identify new revenue opportunities and optimize their pricing strategies, monitor user behavior, detect security threats, and react without delay. However, real-time analytics can be expensive to implement, require technical expertise, and generate false positives. Proper data quality, security measures, and system scaling are also essential for effective implementation. The vague definition of real-time and the requirement to collect detailed requirements from all stakeholders can also present challenges to businesses. Originality/value: Detailed analysis of all subjects related to the problems connected with the real-time analytics. [ABSTRACT FROM AUTHOR]

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