Data Analytics in Insurance Product Management DOI Creative Commons

Sulochan Lohani,

Nimisha Asthana,

Osama Mohammad

и другие.

Deleted Journal, Год журнала: 2024, Номер 6(1), С. 594 - 599

Опубликована: Дек. 15, 2024

Data analytics as a part of insurance product management is revolutionizing the industry because with huge and constantly increasing piles customer claims data at their fingertips, insurers can make better decisions improve many aspects operations. This paper discusses how adaptation risk models artificial intelligence helps to evaluation criteria policy premiums, well predict occurrence high degree certainty. Challenging segments be detected using big analytics, which serve clients gain trust. In addition, there also function detect frauds hence predictive potential since they identify cer tain patterns. However, application in has some difficulties terms quality, privacy, human resources analyze sophisticated data. These challenges demand strong investments infrastructure for storage, terrible, processing recruitment training skilled professionals, together solid governance mentality. abstract establishes that, addition improving internal processes within organizations, increases market competitiveness, innovation, focus products.

Язык: Английский

Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow DOI Creative Commons
Mohammad Shamsuddoha, Eijaz Ahmed Khan, Md. Maruf Hossan Chowdhury

и другие.

Information, Год журнала: 2025, Номер 16(1), С. 26 - 26

Опубликована: Янв. 6, 2025

Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, profitability. This paper examines how AI, machine learning (ML), robotic process (RPA) influence operations to adjust risks vulnerabilities. It focuses on AI other relevant technologies will enhance forecasting predict actual demand, expedite logistics, increase warehouse efficiency, promote instantaneously making decisions. study utilizes thematic analysis find AI-driven applications, including logistics optimization, risk mitigation, among 383 peer-reviewed articles (2017–2024). provides a strategic framework dealing with vulnerabilities, operational excellence, resilient solutions. Additionally, research investigates contributes resilience by predicting disruptions automating mitigation strategies. identifies critical success factors challenges in adopting intelligent analyzing real-world industry implementations. The findings propose organizations aiming leverage achieve agility, real-time information flow effective decision-making.

Язык: Английский

Процитировано

1
A literature review on transformative impacts of blockchain technology on manufacturing management and industrial engineering practices DOI Creative Commons
Dharmendra Hariyani, Poonam Hariyani, Sanjeev Mishra

и другие.

Green Technologies and Sustainability, Год журнала: 2025, Номер unknown, С. 100169 - 100169

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0 DOI Creative Commons
Özlem Sabuncu, Bülent Bilgehan

Journal of Metaverse, Год журнала: 2025, Номер 5(1), С. 64 - 72

Опубликована: Март 21, 2025

Predictive maintenance now heavily relies on digital twins and the Internet of Things (IoT), which allow industrial assets to be monitored decisions made in real time. However, adding human components conventional optimization processes creates new difficulties as Industry 5.0 moves toward human-centric systems. Existing frameworks frequently disregard preferences, intuition, safety considerations, makes operators distrustful unwilling accept them. To enable predictive maintenance, this paper presents a novel multi-objective framework that incorporates feedback into IoT-driven twins. The uses an enhanced particle swarm (PSO) algorithm reconcile competing goals, including maintaining operator safety, optimizing asset reliability, minimizing costs. Furthermore, tasks are adaptively scheduled using built-in reinforcement learning (RL) optimized model parameters fine-tuned for improved accuracy Bayesian optimization. latter is based real-time operational data. In addition promoting safer working environment, suggested approach shows significant reduction unplanned downtime This research contributes development more resilient, adaptive, collaborative systems by aligning with principles 5.0. proposed was tested duration achieved improvement 10 100 hours. further compared PSO algorithm, demonstrating its superiority 7.5% total cost 6.3% decrease downtime. These improvements contribute efficiency better human-machine collaboration unnecessary interventions resource allocation.

Язык: Английский

Процитировано

0
Ethical and Legal Implications of Data in Industry 5.0: Navigating a Hyper-Connected Landscape DOI Creative Commons
Sabir Hussain

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

With the advent of Industry 5.0 and increasing advancements in this area, ethical legal challenges have been surmounted. This chapter will discuss these issues context privacy, fairness, security along with evolving relationship between humans machines at some length. By doing so, an extensive look be made on how data is collected through various means its usage, trade-offs privacy can maintained goods delivered. Similarly, posed by algorithms biases touched. Moving ahead, traverse frameworks, relevant laws, their implications, what global efforts are required. Moreover, case studies best practices like design, accountability, cooperation stakeholders discussed viz-a-viz role technology. In end, online collaboration adaptation to ensure responsible approach 5.0.

Язык: Английский

Процитировано

0
Industry 5.0 in Manufacturing: Enhancing Resilience and Responsibility through AI-Driven Predictive Maintenance, Quality Control, and Supply Chain Optimization DOI Creative Commons

Rachid Ejjami -,

Khaoula Boussalham -

International Journal For Multidisciplinary Research, Год журнала: 2024, Номер 6(4)

Опубликована: Авг. 4, 2024

This integrative literature review investigates the transformative impact of artificial intelligence (AI) on manufacturing, focusing AI-driven predictive maintenance, machine learning-based quality control, and supply chain optimization. By examining current literature, study highlights AI's potential to automate revolutionize manufacturing operations, enhancing efficiency, resilience, transparency. The study's conceptual framework is grounded in three primary pillars: optimization, analytics, each contributing overall enhancement methodology involves a comprehensive scholarly articles, reports, academic publications, AI applications analysis reveals significant improvements operational efficiency resilience due AI, alongside concerns about biases, transparency, implementation issues. findings confirm but emphasize necessity for ongoing supervision, regular audits, development models capable detecting rectifying anomalies. proposes creating jobs such as Manufacturing Oversight Officer (AIMOO), Compliance (AIMCO), Quality Assurance (AIMQAO) ensure responsible utilization, maintaining integrity operations while addressing challenges. concludes that promising transforming manufacturing; however, careful crucial uphold resilience. Future research should prioritize longitudinal studies evaluate long-term impact, focus concerns, fair transparent integration technologies. These have implications practice policy, underscoring need robust frameworks regulatory measures guide effective use manufacturing.

Язык: Английский

Процитировано

1
Retail 5.0: Creating Resilient and Customer-Centric Shopping Experiences through Advanced Technologies DOI Creative Commons
Rachid Ejjami -,

Nurul Ain Fatizah Rahim

International Journal For Multidisciplinary Research, Год журнала: 2024, Номер 6(4)

Опубликована: Авг. 10, 2024

This literature study examines the significant changes by Industry 5.0 in retail industry. It explores sophisticated technologies such as artificial intelligence (AI) and Internet of Things (IoT) to develop robust customized shopping experiences. The emphasizes transformative potential these operations, evidenced current literature. underlines their ability improve productivity, customer satisfaction, data security. study's conceptual framework is based on three main pillars: AI-powered customization, IoT-facilitated supply chain management, security ethics. Each element adds improving efficiency, resilience, customer-centric focus. technique entails thoroughly examining scholarly articles, studies, academic publications, with a specific focus implementing AI IoT paper unveils notable enhancements operational efficiency experience due technology, while highlighting concerns around privacy, ethical practices, implementation challenges. results validate impact that can have industry, importance continuous oversight, frequent evaluations, creation models identify correct irregularities. suggests establishment positions like an Retail Oversight Officer (AIROO), Compliance (AIRCO), Customer Experience (AICEO) guarantee responsible use AI, uphold integrity effectiveness tackle difficulties. ILR indicates adoption modern has revolutionize but it using cautiously maintain preserve confidence. These findings consequences for new retail, emphasizing necessity solid frameworks regulatory measures ensure practical usage. recommended future research give priority conducting longitudinal studies order assess long-term effects technologies. should be addressing related ensuring fair transparent integration sector.

Язык: Английский

Процитировано

0
Data Analytics in Insurance Product Management DOI Creative Commons

Sulochan Lohani,

Nimisha Asthana,

Osama Mohammad

и другие.

Deleted Journal, Год журнала: 2024, Номер 6(1), С. 594 - 599

Опубликована: Дек. 15, 2024

Data analytics as a part of insurance product management is revolutionizing the industry because with huge and constantly increasing piles customer claims data at their fingertips, insurers can make better decisions improve many aspects operations. This paper discusses how adaptation risk models artificial intelligence helps to evaluation criteria policy premiums, well predict occurrence high degree certainty. Challenging segments be detected using big analytics, which serve clients gain trust. In addition, there also function detect frauds hence predictive potential since they identify cer tain patterns. However, application in has some difficulties terms quality, privacy, human resources analyze sophisticated data. These challenges demand strong investments infrastructure for storage, terrible, processing recruitment training skilled professionals, together solid governance mentality. abstract establishes that, addition improving internal processes within organizations, increases market competitiveness, innovation, focus products.

Язык: Английский

Процитировано

0