Continuous flow microfluidics for colloidal particle assembly on porous substrates

Lochab, Varun; Ewim, Daniel Raphael Ejike; Prakash, Shaurya

doi:10.1039/d2sm01414a

Cited by 10 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equally important is initiating change management practices to address fears and resistance, emphasizing AI's value to employees' roles and the organization. • Data Governance: Establishing robust data governance practices ensures that data feeding into AI systems is accurate, consistent, and clean (Lochab et al, 2023;Muteba et al, 2023). This involves setting up data management protocols, regular data audits, and ensuring compliance with data protection regulations.…”

Section: Strategies For Integrationmentioning

confidence: 99%

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

Olubunmi Adeolu Adenekan,

Nko Okina Solomon,

Peter Simpa

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

show abstract

Section: Strategies For Integrationmentioning

confidence: 99%

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

Olubunmi Adeolu Adenekan,

Nko Okina Solomon,

Peter Simpa

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

show abstract

“…By leveraging these datasets effectively, businesses can gain actionable insights, enhance customer experiences, and drive competitive advantage in today's dynamic marketplace. Overview Artificial Intelligence (AI) has emerged as a powerful tool for marketers, offering innovative solutions to develop strategic marketing goals and drive business growth , Lochab, Ewim & Prakash, 2023, Thompson, et. al., 2022.…”

Section: Key Datasetmentioning

confidence: 99%

Harnessing artificial intelligence to develop strategic marketing goals

Kikelomo Fadilat Anjorin,

Mustafa Ayobami Raji,

Hameedat Bukola Olodo

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

In today's rapidly evolving business landscape, harnessing artificial intelligence (AI) has become imperative for organizations striving to remain competitive. This concept paper explores the integration of AI in developing strategic marketing goals, aiming to provide a comprehensive framework for leveraging AI's capabilities effectively. The paper begins by outlining the current state of AI in marketing, highlighting its transformative potential across various facets such as customer segmentation, personalized targeting, predictive analytics, and automation. It underscores the necessity for businesses to embrace AI-driven approaches to stay ahead in an increasingly data-driven marketplace. Moreover, the concept paper delves into the strategic implications of AI adoption in marketing. It emphasizes the need for alignment between AI initiatives and overarching business objectives, emphasizing the role of AI in enhancing customer experience, optimizing resource allocation, and driving revenue growth. Furthermore, the paper addresses key considerations and challenges associated with integrating AI into marketing strategies, including data privacy concerns, ethical implications, and organizational readiness. It advocates for a structured approach to AI implementation, encompassing robust data governance, talent acquisition, and continuous monitoring to mitigate risks effectively. Drawing upon industry best practices and case studies, the concept paper elucidates practical methodologies for leveraging AI in setting strategic marketing goals. It emphasizes the importance of data-driven decision-making, iterative experimentation, and agile adaptation to capitalize on AI's potential for driving innovation and competitive advantage. In conclusion, the concept paper advocates for a proactive stance towards embracing AI in marketing strategy formulation. By strategically harnessing AI's capabilities, organizations can not only enhance their marketing effectiveness but also cultivate long-term sustainable growth in today's dynamic business environment. Keywords: AI, Strategic, Marketing, Environment, Goals, Business.

show abstract

“…The historical context of cybersecurity's evolution in relation to environmental sustainability reveals a landscape where technological advancements and sustainability goals are increasingly intertwined. The protection of digital infrastructure is paramount in supporting sustainable development initiatives, from smart cities to agricultural practices (Lochab et al, 2023). As such, cybersecurity is not merely a technical concern but a fundamental component of the broader sustainability agenda, ensuring that digital technologies can be leveraged safely and effectively to achieve environmental, economic, and social sustainability goals.…”

Section: Historical Overview: the Evolution Of Cybersecurity In The C...mentioning

confidence: 99%

Cybersecurity’s Role in Environmental Protection and Sustainable Development: Bridging Technology and Sustainability Goals

Scholar Chinenye Obasi,

Nko Okina Solomon,

Olubunmi Adeolu Adenekan

et al. 2024

Comput. sci. IT res. j.

View full text Add to dashboard Cite

This study investigates the pivotal role of cybersecurity in bolstering environmental protection and sustainable development, a critical yet underexplored nexus in contemporary research. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, conference proceedings, and industry reports from 2015 to 2023, sourced from databases such as IEEE Xplore, ScienceDirect, and Google Scholar. The methodology is anchored in a rigorous search strategy, leveraging keywords related to cybersecurity, sustainability, and communication technologies, and adheres to defined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings highlight cybersecurity as an indispensable enabler of sustainable development initiatives, safeguarding the technological infrastructure essential for environmental conservation efforts. The study identifies evolving cyber threats as a significant challenge, necessitating adaptive security measures that anticipate and mitigate potential vulnerabilities. Furthermore, it underscores the opportunities presented by advanced cybersecurity technologies, such as artificial intelligence and blockchain, in enhancing the security and efficiency of sustainable practices. Strategic recommendations emphasize the need for comprehensive cybersecurity frameworks, stakeholder collaboration, cybersecurity education, and alignment with regulatory standards to fortify the resilience of sustainability initiatives against cyber threats. The study concludes that integrating robust cybersecurity measures is paramount in the pursuit of sustainable development goals, calling for ongoing vigilance, innovation, and interdisciplinary collaboration to navigate the complex landscape of digital threats and opportunities. This research contributes valuable insights into the critical intersection of cybersecurity and sustainability, offering a foundation for future studies and strategic initiatives aimed at securing sustainable development in the digital age. Keywords: Cybersecurity, Sustainable Development, Environmental Protection, Advanced Security Technologies.

show abstract

Continuous flow microfluidics for colloidal particle assembly on porous substrates

Abstract: Self-assembly of colloidal particles for ‘bottom-up’ fabrication of various patterns and structures is critical for a range of applications including, but not limited to, energy migration, material science, biomimetics, and...

Cited by 10 publications

References 46 publications

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

Harnessing artificial intelligence to develop strategic marketing goals

Cybersecurity’s Role in Environmental Protection and Sustainable Development: Bridging Technology and Sustainability Goals

Contact Info

Product

Resources

About