13 research outputs found
FAIDS: artificial intelligence developmental systems framework for predicting and preventing cyberattacks in supply chain networks
No full textCyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network\u27s resilience and security. The research provides a novel and comprehensive AI framework for cyber security (CS) and supply chain management. The research also discusses the framework\u27s limitations and challenges and suggests future research
FAIDS: Artificial Intelligence Developmental Systems Framework for Predicting and Preventing Cyberattacks in Supply Chain Networks
No full textCyber threats and attacks disrupt and damages supply chain networks (SCN), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental system framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network’s resilience and security. The research provides a novel and comprehensive AI framework for cyber security (CS) and supply chain management. The research also discusses the framework’s limitations and challenges and suggests future research
AI-Powered Operations: Navigating Ethics, Automation, and Strategic Innovation in the Digital Era
No full textArtificial Intelligence (AI) is redefining Operations Management (OM) by transforming how organizations plan, execute, and optimize their core processes. This TREO Talk presents a comprehensive synthesis of AI’s evolution and impact in OM, underscoring its role as a strategic enabler of agility, resilience, and innovation in digitally driven enterprises. Leveraging a Systematic Literature Review (SLR) guided by PRISMA methodology across nine scholarly databases, this research constructs a validated framework that captures the multidimensional role of AI in transforming operational strategies, capabilities, and outcomes across industries. AI\u27s influence spans predictive analytics, robotic process automation, supply chain orchestration, logistics, and real-time quality assurance. For instance, Amazon’s AI-infused inventory systems dynamically anticipate demand shifts, while UPS’s ORION platform uses AI to optimize delivery networks in real time. Siemens’ smart factories deploy AI-driven computer vision and predictive maintenance to reduce downtime and improve throughput. These implementations exemplify how AI facilitates precision, scalability, and responsiveness - empowering organizations to operate more intelligently, minimizing waste, and delivering higher value to stakeholders in increasingly volatile markets. To guide effective integration, this work introduces a multidimensional Adoption Framework comprising: (1) Antecedents—technological readiness, strategic alignment, and data infrastructure; (2) Implementation—structured planning, integration, and performance evaluation; (3) Challenges—ranging from ethical risks and algorithmic opacity to workforce displacement and compliance mandates; and (4) Outcomes including enhanced operational visibility, cost optimization, and accelerated decision cycles. The framework serves as a strategic blueprint for organizations aiming to scale AI adoption effectively, ensuring alignment between technological advancement and business value creation while navigating the inherent risks of digital transformation. The future of AI in OM is shaped by three paradigm shifts. First, AI-driven sustainability is revolutionizing energy and resource efficiency, enabling greener, more responsible operations. Second, ethical AI is emerging as an operational imperative, demanding transparency, accountability, and algorithmic fairness through explainable AI and bias audits (Rai, 2020; Sharma & Sheth, 2021). Third, human-AI collaboration is empowering hybrid intelligence, where machine learning augments human judgment to enhance creativity, contextual decision-making, and adaptability in complex operational environments. This synergy fosters a new model of collaborative operations that leverages the strengths of both humans and machines. This abstract is a call to action for the Information Systems (IS) community to lead in shaping the next frontier of intelligent operations. It offers scholars a rigorous framework for future research, provides educators with insights to inform curriculum design, and delivers practitioners a strategic roadmap for deploying ethical, sustainable, and scalable AI across value chains. Ultimately, this work positions AI not merely as a technological enhancement, but as a transformative force for building the next generation of intelligent, ethical, and resilient operational ecosystems
Dimensions of Artificial Intelligence Maturity Models in Supply Chain Management: A Systematic Literature Review
No full textIn an era where Artificial Intelligence (AI) is revolutionizing industries, understanding its integration within Supply Chain Management (SCM) is paramount. The burgeoning integration of AI into SCM necessitates a robust framework to assess the readiness and maturity of supply chains in adopting AI technologies. The study identifies existing SCM maturity models, delineates their dimensions, and examines the challenges associated with employing these models to measure the maturity level of SCM with respect to integrating AI. By synthesizing the findings, this study underscores AI’s role in optimizing SCM, highlighting key maturity dimensions, enriching the AI maturity discourse, proposing a dimensionality tailored to SCM’s unique demands, and practically it advocates for the development of standardized capability maturity model (CMM) to guide organizations towards AI-driven SCM excellence that navigates digital transformation, enhancing competitive advantage and addressing SCM challenges
AI Capability and Supply Chain Performance: A Systematic Literature Review
No full textSupply chain management (SCM) faces challenges such as uncertainty, volatility, and disruption. Artificial intelligence (AI) can improve SCM by enabling data-driven decision-making, automation, and optimization. However, the literature on AI in SCM is fragmented and a systematic review is lacking. This study evaluated the effectiveness of AI capabilities on supply chain performance through a systematic literature review (SLR) of empirical studies. SLR follows PRISMA guidelines and answers three research questions: (1) What are the types and amounts of AI capabilities in SCM? (2) How do AI capabilities affect supply chain performance? (3) What factors moderate or mediate the relationship between artificial intelligence and performance? SLR searches large databases for relevant articles and uses a thematic analysis approach to synthesize the data. The study explored the application of artificial intelligence in SCM, identified gaps and limitations in the literature, and provided implications and recommendations for managers
Strategic Leadership in Cybersecurity Risk Management: Elevating the Role of Executive Managers
No full textAs the frequency and sophistication of cyber threats continue to escalate, cybersecurity has evolved from a technical concern to a central pillar of strategic enterprise governance. This TREO Talk presents the findings of a rigorous systematic literature review (SLR) encompassing 69 scholarly publications selected using the PRISMA 2020 framework. The study investigates how executive managers (EMs) influence the success of cybersecurity risk management (CRM) programs and frames their participation as both a requirement and a competitive advantage. Despite global investment in security infrastructure, many organizations remain vulnerable to breaches, largely due to the absence of sustained executive leadership in CRM. This research exposes a persistent gap between technical risk solutions and the strategic insight required at the executive level to integrate these solutions across business functions. Drawing from multidisciplinary sources, this work identifies that EMs who actively shape and lead CRM policies achieve superior outcomes in resilience, compliance, stakeholder trust, and organizational agility. The study introduces the \u27Action and Remediation Zone Framework\u27—a novel conceptual model that delineates two interconnected zones of executive involvement: (1) the Action Zone, encompassing proactive functions such as risk appetite definition, cybersecurity budgeting, strategy alignment, and enterprise-wide awareness cultivation; and (2) the Regeneration Zone, focusing on post-incident leadership roles including crisis communication, accountability, trust rebuilding, and systemic improvement. The framework synthesizes the best practices from ISO/IEC 27001, COBIT, and the NIST Cybersecurity Framework, reframing them to center executive agency in CRM decision-making. This TREO Talk will showcase how EMs can influence technical decisions through strategic foresight and governance alignment, reinforcing that their presence is indispensable at every critical juncture—from setting organizational tone to navigating breach recovery. It will also demonstrate that cybersecurity success depends not only on tools and technologies but on visionary leadership that treats CRM as a shared, cultural priority. Furthermore, the session will engage the IS community in discussing future research directions, including empirical validation of the proposed framework, metrics for assessing executive involvement, and longitudinal studies exploring CRM maturity across sectors. By placing EMs at the heart of CRM, this work lays a foundation for redefining cybersecurity leadership in the age of digital transformation and persistent threats. Attendees will gain both theoretical frameworks and actionable insights to influence policy, shape organizational culture, and architect resilient security strategies from the top down
An Analysis of Executive Managers Acceptance of Cyber Security Risk Management – A Systematic Review
No full textInformation security is vital for safeguarding critical assets and services from cyber threats, but it incurs significant organizational costs and technological reliance, raising questions about its value and necessity. Security is not merely a technical issue but a strategic one requiring executive managers\u27 involvement. This study examines how executive management\u27s participation in information security risk management (ISRM) affects organizational security. A systematic literature review of 69 articles identifies the aspects and impacts of executive managers\u27 (EM) involvement in cybersecurity risk management (CRM). Findings indicate that EM involvement is crucial for corporate strategy and business success, enhancing security, visibility and accountability at higher levels. EMs play a key role in protecting critical assets, aligning security strategy with business goals, and fostering a culture of awareness and responsibility. The paper proposes a best practice framework for maintaining EM involvement in CRM, aligning cybersecurity strategy with organizational goals while balancing costs and benefits
Unveiling AI Maturity Dimensions for Strategic Transformation in Supply Chain Management
No full textArtificial Intelligence (AI) is emerging as a cornerstone technology in reshaping how supply chains operate, compete, and evolve in the digital era. As supply chains grow more interconnected and data-driven, the strategic integration of AI offers significant potential to optimize logistics, enhance forecasting accuracy, drive sustainable practices, and elevate resilience against global disruptions. However, a critical challenge persists: despite widespread enthusiasm for AI, organizations often lack a robust, standardized Capability Maturity Model (CMM) that can diagnose their readiness, benchmark progress, and guide incremental adoption within the unique and multifaceted contexts of Supply Chain Management (SCM). This TREO Talk addresses this pressing issue by introducing the results of an exhaustive Systematic Literature Review (SLR), designed to identify, compare, and synthesize existing AI maturity models with explicit focus on their applicability to SCM. Our research systematically surveyed 955 articles across nine academic databases, including IEEE, Scopus, and Web of Science - applying PRISMA 2021 protocols and rigorous inclusion/exclusion criteria. Sixty-six qualified studies were synthesized to reveal not only the recurring maturity dimensions but also the critical gaps in model coverage, validation, and scalability. Key maturity dimensions distilled include Data Processing, Analytics & Insight Generation, Automation & Decision Support, System Integration, Innovation & Learning, and Operational Resilience. These dimensions form the basis for a proposed SCM-centric AI CMM that accommodates dynamic business needs, real-time data flows, and cross-functional stakeholder interactions. What sets this contribution apart is its comprehensive treatment of organizational readiness as a multi-level construct spanning strategy, ethics, infrastructure, talent, and governance. Our proposed model introduces 12 strategic enablers of maturity, including ethical AI compliance, collaboration ecosystems, talent development pathways, and advanced performance measurement capabilities. Moreover, it offers practical mechanisms for mapping current capabilities, identifying gaps, and prioritizing AI investment through well-defined Key Performance Indicators (KPIs) and maturity benchmarks. This TREO session seeks to create a forum for interdisciplinary engagement, drawing on perspectives from information systems, operations, and digital innovation scholars. Attendees will engage in critical reflection on the model’s structure, its adaptability across sectors, and its potential integration with digital twins, blockchain, and IoT frameworks. Emphasis will be placed on the path toward empirical validation, cross-industry benchmarking, and AI governance frameworks that ensure accountability and trust. Ultimately, this TREO Talk aims to stimulate scholarly discourse and practical momentum around a transformative maturity model that empowers organizations to unlock AI\u27s strategic value and align it with the demands of next-generation supply chain ecosystems
AI-Powered Operations: Navigating Ethics, Automation, and Strategic Innovation in the Digital Era
No full textArtificial Intelligence (AI) is redefining Operations Management (OM) by transforming how organizations plan, execute, and optimize their core processes. This TREO Talk presents a comprehensive synthesis of AI’s evolution and impact in OM, underscoring its role as a strategic enabler of agility, resilience, and innovation in digitally driven enterprises. Leveraging a Systematic Literature Review (SLR) guided by PRISMA methodology across nine scholarly databases, this research constructs a validated framework that captures the multidimensional role of AI in transforming operational strategies, capabilities, and outcomes across industries. AI\u27s influence spans predictive analytics, robotic process automation, supply chain orchestration, logistics, and real-time quality assurance. For instance, Amazon’s AI-infused inventory systems dynamically anticipate demand shifts, while UPS’s ORION platform uses AI to optimize delivery networks in real time. Siemens’ smart factories deploy AI-driven computer vision and predictive maintenance to reduce downtime and improve throughput. These implementations exemplify how AI facilitates precision, scalability, and responsiveness - empowering organizations to operate more intelligently, minimizing waste, and delivering higher value to stakeholders in increasingly volatile markets. To guide effective integration, this work introduces a multidimensional Adoption Framework comprising: (1) Antecedents—technological readiness, strategic alignment, and data infrastructure; (2) Implementation—structured planning, integration, and performance evaluation; (3) Challenges—ranging from ethical risks and algorithmic opacity to workforce displacement and compliance mandates; and (4) Outcomes including enhanced operational visibility, cost optimization, and accelerated decision cycles. The framework serves as a strategic blueprint for organizations aiming to scale AI adoption effectively, ensuring alignment between technological advancement and business value creation while navigating the inherent risks of digital transformation. The future of AI in OM is shaped by three paradigm shifts. First, AI-driven sustainability is revolutionizing energy and resource efficiency, enabling greener, more responsible operations. Second, ethical AI is emerging as an operational imperative, demanding transparency, accountability, and algorithmic fairness through explainable AI and bias audits (Rai, 2020; Sharma & Sheth, 2021). Third, human-AI collaboration is empowering hybrid intelligence, where machine learning augments human judgment to enhance creativity, contextual decision-making, and adaptability in complex operational environments. This synergy fosters a new model of collaborative operations that leverages the strengths of both humans and machines. This abstract is a call to action for the Information Systems (IS) community to lead in shaping the next frontier of intelligent operations. It offers scholars a rigorous framework for future research, provides educators with insights to inform curriculum design, and delivers practitioners a strategic roadmap for deploying ethical, sustainable, and scalable AI across value chains. Ultimately, this work positions AI not merely as a technological enhancement, but as a transformative force for building the next generation of intelligent, ethical, and resilient operational ecosystems
Dimensions of Artificial Intelligence Maturity Models in Supply Chain Management: A Systematic Literature Review
No full textIn an era where Artificial Intelligence (AI) is revolutionizing industries, understanding its integration within Supply Chain Management (SCM) is paramount. The burgeoning integration of AI into SCM necessitates a robust framework to assess the readiness and maturity of supply chains in adopting AI technologies. The study identifies existing SCM maturity models, delineates their dimensions, and examines the challenges associated with employing these models to measure the maturity level of SCM with respect to integrating AI. By synthesizing the findings, this study underscores AI’s role in optimizing SCM, highlighting key maturity dimensions, enriching the AI maturity discourse, proposing a dimensionality tailored to SCM’s unique demands, and practically it advocates for the development of standardized capability maturity model (CMM) to guide organizations towards AI-driven SCM excellence that navigates digital transformation, enhancing competitive advantage and addressing SCM challenges
