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Tesisquare
Why we think this paper is great for you:
This paper directly aligns with your interest in leveraging AI for supply chain improvements, specifically focusing on sustainability assessment and document intelligence. It offers insights into practical AI applications within supply chain operations.
This paper directly aligns with your interest in leveraging AI for supply chain improvements, specifically focusing on sustainability assessment and document intelligence. It offers insights into practical AI applications within supply chain operations.
Abstract
This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.
This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.
AI Summary
- AI-assisted Human-in-the-Loop (HITL) and multi-agent agentic AI workflows achieve substantial reductions of 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to fully manual document processing in supply chains. [3]
- Enabling the LLM's 'thinking mode' for complex reasoning increases per-document energy consumption by approximately 56% but still remains vastly more sustainable than manual alternatives for high-token documents. [3]
- The replicability use case demonstrates 100% numerical accuracy in extracting structured data from complex proforma invoices, validating the agentic AI's capability for high-quality, sustainable document intelligence. [3]
- Organizations can dynamically balance cognitive performance and ecological impact by selectively activating resource-intensive AI features (e.g., deep LLM reasoning, multi-agent verification) only for cases of true complexity, optimizing eco-efficiency. [3]
- Human-in-the-Loop (HITL) / AI-in-the-Loop (AI2L): A collaborative paradigm where human operators remain the primary agents controlling the system, with AI providing support, guidance, validation, and correction to algorithmic outputs. [3]
- Full agentic configurations, leveraging advanced reasoning ('thinking mode') and multi-agent validation, deliver significant sustainability gains over human-only approaches, even if resource usage slightly increases compared to simpler AI-assisted solutions. [2]
- The paper introduces a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions, integrating performance, energy, and emission indicators to quantify environmental impact. [2]
- Multi-agent systems (MAS) incorporating specialized parser and verifier agents, while consuming more resources than basic HITL, maintain an order of magnitude lower daily energy usage and carbon emissions than manual workflows, enhancing accuracy and robustness. [2]
- Agentic AI: Autonomous software entities characterized by advanced cognitive functions, capable of sophisticated reasoning, multi-step workflow planning, and dynamic action upon the external environment through integrated tool utilization. [2]
- Belief-Desire-Intention (BDI) framework: A hybrid agent architecture that manages cognitive complexity through Beliefs (knowledge about the environment), Desires (objectives to achieve), and Intentions (committed plans to execute). [1]
Why we think this paper is great for you:
This research is highly relevant to your focus on pricing strategies, exploring optimal pricing for AI services using advanced game theory models. It provides valuable perspectives on dynamic pricing in an AI-driven market.
This research is highly relevant to your focus on pricing strategies, exploring optimal pricing for AI services using advanced game theory models. It provides valuable perspectives on dynamic pricing in an AI-driven market.
Abstract
The proliferation of Large Language Models (LLMs) has established LLM routing as a standard service delivery mechanism, where users select models based on cost, Quality of Service (QoS), among other things. However, optimal pricing in LLM routing platforms requires precise modeling for dynamic service markets, and solving this problem in real time at scale is computationally intractable. In this paper, we propose \PriLLM, a novel practical and scalable solution for real-time dynamic pricing in competitive LLM routing. \PriLLM models the service market as a Stackelberg game, where providers set prices and users select services based on multiple criteria. To capture real-world market dynamics, we incorporate both objective factors (\eg~cost, QoS) and subjective user preferences into the model. For scalability, we employ a deep aggregation network to learn provider abstraction that preserve user-side equilibrium behavior across pricing strategies. Moreover, \PriLLM offers interpretability by explaining its pricing decisions. Empirical evaluation on real-world data shows that \PriLLM achieves over 95\% of the optimal profit while only requiring less than 5\% of the optimal solution's computation time.
The proliferation of Large Language Models (LLMs) has established LLM routing as a standard service delivery mechanism, where users select models based on cost, Quality of Service (QoS), among other things. However, optimal pricing in LLM routing platforms requires precise modeling for dynamic service markets, and solving this problem in real time at scale is computationally intractable. In this paper, we propose \PriLLM, a novel practical and scalable solution for real-time dynamic pricing in competitive LLM routing. \PriLLM models the service market as a Stackelberg game, where providers set prices and users select services based on multiple criteria. To capture real-world market dynamics, we incorporate both objective factors (\eg~cost, QoS) and subjective user preferences into the model. For scalability, we employ a deep aggregation network to learn provider abstraction that preserve user-side equilibrium behavior across pricing strategies. Moreover, \PriLLM offers interpretability by explaining its pricing decisions. Empirical evaluation on real-world data shows that \PriLLM achieves over 95\% of the optimal profit while only requiring less than 5\% of the optimal solution's computation time.
Georgia Tech
Why we think this paper is great for you:
This paper addresses critical aspects of supply chain management, such as capacity planning and resource allocation, which are central to optimizing supply and demand. It offers modern approaches to enhance efficiency in complex supply chain ecosystems.
This paper addresses critical aspects of supply chain management, such as capacity planning and resource allocation, which are central to optimizing supply and demand. It offers modern approaches to enhance efficiency in complex supply chain ecosystems.
Abstract
With the growth of data-driven services and expansion of mobile application usage, traditional methods of capacity and resource planning methods may not be efficient and often fall short in meeting rapid changes in the business landscape. Motivated by modularity, containerization, and open sharing concepts from Physical Internet (PI), this paper proposes an effective approach to determine facility capacity and production schedule to meet current and future demands by dynamically allocating Mobile Production Containers (MPCs). In this work, we develop an iterative two-stage decision making model with dynamic rolling horizon approach. The first stage is capacity planning stage, where the model determines key decisions such as project selection, facility opening periods and project-facility assignment. The second stage is resource planning stage, where the MPC allocation and relocation schedule and weekly production schedule are decided. To validate the proposed model, we conduct a case study over a modular construction supply chain focusing on the southeast US region. The results demonstrate our model not only delivers a consistent production schedule with balanced workload but also enhances resource utilization, leading to cost effectiveness.
With the growth of data-driven services and expansion of mobile application usage, traditional methods of capacity and resource planning methods may not be efficient and often fall short in meeting rapid changes in the business landscape. Motivated by modularity, containerization, and open sharing concepts from Physical Internet (PI), this paper proposes an effective approach to determine facility capacity and production schedule to meet current and future demands by dynamically allocating Mobile Production Containers (MPCs). In this work, we develop an iterative two-stage decision making model with dynamic rolling horizon approach. The first stage is capacity planning stage, where the model determines key decisions such as project selection, facility opening periods and project-facility assignment. The second stage is resource planning stage, where the MPC allocation and relocation schedule and weekly production schedule are decided. To validate the proposed model, we conduct a case study over a modular construction supply chain focusing on the southeast US region. The results demonstrate our model not only delivers a consistent production schedule with balanced workload but also enhances resource utilization, leading to cost effectiveness.
Credere Associates LLC
Why we think this paper is great for you:
This paper is a great fit for your interest in robust supply chain operations, introducing methods for assessing and enhancing resilience against disruptions. It provides practical insights into strengthening supply chain stability.
This paper is a great fit for your interest in robust supply chain operations, introducing methods for assessing and enhancing resilience against disruptions. It provides practical insights into strengthening supply chain stability.
Abstract
Supply chains increasing globalization and complexity has resulted in recent unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns includes the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the most damaging scenarios. Such approaches are limited by the almost endless number of potential threat scenarios and cannot capture residual risk. In contrast to forward stress testing, this paper develops a reverse stress testing (RST) methodology that allows to probabilistically predict which changes across the supply chain network are most likely to cause a specified level of disruption in a specific country or company. The methodology was applied to the case of copper wire production in the USA, a simple good which may have significant implications for national security. Results show that Canada, Chile and Mexico are predicted to consistently be sources of disruptions at multiple loss levels. Other countries may contribute to overall losses during small disruptions but be less important if catastrophic losses are of concern for decision makers (e.g., Papua New Guinea). Yet some countries may be only important when catastrophic disruptions are considered (e.g., Chili). The probabilistic implementation of RST allows for robust and resilient supply chain design addressing both risk and resilience.
Supply chains increasing globalization and complexity has resulted in recent unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns includes the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the most damaging scenarios. Such approaches are limited by the almost endless number of potential threat scenarios and cannot capture residual risk. In contrast to forward stress testing, this paper develops a reverse stress testing (RST) methodology that allows to probabilistically predict which changes across the supply chain network are most likely to cause a specified level of disruption in a specific country or company. The methodology was applied to the case of copper wire production in the USA, a simple good which may have significant implications for national security. Results show that Canada, Chile and Mexico are predicted to consistently be sources of disruptions at multiple loss levels. Other countries may contribute to overall losses during small disruptions but be less important if catastrophic losses are of concern for decision makers (e.g., Papua New Guinea). Yet some countries may be only important when catastrophic disruptions are considered (e.g., Chili). The probabilistic implementation of RST allows for robust and resilient supply chain design addressing both risk and resilience.
Why we think this paper is great for you:
You will find this paper relevant for its discussion on resource allocation and pricing strategies, particularly in the context of mobility-on-demand systems. It explores fairness and efficiency in managing supply and demand through pricing mechanisms.
You will find this paper relevant for its discussion on resource allocation and pricing strategies, particularly in the context of mobility-on-demand systems. It explores fairness and efficiency in managing supply and demand through pricing mechanisms.
Abstract
Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due to surge pricing strategies. Although several fairness-aware frameworks have been proposed in smart mobility, they often overlook the temporal and situational variability of user urgency that shapes real-world transportation demands. This paper introduces a non-monetary, Karma-based mechanism that models endogenous urgency, allowing user time-sensitivity to evolve in response to system conditions as well as external factors. We develop a theoretical framework maintaining the efficiency and fairness guarantees of classical Karma economies, while accommodating this realistic user behavior modeling. Applied to a simulated mobility-on-demand scenario we show that our framework is able to achieve high levels of system efficiency, guaranteeing at the same time equitable resource allocation for the users.
Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due to surge pricing strategies. Although several fairness-aware frameworks have been proposed in smart mobility, they often overlook the temporal and situational variability of user urgency that shapes real-world transportation demands. This paper introduces a non-monetary, Karma-based mechanism that models endogenous urgency, allowing user time-sensitivity to evolve in response to system conditions as well as external factors. We develop a theoretical framework maintaining the efficiency and fairness guarantees of classical Karma economies, while accommodating this realistic user behavior modeling. Applied to a simulated mobility-on-demand scenario we show that our framework is able to achieve high levels of system efficiency, guaranteeing at the same time equitable resource allocation for the users.
Kamiwaza AI
Why we think this paper is great for you:
This paper is valuable for your broader interest in AI, as it focuses on developing reliable benchmarks for enterprise-relevant agentic AI systems. Understanding robust AI evaluation is crucial for successful AI implementation in any domain.
This paper is valuable for your broader interest in AI, as it focuses on developing reliable benchmarks for enterprise-relevant agentic AI systems. Understanding robust AI evaluation is crucial for successful AI implementation in any domain.
Abstract
Enterprise adoption of agentic AI systems requires reliable evaluation methods that reflect real-world deployment scenarios. Traditional LLM benchmarks suffer from training data contamination and fail to measure agentic capabilities such as multi-step tool use and decision-making under uncertainty. We present the Kamiwaza Agentic Merit Index (KAMI) v0.1, an enterprise-focused benchmark that addresses both contamination resistance and agentic evaluation. Through 170,000 LLM test items processing over 5.5 billion tokens across 35 model configurations, we demonstrate that traditional benchmark rankings poorly predict practical agentic performance. Notably, newer generation models like Llama 4 or Qwen 3 do not always outperform their older generation variants on enterprise-relevant tasks, contradicting traditional benchmark trends. We also present insights on cost-performance tradeoffs, model-specific behavioral patterns, and the impact of reasoning capabilities on token efficiency -- findings critical for enterprises making deployment decisions.
Enterprise adoption of agentic AI systems requires reliable evaluation methods that reflect real-world deployment scenarios. Traditional LLM benchmarks suffer from training data contamination and fail to measure agentic capabilities such as multi-step tool use and decision-making under uncertainty. We present the Kamiwaza Agentic Merit Index (KAMI) v0.1, an enterprise-focused benchmark that addresses both contamination resistance and agentic evaluation. Through 170,000 LLM test items processing over 5.5 billion tokens across 35 model configurations, we demonstrate that traditional benchmark rankings poorly predict practical agentic performance. Notably, newer generation models like Llama 4 or Qwen 3 do not always outperform their older generation variants on enterprise-relevant tasks, contradicting traditional benchmark trends. We also present insights on cost-performance tradeoffs, model-specific behavioral patterns, and the impact of reasoning capabilities on token efficiency -- findings critical for enterprises making deployment decisions.
University of Mississippi
Why we think this paper is great for you:
This paper offers an economic perspective on advanced AI, which could provide a broader contextual understanding for your interests in AI and pricing. It explores the economic implications of powerful AI agents.
This paper offers an economic perspective on advanced AI, which could provide a broader contextual understanding for your interests in AI and pricing. It explores the economic implications of powerful AI agents.
Abstract
Conventional wisdom holds that a misaligned artificial superintelligence (ASI) will destroy humanity. But the problem of constraining a powerful agent is not new. I apply classic economic logic of interjurisdictional competition, all-encompassing interest, and trading on credit to the threat of misaligned ASI. Using a simple model, I show that an acquisitive ASI refrains from full predation under surprisingly weak conditions. When humans can flee to rivals, inter-ASI competition creates a market that tempers predation. When trapped by a monopolist ASI, its "encompassing interest" in humanity's output makes it a rational autocrat rather than a ravager. And when the ASI has no long-term stake, our ability to withhold future output incentivizes it to trade on credit rather than steal. In each extension, humanity's welfare progressively worsens. But each case suggests that catastrophe is not a foregone conclusion. The dismal science, ironically, offers an optimistic take on our superintelligent future.
Conventional wisdom holds that a misaligned artificial superintelligence (ASI) will destroy humanity. But the problem of constraining a powerful agent is not new. I apply classic economic logic of interjurisdictional competition, all-encompassing interest, and trading on credit to the threat of misaligned ASI. Using a simple model, I show that an acquisitive ASI refrains from full predation under surprisingly weak conditions. When humans can flee to rivals, inter-ASI competition creates a market that tempers predation. When trapped by a monopolist ASI, its "encompassing interest" in humanity's output makes it a rational autocrat rather than a ravager. And when the ASI has no long-term stake, our ability to withhold future output incentivizes it to trade on credit rather than steal. In each extension, humanity's welfare progressively worsens. But each case suggests that catastrophe is not a foregone conclusion. The dismal science, ironically, offers an optimistic take on our superintelligent future.
Pricing
Abstract
We consider an agent with a rationally inattentive preference over menus of acts, as in de Oliveira et al (2017). We show that two axioms, Independence of Irrelevant Alternatives and Ignorance Equivalence, are necessary and sufficient for this agent to have a posterior-separable cost satisfying a mild smoothness condition, called joint-directional differentiability. Viewing the decision-maker's problem as a Bayesian persuasion problem, we also show that these axioms are necessary and sufficient for solvability by a unique hyperplane. When the cost function remains invariant for different priors, we show that these axioms imply uniformly posterior separable costs that are differentiable.
We consider an agent with a rationally inattentive preference over menus of acts, as in de Oliveira et al (2017). We show that two axioms, Independence of Irrelevant Alternatives and Ignorance Equivalence, are necessary and sufficient for this agent to have a posterior-separable cost satisfying a mild smoothness condition, called joint-directional differentiability. Viewing the decision-maker's problem as a Bayesian persuasion problem, we also show that these axioms are necessary and sufficient for solvability by a unique hyperplane. When the cost function remains invariant for different priors, we show that these axioms imply uniformly posterior separable costs that are differentiable.
AI for Supply Chain
UPC
Abstract
This paper investigates how individual entrepreneurs can turn creative ideas into successful solo businesses in an era increasingly shaped by Artificial Intelligence (AI) agents. It highlights the key steps that connect personal vision, structured experimentation, and lasting value creation, and shows how AI agents can act as digital co-founders throughout this journey. Building on research in entrepreneurship, creativity, and innovation, we present a framework with three key stages: (1) Imagination shaping, where vague goals become clear value propositions, supported by AI agents that help with market scanning, idea refinement, and rapid concept generation; (2) Reality testing, where these ideas are tested through low-cost experiments, structured feedback loops, and efficient execution, with AI agents automating tasks such as prototyping, content creation, customer interaction, and data analysis; and (3) Reality scaling, where successful ideas are transformed into repeatable processes, scalable market strategies, and long-term business models, increasingly operated and optimized by autonomous or semi-autonomous AI workflows. We focus on the specific context of solopreneurship, characterized by limited human resources, complete accountability for decision-making, and a strong association between the founder's identity and the business. The framework clearly identifies key enabling factors such as mental adaptability, effective planning, and successful human-AI collaboration within digital ecosystems. It also thoughtfully addresses ongoing challenges, like uncertainty and cognitive overload, which are heightened by our constant connectivity.
This paper investigates how individual entrepreneurs can turn creative ideas into successful solo businesses in an era increasingly shaped by Artificial Intelligence (AI) agents. It highlights the key steps that connect personal vision, structured experimentation, and lasting value creation, and shows how AI agents can act as digital co-founders throughout this journey. Building on research in entrepreneurship, creativity, and innovation, we present a framework with three key stages: (1) Imagination shaping, where vague goals become clear value propositions, supported by AI agents that help with market scanning, idea refinement, and rapid concept generation; (2) Reality testing, where these ideas are tested through low-cost experiments, structured feedback loops, and efficient execution, with AI agents automating tasks such as prototyping, content creation, customer interaction, and data analysis; and (3) Reality scaling, where successful ideas are transformed into repeatable processes, scalable market strategies, and long-term business models, increasingly operated and optimized by autonomous or semi-autonomous AI workflows. We focus on the specific context of solopreneurship, characterized by limited human resources, complete accountability for decision-making, and a strong association between the founder's identity and the business. The framework clearly identifies key enabling factors such as mental adaptability, effective planning, and successful human-AI collaboration within digital ecosystems. It also thoughtfully addresses ongoing challenges, like uncertainty and cognitive overload, which are heightened by our constant connectivity.
Demand
Dartmouth College
Abstract
Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.
Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.
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