Willie Lin | Smart Manufacturing, Agentic AI & Decision Intelligence

Publications & Research Pipeline

Peer-reviewed publications and ongoing journal submissions focusing on Agentic AI, smart manufacturing, decision intelligence, SAP/MES/ERP integration, and AI-enabled operations management.

A high-tech smart factory floor rendered in photographic realism, with rows of modular industrial machines connected by glowing, translucent data streams arching above them like subtle light trails. The machines have clean, white and gunmetal surfaces with small indicator lights, and the polished concrete floor reflects soft overhead illumination. In the background, large digital displays show synchronized production schedules, supply chain statuses, and predictive maintenance alerts. Cool, diffused white lighting from linear LEDs overhead creates minimal shadows and a clinical yet efficient feel. Shot from an eye-level perspective using the rule of thirds, the composition guides the eye from foreground equipment to background dashboards, emphasizing a modern, AI-orchestrated manufacturing environment that is orderly, scalable, and future-ready.

Research Publications & Journal Submissions

My research focuses on how Agentic AI and decision intelligence can support smarter, more governed, and executable decisions in complex manufacturing and supply chain environments.

Research Focus:
Agentic AI & Decision Intelligence

Application Domain
Smart Manufacturing & Supply Chain

AI agent-driven process automation for dynamic production efficiency and intelligent equipment integration

Journal of Intelligent Manufacturing, 2025

November 2025
DOI: 10.1007/s10845-025-02706-1

MAS-DME is a decentralized Multi-Agent System framework designed for dynamic smart manufacturing. By combining equipment control and resource allocation agents with reinforcement learning, it supports real-time monitoring, adaptive control, and flexible scheduling. The results show improved efficiency, resource utilization, failure response, and product quality.

An integrated framework featuring policy-governed agentic AI for closed-loop manufacturing control with multi-source sensor–MES–ERP

The International Journal of Advanced Manufacturing Tech, 2026

March 2026
DOI: 10.1007/s00170-026-17806-2

This study proposes PGAI-CLMC, a policy-governed agentic AI framework for closed-loop manufacturing control. By integrating sensor, MES, and ERP data, the framework builds a traceable digital thread and supports real-time anomaly detection, adaptive monitoring, digital-twin validation, and human-in-the-loop review. Results from 25,275 manufacturing records show improved anomaly accuracy, fewer false alarms, stronger robustness, and higher operational efficiency, demonstrating its value for trustworthy and execution-ready smart manufacturing.

An ultra-clean SAP transformation landscape diagram brought to life in photographic realism as if it were a physical installation on a large, matte white table. Raised, acrylic blocks in varying heights represent ERP, MES, and manufacturing execution layers, connected by fine metallic lines embedded in the tabletop. Small, illuminated icons indicate integrations with AI agents, supply chain partners, and analytics platforms. Overhead, soft studio lighting creates gentle shadows and crisp edges, highlighting the precision of each component. The background is an unfocused, light gray office setting with glass partitions, maintaining a professional, enterprise feel. Shot from a low, three-quarter angle with sharp focus on the foreground elements, the scene conveys structured complexity, modernization, and deliberate design in SAP-enabled digital transformation.
A nighttime exterior view of a modern manufacturing campus, presented in photographic realism, with clean-lined buildings accented by continuous glass façades and subtle architectural lighting. Above the plant, semi-transparent, glowing circuit-like patterns and iconography float in the sky, symbolizing pervasive agentic AI, IoT connectivity, and decision intelligence guiding operations. The parking areas and access roads are sparsely lit, with reflections shimmering on wet asphalt as if after a light rain. Cool white and soft blue illumination from windows and pathway lights creates a composed, high-tech atmosphere. Captured from a slightly low, wide-angle perspective, the image balances sharp detail in the foreground with a gradual softening toward the horizon, suggesting scale, integration, and a confident, future-oriented manufacturing ecosystem.
An abstract yet photographic visualization of agentic AI for supply chain coordination, depicted as a network of translucent, glass-like cubes hovering above a stylized map of interconnected factories and warehouses. Each cube emits a soft inner glow in shades of blue and emerald, linked by thin, luminous pathways that curve gracefully across the scene. The map beneath is minimal, with subtle topographic lines and faint location markers on a dark slate surface. Overhead spotlights create crisp reflections and highlights on the glass surfaces, while the edges cast delicate, geometric shadows. Shot from a high, three-quarter angle, the composition feels dynamic and precise, conveying autonomy, coordination, and intelligence with a clean, modern aesthetic ideal for a professional portfolio on agentic AI.