Robots in E-Comm Logistics In e-commerce logistics, especially warehousing, robots, robotic arms, and AMRs play a promising role across the entire workflow, from unloading and storage to picking, packing, sorting, outbound, and even returns. With advances in AI and machine learning, robots are now being deployed in areas beyond imagination. Robots with machine vision can unload parcels from trucks. Vision-guided robotic arms with suction grippers or mechanical claws can pick parcels and polybags from an inbound conveyor, and sort them by size and shape, then place them into respective totes and bins. AMRs can carry parcels or containers between sorting zones, conveyors, or loading docks. Instead of relying on fixed conveyors, AMRs create modular routing, flexibly scaling with demand.   Robots are not just machines replacing humans, but now collaborative systems that extend human capacity, reduce labor strain, and enable warehouses to handle massive order volumes with speed, accuracy, and flexibility. With global e-commerce robot deployments projected to exceed 5 million units by 2030, even a single minute of downtime can cost a warehouse significant lost throughput. That’s why cybersecurity can no longer be an afterthought — it must be built into the robot’s brain itself.   With Greater Power in Robots Comes Greater Risk Despite their efficiency, robot-based warehouse automation systems face enormous risks once their networks are compromised. With hundreds of robots and AMRs working in sync, a single breach in the robotic control system can cascade through the entire warehouse, halting sorting lines, misrouting shipments, and creating chaos in scheduling and inventory tracking. The financial impact is immediate: delayed orders, lost revenue, and damaged brand reputation. In a high-throughput e-commerce warehouse, every minute of disruption translates directly into lost cash flow — not to mention the costly repairs and replacements of damaged assets.   The challenges: Robots being hijacked and authorized access being denied Robots being remotely controlled by hackers Hijacked robots spreading malware to other assets   Why Traditional IT/OT Security Tools Fail What makes it worse is that traditional IT and OT security tools often fall short in AI robotics environments. IT security tools such as firewalls, antivirus software, and IAM (Identity Access Management) systems, are based on authentication and authorization, not optimized for cyber-physical assets. In the context of robotics, where systems control motors, sensors, and actuators in real time, an IT-centric tool can’t handle the need for millisecond-level responsiveness or provide safe fallback mechanisms if access is denied.   Classic OT security tools for ICS/SCADA are designed for isolated networks where devices weren’t expected to authenticate and authorize users frequently. However, robots in modern warehouses are mobile, dynamic, and connected everywhere, requiring continuous protection when moving from one place to another, which traditional perimeter-focused OT security tools can’t provide.   Safeguard Robots from the Core NEXCOM eSAF Guardian, powered by NVIDIA® Jetson Orin™ / Jetson Thor™, provides embedded cybersecurity directly inside the "brain" of robot — its controller. It introduces multi-layered defense with: Realtime OT CybersecurityDetects and mitigates cyber threats instantly, ensuring minimal impact on industrial processes. Network MonitorAnalyzes network traffic for anomalies, policy violations, and intrusion attempts via deep packet analysis. System Call MonitorTracks low-level system interactions to detect abnormal or unauthorized activities that may indicate malware or system compromise. File Access MonitorMonitors file usage and access patterns to prevent unauthorized modifications, data theft, or tampering. IEC 62443 ComplianceEnsures global OT cybersecurity standards are met from the very beginning of the product development phase.   Blocking threats from the outside helps, but protecting from within goes further, which enables more accurate detection of unidentified malware through its real actions, not just signatures.     AI in Action Utilizing OT time-series data, the LSTM (Long Short-Term Memory) model is a powerful tool for ML-based prediction, well-suited for scenarios with recurring behavioral patterns. For example, the model can be used to predict the number of device connections of sensors, scanners, or gateways every ten minutes, which helps detect operational deviations and potential cybersecurity incidents. In simple terms, the robot learns its own ‘normal’ behavior — and flags anything that looks off, before it causes downtime.   Through the NEXCOM eSAF Platform Manager, users can train models on at least one week of data for each device and define expected operational threshold ranges. When actual behavior deviates from the predicted range, alerts are automatically triggered. This approach enables intelligent anomaly detection and alerting without relying on traditional rule-based systems.   Benefits By integrating NEXCOM eSAF Gaurdian, robot manufacturers and system integrators gain: Comprehensive Asset Protection Coverage: Extends to Robots and AMRsBeyond common field assets, every embedded node now gains proactive defense. eSAF Guardian protects various robot configurations including humanoid, quadruped, and wheeled types from hijacking, covering the whole warehousing scenarios. Faster Incident Response: Days to HoursWith integrated monitoring of system calls, file access, and network traffic, anomalies are flagged instantly, reducing incident investigation time from days to hours. Reduced MTTR: Hours to MinutesReal-time detection of hijacking attempts and abnormal behaviors prevents unplanned production stoppages, cutting mean time to recovery (MTTR) from hours to minutes. Regulatory Compliance: IEC 62443Aligns with global industry standards at the component level, facilitating robot development and enabling system integrators to capture untapped market opportunities faster. Future-Proof Security with AIAnalyzes network traffic and device behavior to identify deviations from normal operating patterns, continuously evolving with new attack techniques.   As the world’s warehouses evolve into autonomous ecosystems, NEXCOM’s eSAF Guardian turns every robot into a self-defending asset, securing operations, protecting data, and ensuring no robot ever becomes a hacker’s target again.   NEXCOM can help you build safer, smarter robots — from the core to the cloud.

Background The chemical manufacturing sector is a cornerstone of the global economy. According to the International Council of Chemical Associations (ICCA), it contributes $5.7 trillion annually — approximately 7% of global GDP — and supports over 120 million jobs. As the fifth-largest manufacturing sector, it generates $1.1 trillion in direct output each year.   In Taiwan, the chemical industry holds similarly critical importance, ranking third in manufacturing output in 2022, behind only electronics and metal machinery. The sector — from petroleum refining to synthetic fibers — relies on continuous production lines where long, uninterrupted equipment runs are essential and process stability is crucial. Even minor deviations in key parameters can compromise product quality and pose safety risks. While historically reliable, traditional quality monitoring methods such as routine inspections, SPC charts, and operator experience are becoming less effective as product complexity increases and process variables multiply.   NEXCOM and Profet AI have surveyed the market and consulted with numerous companies across the pan-chemical industry to identify the lingering pain points on production lines. Discover how their joint solution tackles the challenge by bringing AI into physical world.   Where the Gaps Were When deviations in product quality occurred, such as shifts in purity or physical properties, teams often spent days searching for the root cause. This typically involved sifting through equipment logs, cross-checking historical parameters, and running trial batches to isolate the issue. But in an environment where time-to-correction matters, this kind of approach fell short:   Highly interdependent parameters made it difficult to pinpoint the root cause of quality issues Trial-and-error approaches prolonged process adjustment cycles Heavy reliance on tacit knowledge slowed response and hindered reproducibility Lack of reliable edge processing platform that provides both AI computing capabilities and legacy device compatibility   Physical AI: From Hardware, Software, to Physical World NEXCOM's industrial PC, combined with Profet AI’s AutoML platform, empowers process engineers to locate the root causes of quality fluctuations by AI training and inference.   NEXCOM TT 300-A3Q is a compact and high-performance system designed for factory automation and AI model training. Its PCIe x16 expansion slot can accommodate high-end GPU, providing the essential computing power for AI training and other advanced applications. The 4 x COM ports and 2 x GbE LAN ports offer ultimate connectivity to field devices, and the 2 x HDMI® & 2 x DP ports are perfect for quad 4K HDR displays, providing the elastic configurations of display matrix for intelligent operation center.   Via NEXCOM TT 300-A3Q, the AutoML platform receives process data from the production line, including temperature, pressure, liquid level, and flow rate. With the platform's no-code interface, engineers easily build predictive models on historical datasets, linking process variables to quality outcome. Key contributing factors are presented through inference by the models, allowing engineers to focus on the parameters that mattered most. Engineers can also explore how different operating conditions influence quality predictions using the models, which provides early visibility into potential outcomes, proactively guiding parameter adjustments even before the results are available.     Reframing the Approach: What You'll Discover A clearer picture of which variables are driving product variation Faster diagnosis of abnormal conditions: Trial-and-error cycles reduced by 57%~61% Precise guidance for parameter adjustments: Validation time drops from 3-5 days to less than 1 day Product stability improves by 28%   Importantly, none of these gains requires reorganizing teams or building an enormous analytics team. The breakthrough comes from equipping the people closest to the work with the right tools.   More Than a Fix, a Repeatable Process What once relied solely on experience becomes something visual, traceable, and reusable. Each model built is automatically documented — its structure, input, output, key variables, and how predictions perform is all well recorded. This transformation turns individual process knowledge into shared organizational intelligence.   It is not only a one-time success; it marked the beginning of a repeatable improvement system, where lessons learned on the field can be reproduced, scaled, and shared across teams and production lines anywhere.   About NEXCOM: https://www.nexcom.com/index.html

Taiwan's Semiconductor Edge Creates Unprecedented Pressure Taiwan’s leadership in semiconductors is the result of decades of investment and deep technical expertise. However, as the industry advances toward smaller nodes, complexity increases significantly. Parameter interactions become more sensitive, product launch cycles accelerate, and process windows narrow. The traditional reliance on experience-based optimization can no longer keep up with these demands.   When you factor in high measurement costs and delayed feedback from metrology tools, the pressure on frontline teams becomes immense. The urgency to improve is constant, yet improvement cycles remain slow and cumbersome. While some companies respond by expanding centralized data teams or investing in massive data lakes, these top-down solutions often overlook the most critical resource: the process knowledge of engineers.   See how NEXCOM and Profet AI's consulting services delivered a solution to empower process engineers, further expanding their expertise with AutoML software and AI computing systems.   Challenge In semiconductor manufacturing, the gap between identifying a problem and resolving it is often measured in weeks, not hours. The crucial process—virtual thickness measurement for CVD (chemical vapor deposition)—may not be the most glamorous application of AI, but it highlights a fundamental truth about the current state of smart manufacturing.   For years, the conversation around AI in factories has centered on technological feasibility. However, in practice, the real bottleneck isn’t the technology; it’s access. The process engineers who understand the problems best—like the subtle interactions between pressure, flow, and temperature—are often the least equipped to build the data models needed to solve them.   We witnessed this firsthand at a major semiconductor company. Despite having a dedicated team of data scientists, most process improvement ideas from the engineering team never made it to the top of an already long priority list. Engineers were eager to solve these problems themselves, but the tools were too fragmented, and the modeling process felt inaccessible. This isn't a data problem; it's a usability problem.   Virtual Metrology for CVD In this context, the company introduced Profet AI’s AutoML platform with a simple goal: to let process engineers build and deploy their own predictive models without waiting in line for data scientists.   CVD is a precision-heavy and time-consuming step where film thickness and uniformity are paramount. The traditional workflow involves running trial batches, relying on senior engineers to tune parameters, and waiting for offline inspections to validate results. This feedback loop is slow, and by the time a deviation is found, corrective actions often come too late.   Using the platform, engineers uploaded their historical process data including pressure, gas flow, temperature, timing, and more as the dataset. Within a few hours, they had built and validated models capable of accurately predicting film thickness and uniformity.   Integrated AI Computing System Profet AI’s AutoML platform is installed in the NEXCOM's TT 300-A3Q, a powerful industrial PC with a PCIe x16 expansion slot for advanced GPU support. Designed to support AI model training and inference, the system is powered by 12th/13th Gen Intel® Core™ processors and accelerates workloads with Intel® OpenVINO and Intel® Deep Learning Boost, making it a perfect companion for the AutoML software.   Comparing to other high-performance products, the NEXCOM TT 300-A3Q's compact size could seamlessly integrate into the limited space, demonstrating its remarkable performance even in the challenging conditions of high temperatures and humidity. The device operates efficiently within a wide temperature spectrum of -5°C to 55°C and a humidity range of 10% to 95%. With all I/Os at front, it is designed for easy maintenance and installation.   The system also supports AI deployments across the factory ecosystem, from edge DAQ (Data Acquisition) to MoM (Manufacturing Operations Management) system, and ultimately integrates with enterprise systems like ERP and MES. AI analyzes incoming operational data from field equipment/PLCs and powers insights in a centralized “Enterprise War Room,” enabling real-time monitoring and KPI-driven decision-making across many AI-enabled modules such as production line monitoring, energy management, and cybersecurity.   In short, NEXCOM TT 300-A3Q transforms traditional factory operations into AI-powered smart manufacturing hubs, enhancing automation, responsiveness, and operational intelligence.   The Numbers: Less Waiting, More Engineering The impact wasn’t in adding more AI—it was in eliminating the wait. The results shifted the team’s entire workflow:   Pre-production parameter setup, which used to take 4 hours, was cut in half to 2. Quality predictions became available in near real-time, eliminating the delay from physical inspections. Physical measurement costs plummeted by an estimated 70%. Most importantly, the overall process yield improved by approximately 2%.   The change wasn’t just about gaining access to a new tool—it was about empowering engineers to act directly on their own process expertise.   From a One-Off Project to a Repeatable System This approach also addresses a common failure point for AI projects: successful models that remain "black boxes" and cannot be scaled because they weren't properly documented.   With the AutoML platform, every step from data selection to model tuning and performance validation, is automatically logged in the platform. This creates a structured, transparent record that allowed future teams to revisit, reuse, and build upon prior work. What starts as a one-off success could now become a repeatable and scalable process.   The True Shift: Empowering Engineers, Not Replacing Them The core challenge in smart manufacturing has never been a lack of data or a shortage of problems to solve. It has always been a bottleneck of usability.   When process engineers are empowered to test their own ideas and build models directly, companies can finally unlock the hidden value in the vast data they already collect. Profet AI's platform and NEXCOM's IPC didn't just provide a tool; it introduced a brand new, more intuitive way of working. When those closest to the problem are empowered to find the solution, AI moves beyond a buzzword on a presentation slide and becomes a practical part of the daily toolkit.   The future of industrial AI isn’t about making engineers into data scientists—it’s about making data science accessible to engineers.   About NEXCOM: https://www.nexcom.com/index.html