SOLUTION BRIEF

TABLE OF CONTENTS
Executive Summary ........................................ 1
Challenges of AI Application Deployment inManufacturing ................................................. 2
Supermicro & MicroAI Solution ....................... 2
Next Generation Factory Management &Process Optimization ...................................... 4......................................................................... 6
Conclusion ....................................................... 6
Further Information ........................................ 7© 2023 Copyright Super Micro Computer, Inc. All rights reserved

Executive Summary
Traditionally, manufacturing maintenance strategies have relied on two
main approaches: scheduled and reactive maintenance. For several
decades these approaches were sufficient to maintain average levels of
overall equipment efficiency (OEE); however, this is no longer the case, as
costly limitations often accompany these approaches. On one end,
equipment would either be over or under-maintained when relying onscheduled maintenance. While on the other end, reactive maintenancesignificantly increases the chance of unplanned downtime and safety risks.
Both scenarios result in the inefficient use of resources while negatively
impacting overall operational efficiency. To overcome these costly
limitations, the industry has started shifting towards a predictive
maintenance approach, which leverages artificial intelligence (AI) andmachine learning (ML) algorithms to optimize OEE. The goal is to utilize
real-time data and performance indicators to proactively identify potentialAugust, 2023

1

equipment failures and prioritize maintenance activities to optimize resource allocation. Although AI and ML-based predictive
maintenance technologies show great promise, it is not always easy to implement as they require certain prerequisites.

Challenges of AI Application Deployment in Manufacturing
Deploying AI-based predictive maintenance in this industry presents specific challenges that must be overcome to ensure
successful implementation. Some of these include:•Limited Historical Data: AI models typically rely on substantial amounts of historical data for training. Usually, asubject matter expert and data scientist would be needed to analyze and develop an algorithm to train the AI model
based on available historical data.

•Data Complexity & Interpretability: In some cases, critical machine information may not have been captured in thepast due to equipment or process changes, making it difficult to establish reliable patterns for accurate predictions.

•Real-time Processing & Latency: Significant computational power and time are likely required, which can introduce
latency that can impact other performances.

•Complex System Integration: Manufacturing operations have a complex ecosystem of interconnected systems. This
will require careful planning and implementation to ensure compatibility and seamless data exchanges between new
applications and existing infrastructure.

•Appropriate Edge Hardware Requirements: Edge devices with sufficient processing power to execute AI workloads
while being robust in design to withstand rugged environments.

Although these challenges prove to be burdensome, AI based predictive maintenance can provide many benefits inmanufacturing.

Supermicro & MicroAI Solution
Realizing these challenges, Supermicro has partnered with MicroAI to develop a turnkey plug & play AI and ML factory
platform. This Digital Factory combines Supermicro’s ultra-reliable Intelligent Edge Systems with MicroAI’s innovativetechnology to enable an AI based predictive solution that can be trained within a few days without needing preexisting
historical data or data scientists.
Features:
Machine-Centric Intelligence
Embedding and training AI and ML algorithms directly onto the MCU or MPU of the manufacturing device or machine.
Customizes AI algorithms based on the asset's specific operational and/or environmental parameters to provide breakthroughpredictive insights into maintenance requirements.
Self-Learning
AI and ML algorithms learn the standard behavior patterns of the asset under various operational conditions. This selflearning occurs without human intervention requirements and provides performance baselines based on actual performancevs. static specifications.
Predictive Analytics
Real-time, asset-specific performance data is automatically collected and analyzed to produce predictive maintenanceintelligence based on current and projected asset health and performance. Maintenance is performed when needed
eliminating unnecessary machine downtimes and reducing maintenance costs.

© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 2023

2

Catastrophic Failure Prevention
Local, real-time, predictive analytics that detect and alert the possibility of device or machine failure. Intelligent, automatedworkflows provide rapid alerts to ensure prompt response and fault mitigation to extend asset lifespans and improve factory
floor safety.
Robust and Reliable Hardware
Built to withstand harsh environments while still delivering high compute performance capabilities. Fanless design offers
reduced energy consumption and ultra reliability.
Deployment Architecture

The deployment architecture consists of multiple components working together to ensure seamless data acquisition, analysis,
and actionable insights.
Overview:
1. Supermicro Edge Device: At the heart of the architecture, these edge devices are deployed throughout the manufacturing
facility to capture sensor data from various machinery and equipment to perform real-time data processing, AI model
inference, and local analytics.
2. Data Acquisition and Integration: This layer ensures seamless integration of all sensors with the edge devices. It utilizes
communication protocols such as Modbus, OPC-UA, or MQTT to capture data from these sources. The acquired data is thenpreprocessed, filtered, and integrated to ensure compatibility and consistency.
3. Analytics Engine: Responsible for processing and analyzing sensor data collected by edge devices. It includes the AI models
specifically trained for factory use cases, such as anomaly detection, fault prediction, and performance optimization. These© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 2023

3

models are compact and optimized for edge computing, allowing real-time analysis directly on edge devices without relying
heavily on cloud connectivity.
4. Central Management System: Allows administrators to monitor the health and performance of the edge devices, track
analytics results, and configure the system settings in a user-friendly dashboard.
5. Connectivity & Communication: This includes wired and wireless connectivity options or industrial protocols like MQTT orOPC-UA.
Supermicro SYS-E100-12T Series Specification:

Next Generation Factory Management & Process Optimization

Features:© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 20234

Asset Monitoring & Self-Reporting: Enables real-time monitoring capabilities through collecting and analyzing sensor data.
It continuously tracks asset conditions such as temperature, vibration, pressure, and other performance metrics to identify
anomalies or deviations from normal operating parameters.
OEE Analysis: Provides the ability to develop real-time AI/ML based insight into OEE performance of any manufacturing lineand shift. The ability to view which equipment is performing below the expected rate immediately allows operations to makeinformed decisions.
Cycle Time Analysis: This can predict the number of jobs that will be completed throughout a cycle. Displays a full line viewthat shows the occurrence of starved or blocked conditions and predicts the root cause of these conditions. This will allow
management to see which assets or operators are underperforming.
Performance Monitoring: AI/ML based Machine Health Scores are generated for each asset by ingesting control systems andsensor data. It’s configured to optimize the days to the next maintenance based on the current performance and health of themachine. It will also generate alarms based on the events happening to the device or environment.
Causality Analysis & Feedback Loop: Identifies root causes of problems or inefficiencies and establishes a feedback loop byproviding insights into the causal relationships between variables. It will then facilitate the integration of these actions into
the operational processes to continuously monitor and adjust performance.

Key Benefits:

Increased Equipment
UptimeCost Savings

Better Privacy & Security

Easy Integration

Improved Operational
Efficiency

Efficient Resource
Utilization

Enhanced Equipment
Reliability

Provides Real-timeAnalytics

Density Package Selection© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 2023

5

Supermicro Servers
Small:SYS-E50-9AP

Medium:
SYS-E100-12T-E

Large:SYS-E302-12D-8C

SYS-110P-FRN2T

Conclusion
AI driven predictive solutions have provided clear advantages for manufacturers, especially those that do not rely on the needfor historical data. Supermicro and MicroAI’s partnership creates a robust and comprehensive predictive maintenancesolution combining AI, Edge Computing, and robust hardware. This powerful combination enables real-time anomaly
detection, proactive maintenance, scalability, and security, ultimately optimizing OEE, reducing cost, and ensuring
uninterrupted production in industrial manufacturing environments. By implementing Supermicro & MicroAI Digital Factory
Platform, manufacturers unlock their manufacturing processes' full potential while maintaining their advantage in thedynamic manufacturing landscape.

© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 2023

6

Further Information
Supermicro 5G, Edge, IoT, & Embedded Brochure 2023
Supermicro Fanless Edge Systems
Supermicro Compact Edge Systems
Supermicro 1U Edge Systems
MicroAI Predictive Manufacturing
MicroAI Digital Factory
MicroAI Digital Intelligent Manufacturing WhitePaper

MICRO.AI

SUPERMICRO

MicroAI™ is the pioneer in edge-native artificial intelligence

As a global leader in high performance, high efficiency server
technology and innovation, we develop and provide end-toend green computing solutions to the data center, cloudcomputing, enterprise IT, big data, HPC, and embeddedmarkets. Our Building Block Solutions® approach allows us toprovide a broad range of SKUs, and enables us to build anddeliver application-optimized solutions based upon yourrequirements.

(AI) and machine learning (ML). The company’s mission is toautomate the management of machines, enable them toself-monitor and self-report, and provide the most accurate
information on machine health. The company’s software isdeployed within edge appliances aggregating data frommultiple machines, as well as embedded on microcontrollers(MCUs) and microprocessors (MPUs) within an individualmachine themselves. MicroAI’s software is used byVisit www.supermicro.com

manufacturing and industrial processing companies, original
equipment manufacturers, and asset owners to predictfailures, identify security issues, and improve life-cycle
management and overall equipment effectiveness (OEE). For
more information visit www.micro.ai.

© 2023 Copyright Super Micro Computer, Inc. All rights reserved

August, 20237