Skip to content
Tiatra, LLCTiatra, LLC
Tiatra, LLC
Information Technology Solutions for Washington, DC Government Agencies
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact
 
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact

Leveraging Microsoft AI: A game changer for manufacturing

The manufacturing edge is the hardware and software constellation on the plant’s premises. It encompasses the full range of end point devices and often a localized data center for storing data and running analytics, monitoring, and other applications.

But organizations are implementing edge strategies that do not consider AI and GenAI requirements. The results are inefficient utilization of edge resources, needlessly complex machine learning models, and impractical use cases, all of which lead to slow or suboptimal adoption by end users.

To deploy and optimize AI on the edge, manufacturing IT leaders should:

  • Segment the plant/assets: Grouping the plant/assets appropriately ensures balanced edge architecture.
  • Design with the end in mind: Taking a long term view of costly edge hardware investments helps keep costs optimal.
  • Miniaturize AI models: Reducing model size reduces the computing resources needed to train and run it, speeds the model’s operations, and enables AI to be more widely distributed in the organization.
  • Optimize network load management: Processing data on the edge minimizes the volume of real-time transiting over the network and reduces latency. 

At the edge, AI can work locally with local data generated from a plant’s operational technology layer, including PLCs, controllers, industrial PCs, IoT sensors, cameras, RFID tags, and more. With latency minimized, locally deployed AI enables autonomous operations (not just automated operations) and real-time responsiveness.

Evaluate a “mesh-of-edges” or edge mesh approach

The above considerations suggest an approach that can be called a “mesh-of-edges.” This approach hosts optimally-sized ML/AI models on optimal compute resources. It provides the ability to scale the architecture for future requirements and it keeps costs in check.

As a result, edge AI can be leveraged effectively for tasks such as real time production monitoring, inventory management, real-time machine fault prediction, process optimization, quality control automation, production line diagnostics, and much more.

A representative mesh-of-edges is shown in the diagram below. Each edge hosts the ability to compute low- to medium-scale machine learning calculations.

Tata Consultancy Services

Prioritize the art of miniaturizing AI models

Miniaturizing AI/ML models greatly helps in reducing the size and cost of the compute resources needed on the edge. There are various techniques for optimally sizing and then miniaturizing these models. Also, there are different ways to host the models on the edge, optimizing the needed storage and compute capacities of the edge device. An intelligently designed pipeline of processing models ensures optimal compute usage without keeping the edge busy at 100% of its capacity.

Make the business case for AI-on-edge

Designing and optimizing the edge for your organization’s AI development has concrete business as well as operational benefits:

  • Edge computing supports instant, AI-powered data analysis to make real-time decisions in response to changes taking place within the manufacturing plant.
  • Edge computing increases the reliability of critical AI applications, because they are no longer dependent on cloud processing or connectivity. This capability is part of a strategy of continuous operation.
  • AI at the edge can leverage compute and storage as needed for training models; and through low-code initiatives, create light-weight AI applications that require less resources.

Manufacturing IT leaders can act now to configure and optimize their edge computing infrastructure to enable effective AI deployments using Microsoft AI. Here are five steps to consider:

  1. Identify key use cases: Assess which AI applications will yield the maximum business when deployed in edge computing.
  2. Implement edge infrastructure: Map out what’s required in terms of resources and expertise to set up a scalable edge infrastructure for AI and integrate with existing systems.
  3. Enhance security: Develop a comprehensive edge security strategy that explicitly addresses AI-specific security issues.
  4. Train and upskill teams: Prioritize the training needed for internal IT and operational teams to manage edge computing infrastructure supporting AI applications.
  5. Pilot projects before scaling: Initiate pilot projects to test and refine edge computing plans before large-scale deployment.

The bottom line

IT leaders can speed up AI deployment at the edge by partnering with a systems integrator like Tata Consultancy Services (TSC), which has expertise, platforms, and services for edge computing and AI in partnership with Microsoft. By partnering with TCS, IT leaders can effectively harness edge computing and AI to optimize their operations, improve efficiency, and ensure the reliability of their AI applications.

To learn more about how TCS can help manufacturing IT leaders optimize the edge for AI, see  Next generation manufacturing enterprise: powered by GenAI.


Read More from This Article: Leveraging Microsoft AI: A game changer for manufacturing
Source: News

Category: NewsJune 21, 2024
Tags: art

Post navigation

PreviousPrevious post:Modernize manufacturing IT to maximize business value on Microsoft CloudNextNext post:Task-specific GenAI copilots boost shopfloor productivity with Microsoft AI

Related posts

Barb Wixom and MIT CISR on managing data like a product
May 30, 2025
Avery Dennison takes culture-first approach to AI transformation
May 30, 2025
The agentic AI assist Stanford University cancer care staff needed
May 30, 2025
Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
May 30, 2025
“AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
May 30, 2025
“ROI는 어디에?” AI 도입을 재고하게 만드는 실패 사례
May 30, 2025
Recent Posts
  • Barb Wixom and MIT CISR on managing data like a product
  • Avery Dennison takes culture-first approach to AI transformation
  • The agentic AI assist Stanford University cancer care staff needed
  • Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
  • “AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
Recent Comments
    Archives
    • May 2025
    • April 2025
    • March 2025
    • February 2025
    • January 2025
    • December 2024
    • November 2024
    • October 2024
    • September 2024
    • August 2024
    • July 2024
    • June 2024
    • May 2024
    • April 2024
    • March 2024
    • February 2024
    • January 2024
    • December 2023
    • November 2023
    • October 2023
    • September 2023
    • August 2023
    • July 2023
    • June 2023
    • May 2023
    • April 2023
    • March 2023
    • February 2023
    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    Categories
    • News
    Meta
    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org
    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

    Find us on:

    FacebookTwitterLinkedin

    Submitclear

    Tiatra, LLC
    Copyright 2016. All rights reserved.