Industrial LLMs: Business of Manufacturing Technology
The Exponential Industry GPT is tailored to the business of manufacturing technology with curated knowledge of manufacturing terminology, business understanding, and technology tradeoffs.
Last week we covered Industrial LLMs: Copilots for Asset Performance with a promise of showcasing the LLMs embedded into industrial automation products this week. However, with the imminent arrival of the GPT Store, I am excited to bring you an update to the Exponential Industry GPT!
Exponential Industry seeks high quality content on the breakthroughs and innovations in manufacturing technology. EI also values crediting (and driving traffic to) the work of journalists and companies creating this content. So when it comes to building a GPT for the community, here is the corpus powering the GPT thus far.
Imbuing Manufacturing Technology Knowledge into a GPT
The first step in building a GPT is providing a specific context and persona. The initial GPT builder process, starts with specifying what to make and the conversational tone.
After initial setup, additional instructions, conversation starters, and knowledge can be added to further tailor the GPT.
Inside the “Exponential Industry GPT - Corpus” document you’ll find a number of articles from leading manufacturing technology firms. Keen readers will note that these articles largely differ from the typical content curated in the weekly digest. This is because the initial focus is to teach the GPT about manufacturing specific terminology, technology, and business fundamentals. Currently the document format closely mimics the source posts and is mostly human readable, but I may make changes as new LLM innovations such as DocLLM or tricks like context stuffing begin to matter.
Moving forward, I will add more content with opinionated viewpoints about emerging technology, product offerings, and the value they bring. This content will be similar to the content I include in the weekly digest. The starter corpus content from Cognite and Instrumental are good representative examples. Reply to my email with your favorite content, so that your viewpoint and product can be recalled by the GPT!
Before and After
The difference in usability between using standard ChatGPT 4 and the Exponential Industry GPT is noticeable. Take a look at two examples (and note the product placement opportunity):
Me: Tips for reducing Operational Costs?
ChatGPT 4:
Reducing operational costs is a key strategy for improving the profitability and sustainability of a business. Here are some tips to help you achieve this:
Streamline Processes: Evaluate your current processes and identify areas where you can increase efficiency. Implementing lean management techniques can help reduce waste and optimize productivity.
Adopt Technology: Invest in technology that automates repetitive tasks. This can include software for accounting, customer relationship management (CRM), or inventory management. Automation not only reduces labor costs but also minimizes errors.
Outsource Non-Core Activities: Outsourcing tasks that are not central to your business, such as administrative duties, cleaning, or IT services, can be more cost-effective than maintaining in-house teams.
…
Monitor and Reduce Travel Expenses: Use video conferencing tools to reduce the need for travel. If travel is necessary, look for cost-effective options and plan well in advance.
Regular Financial Audits: Regularly review your financial statements to identify areas where you might be overspending. This includes reviewing contracts, insurance policies, and any recurring subscriptions or services.
Remember, the goal is to cut costs without sacrificing the quality of your products or services. Sometimes, short-term savings can lead to long-term losses, so always consider the broader impact of your cost-cutting measures.
Me: Tell me about domain data models
ChatGPT 4:
A domain data model is a conceptual representation of the structured data within a specific domain or area of interest, often within a business or organizational context. It's an essential part of software development, database design, and systems analysis, as it provides a clear framework for understanding and manipulating the data relevant to a particular domain. Here's a breakdown of key aspects of domain data models:
Definition and Purpose:
Definition: A domain data model represents and organizes the various elements of data in a specific domain, detailing how these elements are interconnected.
Purpose: The primary purpose is to provide a structured and logical view of the data, which helps in designing databases, software applications, and systems that are efficient, relevant, and scalable.
Components:
Entities: These are the primary objects or concepts within the domain. For example, in a retail domain, entities might include 'Customer', 'Order', 'Product', etc.
…
Now with the Exponential Industry GPT that has tailored manufacturing knowledge.
Me: Tips for reducing operational costs?
Exponential Industry GPT:
To reduce operational costs in a manufacturing or heavy industry setting, consider the following strategies:
Minimize Downtime: Using Overall Equipment Effectiveness (OEE) software can significantly improve effectiveness by reducing downtime. Key aspects include decreasing overtime work, reducing the number of shifts to meet production targets, swiftly adapting to changes in demand and production schedules, and shortening delivery timelines.
Strategic Maintenance Planning: Incorporate a warm-up phase in the production cycle to minimize maintenance expenses, reduce defective products, and enhance machine performance. Smart manufacturing systems can rapidly detect and anticipate failures, leading to reduced maintenance costs and minimized machine downtime.
Focus on Return on Investment (ROI): Understanding ROI helps in assessing potential opportunities and making financial decisions. Strategies include reducing production time per item, minimizing waste, and optimizing the use of automation.
Improve OEE: Improving your OEE can have a direct impact on profitability. For instance, increasing OEE from 64% to 74% can significantly raise profits. It also aids in more predictable planning of production processes, including material and manpower needs, transport, and regular maintenance.
Manage Downtime Costs: Factor in lost revenue from unproduced products, increased labor costs due to downtime, and repair and replacement costs. Accurate calculation and management of downtime are essential for production efficiency.
Leverage Automation: Employ technology or machinery for tasks without human intervention to increase efficiency, productivity, and accuracy, while reducing labor costs and human error. Automation can range from simple rule-based tasks to advanced AI systems.
Implementing these strategies requires a holistic view of your operations and a commitment to continuous improvement and innovation.
Me: Tell me about domain data models
Exponential Industry GPT:
Domain Data Models (DDMs) are a fundamental concept in data management and digital transformation, especially in the industrial sector. They provide structured frameworks for organizing and interpreting vast amounts of data in a way that is relevant and meaningful to specific industries or domains. Let's explore the key aspects of Domain Data Models:
Industry-Specific Best Practices: Domain Data Models in systems like Cognite Data Fusion® offer predefined, configurable models that represent best practices for specific industries. For example, an oil & gas company might use the asset hierarchy and the CFIHOS standard, while a manufacturing firm might follow ISA:95, and a power and utilities company might use CIM. These models cater to the specific needs and standards of each sector.
…
Checkout the Exponential Industry GPT in the GPT Store
As GPTs get more web browsing, podcast listening, and video watching capabilities I hope to link to them just as the weekly digest does. Until then, I will continue to share the corpus powering the Exponential Industry GPT and develop more capabilities for discovering leading manufacturing technology firms and processes.