Revolutionizing Parts Replacement in Discrete Manufacturing with Generative AI

In the fast-paced world of discrete manufacturing, efficiently replacing machine parts is crucial to maintaining productivity and minimizing downtime. This is a function of identifying the faulty parts and quick, accurate search for the right parts. 

Traditional methods of identifying parts for replacement are often reactive, relying on manual monitoring, historical data, and scheduled maintenance. However, these approaches can lead to unexpected breakdowns, costly delays, and inefficiencies. 

Even the traditional methods of part search often involve manual processes, relying on databases, catalogs, and human expertise. However, these methods can be time-consuming and prone to errors, leading to delays in production and increased operational costs. 

Generative AI is poised to revolutionize how manufacturers manage parts replacement, offering intelligent, proactive solutions that transform this critical aspect of operations.

Understanding the Challenges 

Manufacturers face several challenges when it comes to parts replacement.

Manufactured parts come in an immense variety of shapes, sizes, and specifications, often with subtle differences that can be critical to their function. Traditional part search methods typically involve searching large databases, consulting with experts, or referencing extensive catalogs. This slow process leaves room for human error, especially when dealing with complex assemblies or rare parts.

Finding the exact part required for a specific application can take hours, if not days, especially if the part is obscure or discontinued.

The unpredictability of machine failures, variability in parts quality, and the complex logistics of sourcing and replacing components all contribute to inefficiencies. A study by Deloitte found that unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Furthermore, the traditional approach often results in either overstocking parts, which ties up capital and storage space, or understocking, leading to production delays.

Generative AI: A New Approach to Parts Replacement 

Generative AI, a subset of artificial intelligence that creates new content and solutions by learning from existing data, offers a transformative approach to parts replacement. Unlike conventional predictive models, generative AI can dynamically simulate countless scenarios to optimize parts replacement strategies. Here’s how: 

1.  Advanced Pattern Recognition

Generative AI can analyze vast amounts of data from multiple sources, such as technical drawings, CAD files, product specifications, and historical part usage data. By recognizing patterns and correlations that human analysts might miss, AI can identify the required part or suggest the best alternatives based on specific parameters like size, material, and functionality.

For instance, if a part is no longer available, generative AI can analyze the functional requirements and propose an alternative that meets or exceeds the original part’s specifications. This capability significantly reduces the time spent searching for parts, ensuring that production can continue without unnecessary delays. 

2.  Intelligent Search Engines

Manufacturers can integrate generative AI into their existing databases and inventory systems, creating intelligent search engines for more intuitive and efficient queries. Instead of relying on exact matches for part numbers or descriptions, these AI-powered systems can understand natural language queries and context, making it easier for users to find the right part even if they don’t have all the technical details.

For example, a maintenance engineer could input a description like "gear for high-torque motor, 2 inches diameter," the AI would quickly identify the most suitable options, even if the exact part number is unknown. This flexibility dramatically speeds up the search process and reduces reliance on specialized knowledge.

3. Real-Time Inventory Integration

Generative AI can be integrated with real-time inventory management systems, providing instant access to the availability of parts across multiple locations. This integration allows manufacturers to find the right part and determine its availability, delivery time, and cost in real-time.

By having a comprehensive view of all parts in stock, manufacturers can make informed decisions about sourcing and logistics, further streamlining the replacement process.

4.  Customized Part Recommendations

In addition to finding existing parts, generative AI can suggest custom solutions when standard parts aren’t available or suitable. By analyzing the specific needs of a manufacturing process, AI can generate recommendations for custom parts that can be quickly fabricated using advanced manufacturing techniques like 3D printing. This capability is precious when time is of the essence or when dealing with obsolete or highly specialized components.

5. Predictive Maintenance and Real-Time Monitoring

Generative AI can analyze vast amounts of data from sensors, machine logs, and historical maintenance records to predict when a part is likely to fail. This capability allows manufacturers to shift from reactive to predictive maintenance, replacing parts before they fail. According to a McKinsey report, predictive maintenance can reduce downtime by 30-50% and lower maintenance costs by 20-30%.

6.  Optimized Parts Inventory

One of the most significant advantages of using generative AI is its ability to optimize parts inventory. By predicting which parts are likely to fail and when, the system can automatically adjust inventory levels, ensuring that critical parts are always in stock without overstocking. This optimization reduces the need for excessive inventory, freeing up capital and reducing storage costs.

A McKinsey report estimates that AI can reduce inventory levels by 20 to 30 percent by improving demand forecasting through dynamic segmentation and machine learning, and optimizing inventory through simple and cost-effective tools.

7. Automated Supplier Coordination

Generative AI can also streamline the supply chain by automatically coordinating with suppliers to ensure the timely delivery of replacement parts. By integrating with supplier databases and leveraging machine learning algorithms, the AI system can select the most reliable and cost-effective suppliers, negotiate terms, and even place orders autonomously. This automation reduces the administrative burden on manufacturers and ensures that parts arrive just in time, reducing downtime. 

8. Enhanced Quality Control

By analyzing data on parts performance and failure rates, generative AI can identify trends and anomalies that might indicate a quality issue with specific components. Manufacturers can use this insight to address potential problems before they escalate, improving overall product quality and reducing the likelihood of recalls. According to a study by PwC, implementing AI in quality assurance could boost defect detection rates by as much as 90%.

Leverage NVIDIA Inference Microservice (NIMS) and A100 GPUs 

Coupled with NVIDIA Inference Microservice (NIMS), when deployed on an A100 GPU, this solution can become more streamlined and efficient, allowing businesses to unlock the full potential of their AI models in production environments. 

The solution leveraged agentic AI with an exciting approach of managing parallels with NIMS on A100 GPU clusters to achieve better outcomes. The agents developed and operated as multiplexer while converging with semaphore-based outcomes as part of the solution leveraging NVIDIA's core architecture that facilitates multiple parallel workloads efficiently, using their NIM AI inference. 

This is the uniqueness( app algorithm + hardware) achieved in our solution. 

While deploying the solution on cloud, prescribed documentation for dockerized Installation of Nvidia drivers for Llama3 model was modified with the following blocks for seamless execution;

A. Update the CUDA drivers: https://developer.nvidia.com/cuda-downloads? target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_typ e=deb_local

--> wget Coupled with NVIDIA Inference Microservice (NIMS), when deployed on an A100 GPU, this solution can become more streamlined and efficient, allowing  businesses to unlock the full potential of their AI models in production environments.  

--> The solution leveraged agentic AI with an exciting  approach of managing  parallels with NIMS on A100 GPU clusters to achieve better outcomes. 

--> sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600

-->  wget Coupled with NVIDIA Inference Microservice (NIMS), when deployed on an A100 GPU, this solution can become more streamlined and efficient, allowing  businesses to unlock the full potential of their AI models in production environments. 

--> The solution leveraged agentic AI with an exciting  approach of managing  parallels with NIMS on A100 GPU clusters to achieve better outcomes.

--> sudo dpkg -i cuda-repo-ubuntu2004-12-4-local_12.4.1-550.54.15-1_amd64.deb 

--> sudo cp /var/cuda-repo-ubuntu2004-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/

--> sudo apt-get update

--> sudo apt-get -y install cuda-toolkit-12-4

--> sudo apt-get install -y cuda-drivers 

--> nvidia-smi 

B. Run the docker in detached mode (so that the terminal can be closed) and the server can be left to run in the background

-- docker run -d --rm -it --name mistral-7b-instruct --runtime=nvidia -e CUDA_VISIBLE_DEVICES=0 --shm-size=8G -v $(pwd)/model-store/ensemble:/model- store/ensemble -v $(pwd)/model-store/trt_llm:/model-store/trt_llm -p 9999:9999 -p 9998:9998 -p 8080:8080 nvcr.io/ohlfw0olaadg/ea-participants/nemollm-inference-ms:24.01 nemollm_inference_ms --model mistral-7b-instruct --openai_port="9999" -- nemo_port="9998" --num_gpus=1

Meanwhile, NIMS abstracts the complexities of serving AI models by offering a microservice architecture that can be easily integrated into existing infrastructure, a requirement still dominant with enterprise customers. It supports multiple frameworks, including TensorFlow, PyTorch, and ONNX, making it a versatile tool for deploying AI applications across different platforms.

NIMS leverages the power of NVIDIA GPUs, including the A100, to deliver low-latency, high- throughput inference, ensuring that AI models perform optimally in real-time applications. Key features of NIMS include:

Case Study: Intelligent Search for replacement parts of machines  

Intelligent parts assistance is offered as a service. It provides obsolete, new, and competitor part recommendations for device/spare parts for a global instrumentation company that develops superior hygienic sensors for the food and life sciences industries.

It also creates configurable BoM (Bill of Material) for end customers in real-time, which helps reduce the lead-to-quote and delivery time. This allows the internal design and engineering team to save time sending part information to suppliers, vendors, and the internal manufacturing team and deliver timely to provide replacements in a timely fashion.

Solution View:

A vector store was created from machine parts datasheets using RAG and Gen AI techniques, and the data was stored as high-dimensional insight vectors. RAG-BERT pre-trained models convert each part's description, specifications, and related metadata into numerical representations. These embeddings are stored in a vector database, enabling fast similarity searches.

Gen AI agents orchestrate across multiple LLMs to interpret user prompts and match them to the vector store. When a user queries for a replacement part, the system first interprets the prompt using natural language understanding (NLU) models. The query is then transformed into a query vector via an embedding model.

The Gen AI agent correlates this query vector with the stored vectors using LLMs. If multiple LLMs are involved, the agent may distribute tasks, such as one model handling language interpretation and another focusing on technical context.

Finally, the agent retrieves the closest matching insight vectors representing the best replacement parts. This approach ensures the user receives accurate and contextually relevant part recommendations efficiently.

Benefits:

1. Decrease in time to serve (days to seconds)

2. Real-time recommendations for end customers and internal service engineers

3. FTE save (Average 6 man-hours per USD 1200 device, which is 8-10% of device value)

The Future of Parts Replacement

As generative AI continues to evolve, its applications in discrete manufacturing will expand, offering even more sophisticated tools for parts replacement and maintenance. Manufacturers that embrace this technology will not only gain a competitive edge but also set new standards for efficiency, reliability, and innovation in the industry.

In conclusion, generative AI is more than just a buzzword—it’s a powerful tool transforming how manufacturers approach parts replacement. By leveraging AI’s predictive capabilities, manufacturers can minimize downtime, optimize inventory, and ensure the highest quality standards, all while reducing costs and boosting productivity. The future of parts replacement in discrete manufacturing is intelligent, proactive, and driven by generative AI.

Mastek Team:

Abhilasha – Product Head,  icxPro

Sunder Subramaniam – Head of Strategic Initiatives, Group CEO Office

Nvidia Team:

Vinay Raman, Akhil Raj Kumar Saraswathi - NVIDIA Enterprise Support Team

Phil Lee- GSI Client Director