Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Manufacturers are often hobbled by the volume, diversity, and siloed nature of data from across the value chain; this prevents them from taking faster decisions and improving efficiency.
- Creating large language models (LLMs), which are fine-tuned using industry-specific data, will help manufacturers with more contextual insights. It is a journey to creating ‘enterprise-wise’ LLMs from ‘world-wise’ ones.
- Such tailored LLMs can help manufacturers increase accuracy leading to higher quality output, take decisions faster, and manage costs better.
On this page
On this pageinpage
The pitfalls of unstructured data
The sheer volume and diversity of data from supply chains, production lines, customer demand, and market trends are both a boon and a challenge for a manufacturer.
These datasets, often siloed across value chains and buried in intricate manuals, technical documents, and reference materials can be overwhelming and difficult to decipher. Fragmented data also presents a significant challenge to navigate for new hires or people with language barriers.
Crucial information may be further obscured by undocumented procedures; those little tweaks or adjustments learned through experience, but never formally documented. Capturing, codifying, and disseminating such knowledge prove difficult and can significantly impact production efficiency and quality control. That problem is magnified as more experienced employees superannuate and such tacit knowledge disappears without being passed on to the next generation of shop floor workers.
These challenges create a knowledge bottleneck, hindering the ability to effectively train new personnel, maintain consistent quality control, and adapt to the ever-evolving demands of modern manufacturing.
Hope on the horizon
Generative AI (GenAI) and large language models (LLM) on which it is founded, can go a long way in addressing the challenges.
For manufacturers, GenAI can be trained on a wealth of information from production data and sensor readings to maintenance logs and even scanned manuals (complete with AI-powered optical character recognition to extract text). This allows the AI engine to not only understand documented procedures but also infer undocumented knowledge from historical trends and sensor data.
While general-purpose LLMs are trained on diverse datasets making them versatile across a broad range of topics, this breadth comes at the expense of depth in specialized areas. Hence, such models often do not grasp the specific nuances of industries or sub-domains. Their inherent ‘world-wise’ nature needs refinement to understand the intricacies of a specific manufacturing domain. Training the model on industry-specific data enhances its ability to generate relevant content and provide actionable insights.
From world-wise to enterprise-wise
Manufacturers need tailored LLMs which will provide them with contextual information.
Here is how we can create an industry-specific, function-focused, enterprise-wise LLM for a manufacturer (see Figure 1):
Fine tuning the data
Building a manufacturing lexicon: This involves creating a robust manufacturing lexicon by feeding the LLM with massive data sets of domain specific text and code. The lexicon will use manufacturing process manuals, technical documents such as white papers, industry standards, and engineering specifications relevant to the specific manufacturing domain. The lexicon will also include industry glossaries – terminologies specific to the sector to ensure the LLM understands the precise meaning of technical terms – and textual data extracted from interviews with experienced workers, capturing undocumented best practices and troubleshooting techniques.
Curating for manufacturers (manufacturing-wise): With the manufacturing lexicon in place, the LLM’s massive pre-trained model can be fine-tuned. This involves retraining the model on the curated dataset, focusing on the specific relationships and patterns within the manufacturing language. Through this process, the LLM will learn manufacturing jargons, identify process relationships and extract implicit knowledge.
Tuning for function-level expertise (function-wise): As the solution delves deeper into function-specific knowledge, the LLM is exposed to even more specialized data sets such as function-specific process manuals, service manuals, and troubleshooting guides. It will also be trained to understand function-specific workflows, solutions, and predictive maintenance. Through this instruction-tuning process, the LLM is capable of understanding function-specific workflows and generating function-specific solutions.
Creating an enterprise-wise LLM: This process involves incorporating enterprise-specific data to create a truly customized LLM. This can include company policies and procedures, historical production data, and personalized recommendations. By fine-tuning with this data, the LLM becomes enterprise-wise, capable of understanding specific company contexts, generating specific insights, and providing personal recommendations.
Figure 1: Customizing LLMs with contextual knowledge
Implementing responsible AI guardrails
Throughout this process, it is important to implement robust guardrails to ensure security, privacy, and ethical AI practices. These include data synchronization and encryption to protect sensitive information within the training datasets, a human-in-the-loop system for ensuring transparency, and mitigating bias in the LLM output. These steps can build a powerful and specialized LLM that enhances manufacturing expertise and improves operational efficiency across the organization.
Improving LLM outputs with retrieval augmented fine tuning (RAFT)
Now that there is a fine-tuned LLM made ‘wise’ at multiple levels, the fine-tuned model is complimented with RAFT to bring together the benefits of both retrieval augmented generation (RAG) and fine-tuning. It enables the model to learn patterns specific to the target domain while also enhancing its ability to understand and utilize external context effectively. This integration significantly enhances decision-making processes, streamlines operations, and provides a competitive edge by making critical information readily available and actionable.
The fruits of labor
Customized LLMs are the solution to break the invisible walls of tacit knowledge and complex documentation in the manufacturing industry.
Such an AI-powered knowledge engine can help with:
- Ensuring accuracy: The LLM can analyze vast amounts of data, minimizing human error and ensuring consistent adherence to best practices, leading to higher quality output.
- Driving faster decision making: Real-time analysis of sensor data, historical trends, and large corpus of documents and manuals allows for quicker identification of potential issues and faster decision-making.
- Improving availability and accessibility of tacit and expert knowledge: The captured knowledge of experienced workers is no longer lost with retirement. LLMs scale this ability, making it accessible to new hires and empowering them to make informed decisions.
- Bettering cost efficiency: Reduced errors, improved quality control, and proactive maintenance strategies lead to significant cost savings. Additionally, faster training times for new hires can further optimize operational costs.
- Enabling proactive risk management: By analyzing trends and predicting potential equipment failures, the LLMs enable proactive maintenance, preventing costly downtime and ensuring smooth production flow.
- Facilitating continuous learning and improvement: The model's ability to learn and adapt from new data fosters a continuous improvement loop. As the LLMs are exposed to more data and experiences, it becomes even more adept at understanding the nuances of the manufacturing process, leading to further efficiency gains and innovation.
The road ahead
The manufacturing value chain typically includes product development, supply chain management, manufacturing operations, sales and marketing, distribution, and after-sales service.
Advanced technologies are used across these processes to improve the overall productivity and efficiency of each of the steps. GenAI is a powerful tool to break the siloed data in this chain, make it meaningful, and show actionable insights to be derived from it. It can unlock the true potential of manufacturing operations on the floor and in the field. By empowering workers with on-demand access to tacit knowledge, enabling faster decision-making, and facilitating continuous learning, this technology paves the way for a future of intelligent manufacturing.
