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The manufacturing industry is constantly finding new ways to increase automation, gain operational visibility and accelerate product and technology development. This requires companies to always stay at the forefront of deep technological advances. One of the more recent technological advances we see arising in the manufacturing industry is the use of Generative AI, particularly Large Language Models (LLMs). While Generative AI is capable of creating new and unique data based on patterns it recognizes in existing data, LLMs takes things a step further by providing an ability to understand and organize complex information and generate human-like interaction.

Manufacturing industry generates massive volumes of complex unstructured data (sensors, images, video, telemetry, LiDAR etc.) generated by connected vehicles, factories, buildings and workers, much of which needs the ability to stream data in real-time and fused with important contextual data sources to respond to important events in a meaningful way.

Here's the deal: to harness the power of this data, your people don't want more apps, more data and more browser windows, they fundamentally want to be able to do their job better. This is where LLMs have a game-changing impact on the industry as it will fundamentally change how people interact with systems and documents, resulting in several orders of magnitude improvement in productivity, customer satisfaction and financial performance. Below, we will explore three areas where LLMs can drive real-world impact and ROI today.

More Delightful Customer Experiences

LLMs are going to be absolutely essential to any personalization initiative. At its core, the use of LLMs deliver speed and consistency, the two of the most important attributes of delivering a world-class customer experience, giving end-customers the ability to interact and resolve their needs without the need for a human in the loop. As automotive OEMs add more sensing and software capabilities in nearly every car, conversational features will underpin the design of a smarter cockpit, with comfort, route planning and entertainment adjustments being executed through natural language prompts.

This capability can also be extended into further personalization of the ownership experience. Dealers are an important intermediary in the delivery of post-sale services. Instead of navigating different websites or phone calls to understand appointment availability, a future experience could have owners asking "when's the next available appointment for routine maintenance near me?" and surface results that are immediately actionable for the owner and shape an experience that is consistent across many dealers. In the case of speaking with live agents, LLMs make it possible to make agent interactions more productive and successful by curating an AI-guided script based on the nature of the query, what open/unresolved issues exist for the customer in question and the overall health of the relationship with the brand, leading to more personalized interactions.

More Prescriptive Field Service

Let's take an example of a use case in field service such as predictive maintenance where the goal is to not only gain predictive insights into equipment health, but also orchestrate the most effective action i.e. positioning of the right people, right parts in the right place and the right time and conduct the right maintenance before it leads to unnecessary downtime.

Typically, it begins with product telemetry and sensor data being continually analyzed to predict downtime risk for equipment in the field. From there, the technician has to navigate a complex and lengthy review of information from numerous applications and dense technical manuals to understand how to troubleshoot the issue and then perform procedural steps as laid out in the documents - this is time consuming, error-prone and sub-optimal for end-customers. Moreover, it shapes a troubleshooting process that may be repeatable, but not necessarily one that adapts and learns over time.

Instead, LLMs trained specifically on the process of troubleshooting the equipment could yield much better outcomes. First, a conversational model could be used by technicians to interact with technical manuals - and be pointed to the right section based on the nature of the issue, saving valuable time and money. Second, by integrating learnings on actions from 1,000s of similar events encountered in the past by other technicians, the technician can receive more prescriptive information on the most effective action that can be taken to yield the best results. Last, but not the least, it can incorporate all the warranty status and documentation for the product to inform the best economic decision for the end-customer, making the service experience even more precise and personalized.

Instead of wrangling multiple documents and manuals, technicians can be guided by an expert co-pilot that is constantly by their side, enabling quicker diagnosis, performing the most effective maintenance action that maximizes uptime, and empowering field staff to support more customers every day.

More Productive Operations

Manufacturing operations run continuously 24x7x365 - and require continuously improving processes and navigating increasingly unpredictable supply chains to deliver quality products to customers. One key issue that impacts success is the flow of information across different departments, stations and workers on the shop floor. The answer to this often is periodical production reviews at every level - station, department and management. A repetitive task to support these reviews is for operations analysts to aggregate key metrics and information from different systems and prepare reports - both a repetitive and time consuming process. The real goal of these reviews is to gain visibility into performance gaps and make data-driven prioritization decisions to boost operational efficiency, customer service levels and financial performance, but the process to gather information is slow, manual effort intensive and lacks repeatability.

Consider the ability to simply ask "which customer orders are most at risk in the production schedule this month?". This allows for more time spent in understanding bottlenecks and devising recovery strategies to get on-time delivery performance on track, and less time wrangling data from multiple systems with complex queries. The use of LLMs enables workers on the shop floor to query complex systems such as digital twins, control towers and other teams without having to write lines of code or complex SQL queries, making these systems accessible to non-technical users - leading to a step change in responsiveness and productivity.

How to get started

These examples are just some of many possible areas where LLMs can unlock value in the industry. As always, the business unlock depends on how companies can uniquely orchestrate this technology in a way that differentiates them from competition. This is where open source approaches towards LLMs provide a more sustainable path to creating value by enabling companies to retain control of their data and intellectual property, provide flexibility to uniquely optimize models for their industry-specific context and use cases, and design an architecture to deliver such capabilities that scales with associated business outcomes.

What to learn more? Visit our site to learn about the Lakehouse for Manufacturing or learn how you can harness LLMs yourself in our webinar: Build Your Own Large Language Model Like Dolly.

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