Is Generative AI in manufacturing the secret your competitor won't tell you about?

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    • Aishwarya SaranInformation Alchemist
      Without data you are just another person , with an opinion.
    • Manufacturing
    • Data Science
    • Gen AI

    Editor’s Note: Generative AI is paving the path for AI in manufacturing for the future. More than 80% of enterprises will have used Generative AI APIs or deployed Generative AI-enabled applications by 2026 according to Gartner. Does this mean you need to implement Generative AI in your business as soon as possible or do you need to tread with caution?

    This blog helps you answer this question by providing you with an industry overview through actionable insights into use cases, risks, and how to start your GenAI adoption journey.

    Where does Generative AI in manufacturing stand?

    It’s not every day that you get to redefine the future of the manufacturing industry.

    Not even a year ago, AI particularly at the edge was the hot topic in manufacturing, but now GenAI has become a new topic of consideration. Despite the buzz around generative AI in manufacturing, it is important to maintain a measured perspective. Instead of a hasty adoption of GenAI driven by FOMO (fear of missing out), manufacturers should recognize that simply applying tools like ChatGPT on their will or simply rushing GenAI implementation without a clear AI strategy is not enough.

    The current trend of executives chasing GenAI without a clear strategic need appears more reactive than proactive. It's important to remember that GenAI is still AI and the roadmap, vision, strategy, and process needed for AI adoption is applicable for this too. Neglecting these principles and overlooking proven AI solutions can lead to wasted resources and a potential decline in GenAI adoption rates.

    Otherwise, adoption rates will fall over time (given the significant amount of money and resources needed this would be a major loss). For this to “not” happen, choosing the right path to success by choosing the right use cases in the right format becomes imperative.

    Top 5 generative AI use cases in manufacturing industry

    The potential of ROI through its effective implementation warrants manufacturers' enthusiasm. Regardless of their affinity for digital technology, manufacturing executives ranked AI (including GenAI) first among technologies that could positively disrupt their operations according to various surveys.

    generative ai in manufacturing survey banner
    Generative AI in manufacturing ranks the list of technology that could positively disrupt industry

    The use of GenAI has been popularized for enhancing productivity and efficiency but GenAI is not just for that. Its exceptional capacity to analyze and comprehend large quantities of data makes it a true match for an industry that generates more than 26 Gigabytes of data per day.

    With this in mind, let’s look at the top 5 manufacturing generative AI use cases that influence the manufacturing lifecycle.

    generative ai use cases in manufacturing infographic
    Generating new designs, Predictive Maintenance and scheduling, Supply chain Management, Customer Service & Support, Production & Inventory Management.

    1. Generating new designs

    As complicated as it sounds the generative designs follow a simple principle: Focusing on manufacturability, matching requirements to specs, and generating realistic 3D models and digital twins for testing.

    We get it. At present, it may seem a little far-fetched taking into consideration the whole process of starting from collecting sensor data, analyzing it, forming base results, and then generating various output images all keeping in mind the defined requirements and in a few minutes. But it's not baseless.

    The ability to evaluate and iterate countless design variations optimizes factors like material usage, structural integrity, cost efficiency, and performance. But that's not all, it also brings speed and agility to developing new products or refining existing ones. This empowers manufacturers and designers to closely track, evaluate, and improve designs before manufacturing begins. The wide acceptance of Generative design tools like Autodesk is proof of it.

    2. Predictive Maintenance and Scheduling

    Predictive maintenance is the best-practice strategy that identifies and rectifies possible failures before they happen which can save manufacturers 1.6 million hours of downtime annually and $ 734 billion through a 6% increase in productivity according to Siemens.

    But here’s the catch. To accurately predict equipment malfunctions, businesses need substantial high-quality data. However, many companies lack the necessary data engineering resources to handle complex data sets. Generative AI can address this challenge by creating new synthetic data sets for analysis, expanding the potential training data for predictive models. This approach bypasses the need for extensive pre-existing data and reduces the number of employees required for the job.

    3. Supply Chain Management

    Supply chain disruptions are not news for manufacturers. Also, while dealing with these long-term disruptions, now they are tasked with the responsibility of ethical and sustainable sourcing. This highlights the need for scalability and end-to-end visibility across the supply chain.

    Since generative AI has become synonymous with scalability deploying it has proven to be useful. Large language models, like LLMs, can adapt to multiple uses, delivering recommendations for best-suited suppliers based on relevant criteria — such as bill of materials specifications, raw material availability, delivery schedules, or sustainability metrics. Adept at extracting provisions using natural language processing from legal and contractual documents, it can deliver real-time insights into supply chain performance making generative AI valuable for the supply chain to streamline interactions.

    The next wave of supply chain excellence is here! Find out:
    • Key barriers to Generative AI implementation
    • Value Mapping for Supply Chain Operations
    • Implications Reshaping Supply Chain Dynamics
    Know More

    4. Customer Service and Support

    Customer expectations for after-sales services are getting higher. According to Salesforce, 80% of business buyers expect companies to respond and interact with them in real-time, and 82% say personalized care influences their loyalty.

    To deliver to these expectations by automating common interactions like troubleshooting and parts ordering, GenAI chatbots and virtual assistants can deliver faster service and immediate issue resolution. Earlier analytics bots were rule-driven and required extensive training data, limiting their use.

    Large language models are revolutionizing this, making it easier to extract insights from data. For instance, interpreting complex datasets with charts, graphs, or tables will become more intuitive, revealing information that was previously hidden in plain sight. Leveraging large language models (LLMs), GenAI can hold natural conversations with customers, analyze queries, and provide step-by-step guidance 24/7. It's like having an acting manufacturer's representative working to enhance customer experience, manage field inquiries, and even respond to online reviews.

    5. Production and Inventory Management

    We all are aware of cost outruns of inaccuracies in inventory management and suboptimal resource allocations led by poor demand forecasting, lack of inventory visibility, and overstocking and out-of-stock situations adding up to process inefficiency.

    However, this can be resolved using the capabilities of Gen AI models to analyze historical sales data, market trends, and other key factors. Based on the insights, it allows manufacturers to optimize them to match the dynamic requirements of stock. Further, its role in production planning helps in achieving optimal production schedules, ideal resource allocation, and workflow optimization.

    You are one step away from finding your true match!

    Ask yourself these questions to find the right Generative AI use cases that fit your needs.

    Get insights

    Now with a better understanding of generative AI use cases and potential, you are ready to take the next step.

    You are ready but where to start?

    Well, the good news is you have already taken step one by understanding the use cases but there’s more. To fully understand the answer to this, you need to ask yourself a simple question: What do you want from generative AI or AI in general?

    While your business objectives might focus on revenue growth, improved customer satisfaction, or cost reduction, AI offers a toolbox of techniques to achieve them. Generative AI (GenAI) is a powerful subset of AI with unique capabilities. The key is identifying which AI techniques or GenAI applications are most relevant to your specific needs.

    Goal How AI enables the goal Use Cases
    Topline revenue growth Change in business model supported with AI-enabled initiatives Change in business model supported with AI-enabled initiatives
    Improved customer satisfaction Conduct better behavioral analysis and more personalized recommendations Change in business model supported with AI-enabled initiatives
    Increased productivity Augmenting AI to automate mundane tasks Code and content generation, knowledge management

    Is having a better understanding of use cases enough? The answer is no.

    Risks and Considerations

    You cannot make a well-rounded decision without knowing both sides of the same coin. There’s no doubt about the fact that generative AI in manufacturing offers a plethora of benefits, but there are challenges to consider:

    1. Intellectual Property Risk

    Since Generative AI uses large data to model, it also uses the inputs received from users to train. Companies like Amazon have already sounded the alarm with their employees, warning them not to share code with ChatGPT as there is a risk of it being replicated

    2. Skill Gap

    The continuous upgrading of new technology adoption into manufacturing processes leads to a discrepancy between its implementation and the necessary expertise of the workforce.

    3. Employee Misuse

    Employees can misuse LLMs, especially contract workers who can pose off the content written by tools like theirs. Also leveraging such tools for laws and regulations might skip some ethics concerns or the latest changes in regulations.

    4. Inaccurate Results and Bias

    Since genAI adoption in the manufacturing industry is still at an early stage, Hallucinations are the most common risk that executives state. ChatGPT and Meta’s generative AI bot Galactica have seen quoting citations that have never existed as their sources. Also, if the input has inaccurate data it would result in incorrect output.

    The road ahead

    The future of manufacturing is driven by innovation and adaptability. By embracing Generative AI in manufacturing, manufacturers can unlock a world of possibilities, from faster product development to optimized production processes and enhanced customer experiences.

    As rightly pointed out by our CTO, Ankit Rana, “GenAI is not something that can even be remotely ignored – because even if you want to your competitors won’t.

    Right now, the questions that most CTOs and CEOs ask are not What is Generative AI, but rather how do we get started with it and what use cases should we start with.”

    If you're considering incorporating Generative AI into your business processes, our team of skilled professionals, who have a proven track record of delivering high-performance AI solutions across diverse domains, is ready to assist you.

    Get in touch with us today!

    About Author

    generative ai in manufacturing
    Aishwarya Saran

    Information Alchemist

    Without data you are just another person , with an opinion.

    Generally Talks About

    • Manufacturing
    • Data Science
    • Gen AI

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