Generative AI in manufacturing

What Is generative AI in manufacturing?

Why do manufacturing companies need Gen -AI enable applications?

As theory meets practice, manufacturing organizations are discovering how AI features can unlock enhanced efficiency and automation and facilitate strategic decision-making.

Manufacturing companies need Gen-AI-enabled applications to automate design, optimize workflows, and accelerate innovation. These tools analyze large datasets to generate new product ideas, predict equipment failures, and improve decision-making. Gen-AI reduces time-to-market, enhances productivity, and supports continuous improvement across design, engineering, and production.

While generative AI helps manufacturers dive deep into design and prototyping without the associated hard work. Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers are some of the technologies that generative AI uses in contrast to traditional AI. Generative AI can also generate new data and drive innovation in product creation.

Key applications of generative AI in manufacturing

Key applications of generative AI in manufacturing include product design, process optimization, quality control, supply chain management, and predictive maintenance. Generative AI enables manufacturers to design compliant and efficient prototypes, streamline operations, detect defects in real time, anticipate supply chain disruptions, and predict equipment failures before they occur. By using data-driven insights, generative AI enhances production resilience, reduces costs, and ensures quality and safety across workflows.

Let's take a closer look at each key application of gen AI in manufacturing. 

Product design and prototyping

The manufacturing industry is vulnerable to multiple global factors, from climate change and labor shortages, to supply chain disruptions. As a result, production can take a beating without sufficient planning, forecasting, and resilience. Generative AI identifies these constraints and predicts alternative designs, prototypes, and strategies that help achieve market-friendly results despite volatility.

GANs and VAEs, in particular, can help generative AI implement strategies that optimize materials, weight, and strength. Most importantly, it allows the design of products and prototypes that comply with changing rules and regulations across jurisdictions. Consequently, manufacturing units will be prepared to deal with a turbulent market atmosphere and innovate and deliver cost-effective products that resonate with an increasingly demanding customer base.

Process optimization

Research shows that existing manufacturing methods are only sometimes efficient, although businesses may be familiar with them. It's essential to innovate and change what we have grown accustomed to improve efficiency and reduce costs. In the recent past, process optimization required enormous effort and brainstorming. It's well-known that brainstorming does not always lead to the best outcomes, and implicit biases may color resulting decisions. Generative AI negates implicit biases and ensures that new manufacturing methods are efficient and result-oriented.

It analyzes existing workflows and suggests how they can be optimized. Next, it looks at how to configure equipment and allocate resources to streamline production and reduce wastage. Last but not least, it also suggests ways to make working more enjoyable and productive for your staff. All this is possible thanks to launching data repositories that can be analyzed to identify what can go wrong and what might work best in the given circumstances.

Quality control and defect detection

Quality control has traditionally been largely randomized and subjected to unpredictable checks. While statistically, this is the most effective way to identify defective products, it can be a double-edged sword. To begin with, entire batches can be discarded or destroyed because a few items in a lot turned out to be defective. On the other hand, it is equally easy to miss out on defective products because random checks can't always identify faulty products.

Generative AI uses a large number of image-related datasets and sensor-based information to identify patterns associated with defects. These deviations can easily be picked up by generative AI-enabled ERP tools in real time so that defective products can be identified right on the production line. As a result, you will not have to deal with rework, wastage, product returns, and general customer dissatisfaction.

Supply chain management

As previously mentioned, multiple global factors can influence supply chain performance, and consequences can be unpleasant if adequate planning is not implemented. Unfortunately, plans depend on evidence; unless evidence considers future possibilities, they can go quite wrong. Generative AI simulates various supply chain scenarios and identifies optimal routes, inventory levels, and procurement processes.

As generative AI algorithms consider multiple constraints and fluctuations, you can rest assured that there is a steady flow of stock arriving at your warehouse or distribution centers. As noted in the Hype Cycle report, a business impact of “Embedded AI in ERP” clarifies this succinctly “AI embedded in ERP will bring systems that support users in their role in starting or continuing business processes and creating business capabilities.”* Using an ERP with embedded generative AI, you can accurately identify supply chain outcomes to be well-prepared against contingencies.

Predictive maintenance

Proactive maintenance has long been a feature of AI-enabled ERP systems. Companies would identify defects and phenomena that required attention thanks to Internet-of-Things (IoT) sensors. However, any intervention depended on sensor data and real-time information relayed by the devices. Thanks to generative AI, it is now possible for ERP tools to predict failures even before they occur.

By analyzing historical data and correlating them with real-time sensor outputs, AI can pinpoint deviations from normal behavior and predict failures well in advance. Not only is this important to reduce product failure and maintenance costs, but it is also essential to avert accidents and injuries while dealing with faulty equipment. Most importantly, generative AI-enabled ERP helps manufacturers to solidify customer trust and confidence in their products.

The impact of generative AI on ERP systems

Generative AI transforms ERP systems by enabling intelligent automation, predictive analytics, and real-time decision-making. It enhances modules like supply chain, finance, production, and maintenance by generating optimized plans, forecasting disruptions, and personalizing user experiences. This results in faster workflows, reduced errors, and more adaptive, data-driven operations.

Generative AI has far-reaching implications for ERP technology in the short and long term.

ERP products have long been criticized for being clunky and having a steep learning curve. As a result, many ERP failures have occurred due to employee non-acceptance, poor implementation, and unnecessarily complex architectures. Traditional artificial intelligence and a focus on user interface have already made modern ERPs unrecognizable compared to their legacy versions.

Generative AI will continue to revolutionize ERP and ensure businesses derive the benefit they expect from these organization-wide software programs. However, witnessing a transformational benefit requires a timeline of at least two to five years for generational AI.

According to Gartner research, “organizations looking for operational advantages need to balance investments that offer short-term incremental improvements with those that offer high and even transformational benefits over the longer term.”*

This is only possible when businesses adopt timelines and vendor roadmaps while closely studying various outcomes. Organizations should consider adopting conversational UI, exploring digital modeming, embedding AI into specific use cases, and considering other aspects specific to them.

Integration of AI capabilities in modern ERP solutions

Modern ERP software products already feature AI technologies to a certain extent. From predictive analytics to machine learning and natural language processing, advanced ERP tools have AI embedded. Companies have already begun to see results, ERP insights are more meaningful now, and automation of manufacturing processes is more straightforward than years ago. With the advent of generative AI, things are expected to become much more efficient.

Specifically, conversational UI will help employees navigate the interface of complex ERP products without extensive training. Intuitiveness will be built into ERPs due to generative AI's capability to process human language. A clear outcome of integrating generative AI with ERP is sophisticated design and prototyping capabilities.

Businesses can iterate and fix issues before they occur and ensure high-quality products quickly reach the market. Before integrating generative AI with existing ERP products, it is essential to define business goals, evaluate existing ERP capabilities, choose suitable AI models, such as GANs or VAEs, and adopt a policy of continuous improvement.

How generative AI is enhancing ERP functionalities for manufacturers

Generative AI enhances ERP functionalities for manufacturers by automating design, predicting maintenance needs, optimizing supply chains, and improving decision-making. It enables real-time insights, adaptive scheduling, and defect detection. These capabilities help manufacturers reduce downtime, boost productivity, and respond faster to market changes through intelligent ERP systems.

For instance, Priority Software has steadfastly worked towards integrating generative AI into its ERP software, with a specific focus on enhancing manufacturing processes. Generative AI has revolutionized multiple ERP functionalities for manufacturers.

Product design and prototyping on ERP products is now easier than ever. This allows manufacturers to transition from design to production quickly. It also enhances the project management module by enabling it to identify bottlenecks and problems with existing production workflows. ERPs can deliver predictive maintenance and asset management using large data repositories and real-time information. They can also control quality and detect defects quickly, enhancing customer satisfaction and confidence. Most importantly, a data-driven approach eliminates problems related to order fulfillment, erroneous interpretation of demand forecasts, and difficulties with providing prescriptive recommendations to users.

Benefits of generative AI for manufacturers

Generative AI benefits manufacturers by increasing efficiency, reducing costs, enhancing innovation, and improving decision-making. Embedded in ERP systems, it identifies process risks, predicts outcomes, optimizes supply chains, and boosts productivity. It also simplifies interfaces and enables evidence-based strategies across design, production, and operations.

Regarding a business impact of Embedded AI in ERP, , Neha Ralhan writes in the Gartner Hype Cycle Report, “AI use cases can provide a high level of risk mitigation by identifying transactions that are outside of normal parameters or company policy and mitigating the risk of material errors in the enterprise's financial, risk and audit statements.”*

Let's take a deeper look at the 9 main benefits of generative AI for manufacturers.

1. Increased efficiency and productivity

In the coming years, the manufacturing industry will have to brace for a highly competitive world that is not always stable in terms of policies, finance, or political environment. With these risks in mind, ensuring manufacturing efficiency and productivity at the highest level is essential. Generative AI can identify potential loopholes and mitigate multiple risks by identifying patterns within processes.

As generative AI will enhance automation, risk-free processes can percolate to every department, including supply chain and inventory management. As the Hype Cycle Report states, “Within the supply chain, the intelligent operations center enables the automation of the myriad tasks involved in managing the entire end-to-end supply chain, resulting in improved performance and margins.”*

Essentially, generative AI-embedded ERP tools will be a book for workers and help them become more productive and engaged at work.

2. Cost reduction

ERP has long helped manufacturers to reduce their costs and improve efficiency. Cost reduction can be implemented by choosing the right quality of raw materials and suppliers, identifying efficient processes, helping employees be more productive, and changing existing workflows that are more of a liability than assets.

Generative AI helps manufacturers do all this by identifying hidden patterns and eliminating problematic processes that cause bottlenecks. It can also help choose the right vendors and suppliers to acquire raw materials and components at the best cost and quality possible. Most importantly, it uses historical data and real-time processes to suggest efficient strategies that improve working conditions and productivity. All these lead to cost reduction and better utilization of financial resources.

3. Enhanced innovation and creativity

Studies show that one of the reasons why companies lag behind their peers is due to reduced innovation and creativity. Innovative companies are likely to remain profitable in the future even when their bottom line figures aren't good. Hence, investing in tools that promote innovation and creativity is essential in a highly competitive manufacturing atmosphere.

Generative AI-embedded ERP tools identify design possibilities and innovative concepts that engineers can experiment with. It also accelerates the creation of new products that resonate with the customers' needs. Generative AI uses real-time production-related data to suggest more efficient workflows than the current ones. Moreover, creating novel products and solving problems customers previously couldn't identify will enhance manufacturers' repute.

4. Improved decision-making capabilities

Decisions affect manufacturing outcomes in more ways than one can imagine. Although traditional AI and predictive analytics have long helped manufacturers to make decisions based on rich insight, they still cannot fathom the unpredictable nature of world events and policy shifts. As supply chains and logistics greatly depend on international shipping routes, changing business policies, and political agreements, it is essential to consider these myriad factors.

A generative AI-embedded ERP identifies these potential risks and suggests the right course of action considering given circumstances. This allows manufacturers to make informed decisions backed by evidence and realistic possibilities. In short, generative AI helps manufacturers make future-proof decisions and remain responsive to changing market dynamics.

5. Challenges and considerations

Using generative AI in the context of manufacturing is not without risks. Generative AI depends on natural language processing, conversational UI, and algorithms that detect patterns within images, videos, and text. Hence, there is a chance that relying only on generative AI without using traditional embedded AI and predictive analytics runs the risk of hallucinations and incorrect conclusions.

Moreover, companies and consumers will also have to grapple with deep fakes and questionable data sets in the coming years. Thankfully, these challenges can be quickly addressed when generative AI is used for what it's worth making user interfaces friendly and conversational and identifying patterns within naturally occurring human content such as text, images, and video.

Priority ERP, for example, uses traditional AI, machine learning, and predictive analytics to analyze historical data and real-time information. Generative AI adds a layer of convenience and user-friendliness to what would otherwise be a complex architecture. Hence, with due diligence, challenges associated with generative AI can easily be overcome. Here are a few common challenges with suggestions to improve:

6. Data quality and quantity requirements

In manufacturing situations, it is vital to maintain a high level of data accuracy. Procurement, design processes, and production depend on historical data and real-time information. Deviations and incorrect information can render insight incorrect. Although generative AI is excellent at analyzing and processing human-generated content such as text, video, and images, it often struggles with quantitative data.

Hence, using generative AI alongside established AI algorithms and models is essential. This will offset any risks and potential incorrectness of insights. As generative AI models are being tested and used on the latest versions of ERP, it may struggle to process older versions of ERP data.

7. Implementation and integration issues

Most manufacturers still use legacy ERP systems, which may not be ideal even for traditional AI-enabled ERP. As cloud ERP is still not mainstream among manufacturers, many businesses may struggle to catch up. As a result, there may be issues with regard to implementation and integration.

For instance, manufacturers must transition those applications to AI-compliant versions if they use other third-party software solutions incompatible with advanced AI solutions. All this may be difficult for manufacturers who still struggle with concepts related to automation, cloud-based software solutions, and intelligent insights.

8. Ethical considerations and responsible AI use

In recent years, many discussions have concerned ethical and responsible AI usage. Generative AI has received much attention from ethical committees regarding reliability, privacy, and security. It is crucial to work with an ERP vendor who understands these ethical considerations and ensures the safety and security of all users regardless of the timeline. In addition, many have raised concerns about security and intellectual property.

In the Hype Cycle report's Generative AI in ERP section, Greg Leiter states, “Security and intellectual property protection remains a concern. While large hyperscale vendors and startups are racing to make generative AI services enterprise-ready, in the short to midterm, there will still be a lack of regulation and appropriate adaptable oversight.”* These challenges can be overcome, too, if you choose a vendor who prioritizes ethical matters, such as we do at Priority Software.

9. Workforce adaptation and training needs

There is a general sense of discomfort among workers regarding artificial intelligence. Although traditional AI did not pose such concerns among laymen, generative AI has raised concerns about people losing jobs, machines taking over human intervention, and other related concerns.

Thankfully, generative AI cannot replace existing workflows; it only enhances and makes things easier for staff and employees. Thanks to conversational UI, they can communicate more effectively with each other and machines. In addition, training employees to use ERP without requiring extensive technical knowledge is an advantage of generative AI.

Future trends and predictions

Most companies have sleepwalked into an era of AI, let alone generative AI. In the coming years, there will be a growing focus on integrating AI into existing ERP systems and developing new ERP software programs incorporating generative AI at an advanced level.

The Hype Cycle report states, “The market disruption of generative AI has drawn additional attention and increased pressure on applying AI in the ERP solution landscape, with ERP vendors responding to this demand by working on pilot use cases that drive GenAI along with everyday AI and ML capabilities already in use.”*

Some emerging technologies that may be used alongside generative AI include enterprise asset management, embedding AI into cloud solutions, and making generative AI more error-free.

In the next two to five years, ERP vendors will focus on developing AI algorithms that are compatible with their existing products. However, this may only be possible for more extensive and established vendors. It's important to remember that AI models are expensive to build, and not every company can do so successfully.

Both traditional and generative AI will positively affect manufacturing. To remain relevant, competitive, and productive, manufacturing companies will have no choice but to move to AI-enabled ERP products. Generative AI will help companies design better and more appropriate products that resonate with their target audiences. It may also be used to quickly introduce products into markets.

Preparing your manufacturing business for generative AI

It may seem daunting to launch your business processes into the AI macrocosm. Firstly, AI appears to be evolving rapidly, so making long-term decisions seems complicated. Second, you may wonder if you have the right tools and technology to shift to an AI-enabled ERP tool. Finally, you may have concerns about generative AI, especially ethics and security.

Here are the 5 steps to assess readiness for AI implementation:

1. Accept that every manufacturing company needs AI assistance
2. Evaluate your existing hardware and software to understand the gaps
3. Speak to employees and understand the bottlenecks that prevent production from moving smoothly.
4. Identify an ERP vendor who prioritizes the ethical usage of traditional and generative AI.
5. Learn how AI can make your production process more efficient and productive.

In short, it comes down to choosing the right ERP solution with AI capabilities. If you still use a legacy ERP tool, you might have to implement data cleansing and migration before you attempt to transition.

You may also invest in compatible hardware to ensure your employees can use the latest conversational UI.

Priority Software's ERP solutions are specifically geared toward helping and supporting manufacturers in their AI adoption journey. Although it may seem difficult initially, the results will speak for themselves. Priority ERP uses both traditional and generative AI to ensure that challenges associated with using generative AI are addressed. In addition, timely updates, training, and after-sales support will keep your AI-enabled ERP future-proof.

In the manufacturing sphere, AI is here to stay

AI is an established technology that will continue to be used and implemented by software vendors of all kinds. It has transformative potential in manufacturing and will help you create and deliver novel products that resonate with your customers.

Manufacturers must adopt AI-enabled software programs to remain competitive and productive. Although some may be hesitant to use generative AI, the right ERP vendor can allay their doubts. Contact us today to understand how traditional and generative AI can enhance your production process.

*Gartner, Hype Cycle for ERP, 2024, June 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.